**Actionable Capability for Social and Economic Systems (ACSES)**

**Georgia Institute of Technology**

Peter Brecke

Patricia Voorheis

Thom McLean

**IntePoint, LLC**

Mark Armstrong

**Oak Ridge Center for Advanced Studies**

Paul Gilman

Heather O'Maonaigh

**Oak Ridge National Laboratory**

Steven Fernandez

Auroop Ganguly

Erin Middleton

Amy Rose

Phil Coleman

Olufemi Omitaomu

Cathy Jiao

**University of North Carolina at Charlotte**

Mirsad Hadzikadic

Chris Eichelberger

Theodore Carmichael

Moutaz Khouja

Joseph Rutherford

Amar Saric

Min Sun

Joseph Whitmeyer

Contract POC:

Steven Fernandez

PO Box 2008, MS 6017

Oak Ridge, TN 37831

Voice: (865) 576-3565

fernandezsj@ornl.gov

Date of Report

May 2008

TABLE OF CONTENTS

**Executive Summary 1**

**Chapter 1: Evaluation of Data Availability and Worldwide Survey**

**of Data Quality 6**

> Research Objective 6

Definition of Data-quality Levels At the National Level 6

Definition of Independent Variables 7

Assigning Data-Quality Levels at a Dimension Level 7

Maps of Data Quality by Dimension 9

The Afghan Example 13

Lessons Learned and Insights 17

**Chapter 2: Agent-Based Modeling 19**

Introduction 19

Social Theories 19

The Leadership Theories 21

The Social Mobilization Theories 22

The Relationship between Social Theories 23

Utility Function 23

Geographical Presentation 25

Agents 25

Simulation Steps 26

Update Step 27

Action Step 27

Data Step 30

Learning Step 30

Change in Loyalty 31

Change in Ideology 34

Utility Step 36

Behavior Step 38

Check Step 39

NetLogo Simulation Outputs 39

Territorial Control 39

Hardship 39

Mindshare 39

The Simulation Scenario 40

Calibration 40

Inputs 40

Static Inputs 40

Regarding Assassination 41

Variable Inputs 43

Concerning Time and Scheduling 43

Outputs 43

Three Incremental Agent Designs 44

Approach 1 -- Fixed Attribute Values 44

Generalizing across Parameter Settings 45

Analysis 47

Approach 2 -- Learning 49

Learning Theory 49

Implementation of Learning 50

Results from Implementing Learning 51

Analysis 53

Approach 3 -- Calibration via a Genetic Algorithm 54

Technical Design of the GA 54

Results from the GA 55

Analysis of the GA 57

Recommendations 58

Future Work for GA 58

Insights 59

**Chapter 3: Real-Time Updating 60**

Introduction 60

Computer Simulation Systems 60

Analytic Simulation 61

Interactive Simulation 61

Simulation Execution Models 62

Sources of External Data 63

Autocoding of Free Text and Real-Time Event Generation 67

Anticipated Benefit from Runtime Input 68

Problem Description and Conceptual Model 69

The Synchronization Problem 69

The Mapping Problem 71

Channels 71

Event Handlers 71

Transforms 72

Implementation and Examples 73

Autocoding and External Data Sources 75

Mechanics of Event Insertion 76

Specifying Affected Agents 76

Future Work 76

Insights 76

**Chapter 4: Accounting for Surprise 78**

Introduction 78

Surprise: An Overview 78

Agent Clustering 79

Threshold Response 85

Evaluating Command Options for Ghazni in 2006 88

Insights 92

**Chapter 5: Required Computational Complexity 93**

Introduction 93

Addressing the Computational Complexity Issue 95

Computational Costs 103

Simulation Setup of the ORMAC System 104

Summary of the Adaptation of the Social Theories for

> the Comparison Experiment 104

Design of the ORMAC Agent 107

Geographical Attributes 107

Mandatory Agent Attributes 108

Flow Diagram of the ORMAC Simulation 109

ORMAC Simulation Output Description-Feasibility Experiment 110

Comparison of NetLogo and ORMAC Results in Response

to the Commander's Questions 116

Insights and Conclusions 123

**References 125**

**Appendices**

Appendix 1 Research Plan 131

Appendix 2 Data Sources for Independent Variables 136

Appendix 3 LandScan Methodology 144

Appendix 4 Narrative Description of Key Data Sources 151

Appendix 5 Dimension Data Quality by Country 166

Appendix 6 Real-World Correlation 173

Appendix 7 Real-Time Update Code Description 177

Appendix 8 Generating Narrative 188

Appendix 9 Visual Analytics 191

Appendix 10 Visual Plots of NetLogo and ORMAC Comparisons 197

**Tables**

Table 2-1 Theories implemented in the simulation model 20

Table 2-2 The NetLogo regions 25

Table 2-3 Agent ability to identify other agents 27

Table 2-4 Simulation procedures within the conceptual flow diagram 42

Table 2-5 Model event time steps 43

Table 2-6 Simulation results by run 46

Table 2-7 Statistically significant parameter settings 46

Table 2-8 Recommended parameter settings 47

Table 2-9 The additional evaluation series 51

Table 2-10 Best performing chromosomes 55

Table 3-1 Analytic and interactive execution models 62

Table 4-1 Experimental settings, Set 1 88

Table 4-2 Experimental settings, Set 2 88

Table 4-3 Time-step translation 89

Table 5-1 Complexity comparison between ACSES and Germann

simulation systems 102

Table 5-2 Mindshare from Gallup World Poll 107

Table 5-3 Correlation coefficients between minority opinion

and WITS databases 115

Table 5-4 Points of event insertion 118

Table 5-5 Correlation coefficients between NetLogo and ORMAC outputs 119

Table 5-6 MSE between NetLogo and ORMAC outputs 120

Table 5-7 Correlation coefficients between runs for NetLogo outputs 120

Table 5-8 Correlation coefficients between runs for ORMAC outputs 120

Table 5-9 Linear correlation between ORMAC and NetLogo

runs at district levels 122

Table A4-1 Example district profile 159

Table A4-2 Gallup World Poll coverage 161

Table A4-3 Performance indices of the World Path 164

**Figures**

Figure 1-1 Quality of data needed to build a synthetic population 9

Figure 1-2 Flow of data source employment in construction

of the Afghan synthetic population 13

Figure 1-3 Geospatial distribution of agents representing the Afghan
population 14

Figure 1-4 Ethno-linguistic mappings used for ethnic interpolation 15

Figure 1-5 Perceived war zones 16

Figure 1-6 Areas of opium cultivation 17

Figure 2-1 Conceptual flow chart of the simulation 26

Figure 2-2 Results of action choices 30

Figure 2-3 The results of varying parameters across multiple
replications 51

Figure 2-4 Course of simulations 56

Figure 2-5 The difference between mindshare and control in the
simulation 57

Figure 3-1 Logical structure of a TENA system 64

Figure 3-2 Data-driven agents 65

Figure 3-3 A process for updating forecasts (Plale 2005) 66

Figure 3-4 Comparing models for updating forecasts 66

Figure 3-5 Two-step transformation process 70

Figure 3-6 Notional geocoding construct 73

Figure 4-1 Simulation results at various time steps 80

Figure 4-2 Simulation results for Runs 1--5 82

Figure 4-3 Results of surge experiment 86

Figure 4-4 Results as a function of run history 87

Figure 4-5 Resulting Ghazni mindshare for various parameter settings 89

Figure 5-1 The conceptual design of the agent 97

Figure 5-2 Germann model notional architecture 99

Figure 5-3 Example goal, standard, and preference tree 100

Figure 5-4 Flow diagram of social mobilization theories plus

cognitive dissonance theory 109

Figure 5-5 Flow diagram of social mobilization theories plus homophily
theory 110

Figure 5-6 Percentage pro-Taliban minority opinion by district 111

Figure 5-7 Distribution of pro-Taliban minority opinion 111

Figure 5-8 Distribution of Taliban terrorist incidents 112

Figure 5-9 Distribution of Taliban terror incidents with Ghazni

province highlighted 113

Figure 5-10 Distribution of pro-Taliban minority opinion

under representative theory 113

Figure 5-11 Minority pro-Taliban opinion correlated with 2006 terrorist
incidents 114

Figure A4-1 The LandScan quality map 151

Figure A4-2 2002 Afghan population density 153

Figure A4-3 Local NGO sectoral activities 160

Figure A6-1 Raw mindshare and WITS incidents 173

Figure A6-2 Interpolating mindshare 174

Figure A6-3 Distributing a single discreet WITS incident over 5 days 174

Figure A6-4 Distributing a series of 7 discreet WITS incidents over 14
days 175

Figure A6-5 Visualization of distributed WITS data 175

Figure A6-6 Correlations with assumed delay 176

Figure A9-1 Original NetLogo home page 191

Figure A9-2 Example workspace in new probe-based interface 193

Figure A9-3 Localized control capabilities 194

Figure A9-4 Two probes over a Taliban-controlled area 195

Figure A10-1 Time-series plots of NetLogo and ORMAC simulations 197

***Executive Summary***

The foundation of the Actionable Capability for Social and Economic
Systems (ACSES) project is a useful regional-scale social-simulation
system. This report is organized into five chapters that describe
insights that were gained concerning the five key feasibility questions
pertaining to such a system:

1.  Should such a simulation system exist, would the current state of
    data sets or collectable data sets be adequate to support such a
    system?

2.  By comparing different agent-based simulation systems, is it
    feasible to compare simulation systems and select one appropriate
    for a given application with agents behaving according to modern
    social theory rather than ad hoc rule sets?

3.  Provided that a selected simulation system for a region of interest
    could be constructed, can the simulation system be updated with new
    and changing conditions so that the universe of potential outcomes
    are constrained by events on the ground as they evolve?

4.  As these results are constrained by evolving events on the ground,
    is it feasible to still generate surprise and emerging behavior to
    suggest outcomes from novel courses of action?

5.  As these systems may for the first time require large numbers
    (hundreds of millions) of agents operating with complexities
    demanded of modern social theories, can results still be generated
    within actionable decision cycles?

Chapter 1 identifies and characterizes global data sources and the
granularity needed to synthesize and model populations with sufficient
detail to support social-simulation environments. Data describing
worldwide demographic, economic, and social attributes of the world
population have previously been deemed inadequate for these purposes.
However, geospatial sciences have advanced a number of techniques for
merging disparate databases to achieve new levels of granularity for
social simulators.

A survey was conducted to determine whether the data required to build
synthetic populations exist and if so, to assess data quality as it
pertains to building simulation-useful synthetic populations. The
suitability of indicator data sets was illustrated by building an
example synthetic population for a typical case study. Afghanistan was
selected for this case study because gathering data for Afghanistan
supported other tasks of the ACSES project and because its data
availability situation was thought to be especially challenging. Chapter
1 describes how the following disparate types of information can be
integrated to construct useful synthetic populations.

-   LandScan Global is used by a majority of key U.S. and foreign
    agencies as their base data system for worldwide geospatial
    distribution of populations. Population dynamics are available on a
    1 sq km grid and can be augmented through current imagery programs.
    This capability is a recent contribution to the National
    Geospatial-Intelligence Agency's (NGA) geospatial data mission.

-   Data on military and peacekeeper forces' loyalties, readiness, and
    deployment are widely available through a combination of UN and
    classified force projections and reside mostly within classified
    sources.

-   Economic data, although usually collected at the national level, can
    be disaggregated by applying data fusion techniques (similar to
    those applied to the LandScan Global databases) to data on
    industrial plant location, agricultural land use, and workforce
    distribution.

-   Data sets on social attitudes, beliefs, and social cleavages have
    been developed for a majority of the globe (on a population basis)
    through anthropological studies, worldwide polling data, and
    classified sources. These sets can be disaggregated to the level
    needed to construct synthetic populations.

-   Informational systems networks can be built to imitate civilian and
    propaganda channels in addition to traditional command and control
    networks, including communication infrastructures and transportation
    routing. These data sets typically require access to classified data
    sources.

-   Infrastructure systems and networks have adequate modeling of
    engineered structures and can handle infrastructure interruption and
    repair from targeted and widespread disruption. Information on
    infrastructure systems is available in both open and classified
    sources.

Although direct, highly disaggregated data (at the lowest level of a
population) would be difficult to collect directly, attitudes and
beliefs can be credibly ascertained using two forms of disaggregation.
The most common form is geographical disaggregation. In the case of
modeling a population, the location of different population subgroups
can be inferred from land-use patterns.

The second form of data disaggregation is category disaggregation. Data
is collected and individual cases sorted with respect to different
categories of a theoretically relevant variable. For example, a
population can be disaggregated by ethnicity. Ethnically differentiated
data such as that gleaned from opinion polls can be combined with
geographically disaggregated data, as has been done by LandScan, to
establish a value for local populations' attitudes about the government
or insurgent groups.

Within Chapter 2, we demonstrated that it is possible to build
agent-based systems informed by modern social theory and compare the
results in such a way that the relative skill of the different models
can be evaluated. For this comparison we employed three theories within
a case study about control and mindshare in modern Afghanistan.
Critically important is the feasibility of modeling the relevant
behavior of populations, beginning at the level of the individual, or
agent. In Chapter 2, we explored three options for describing agent
behavior in our simulation. Each approach built upon the previous one,
as more adaptability was added to agent behavior. Specifically, the
first approach employed agents with static attributes, fixed at the
beginning of the run and unchanged throughout the simulation. In the
second approach, agents were initially assigned attributes, but these
could change over time as the agents learned and adapted to their
situations. The third approach began with assignment of random attribute
values, and the model was then calibrated with a genetic algorithm to
favor those values that produced simulation results most predictive of
the real Afghanistan. The shift over time in mindshare of Afghans, as
predicted by simulation runs with each of these agent models, was
compared with the real-world occurrence of terrorist events in
Afghanistan. Some of the key lessons learned and insights were as
follows:

1.  It is possible -- even within a simulation that relies heavily on
    stochastic processes -- to evaluate multiple replications of
    discrete combinations of parameter values to identify optimal
    simulation settings and trends.

2.    The simulation can exploit the underlying social models. This is a
    direct consequence of the equations embodying the social models,
    rather than an emergent behavior of the society of agents.

3.  Adaptation can increase the verisimilitude of the running model in
    some cases.

4.  The comparisons we performed suggest that the third (genetic
    algorithm) approach to agent behavior performed the best:  it
    explored the most granular search space, and produced results that
    were entirely consistent with our earlier findings.

Chapter 3 evaluates the feasibility of injecting real-time events, and
several approaches are suggested. To handle the volume of documents that
could explain relevant events, documents and cables and knowledge
discovery tools are crucial to allow feasible real time updating.
Currently employed systems can be deployed to retrieve real-time
information, de-duplicate, and screen data, and an
information-extraction software component can be inserted to extract
events from the data produced by Piranha. These events can be
incorporated into the ACSES simulation as real-time updates.

Chapter 4 discusses the feasibility of constraining results by evolving
events while still generating surprise and emerging behavior with a
complex adaptive system model of roughly 10,000 agents, each of which
has its own rules and attributes that take into account social,
economic, and military factors at the local level. The experiments
identified two distinct types of surprising, or emergent, behavior and
used those results as a basis for evaluating various courses of action
(COA).

Many recent advances described within Chapter 5 permit us to consider
the computational requirements of the next generation of human, social,
behavioral, and cultural (HSBC) modeling and simulation systems that
support the ability to anticipate adverse human behavioral developments,
and that allow us to evaluate alternative courses of action to mitigate
their impact on U.S. national security interests. These recent advances
include

-   new approaches to multi-agent systems that allow us to envision
    goal-oriented agents and event responses that span the HSBC domain,
    as well as modern frameworks and architectures that support models
    using agents that obey rule sets informed by modern social theory;

-   advances in high-performance computing (HPC) resources that can
    assist computationally intensive cases when large numbers of agents
    are required to adequately describe regional stability and when
    aggregation obscures causal factors;

-   emergence of architectures that can handle 10^8^ agents,
    disaggregated to the level of individuals behaving according to
    modern social theories within community scales important to regional
    stability, while still providing useful predictions in
    computationally tractable times.

Key to the next generation of social modeling and simulation is the
ability to (a) recognize and characterize a surprising event that
threatens or impacts a region\'s stability, (b) model historical events
that led to the event with sufficient transparency to allow insight into
causal factors, and (c) use this insight to evaluate diplomatic,
information, military, and economic (DIME) options for increasing
regional stability by influencing emerging events.

In Chapter 5 we illustrate the feasibility of operating such a complex
agent-based simulation system that can generate courses of action in
computational times much less than 24 hours. The approach to this task
included

-   estimating the maximum hypothetical complexity of the agents that
    provides for each dimension of the HSBC, incorporating elements of
    modern social theory;

-   characterizing the maximum complexity of the environment that allows
    for real-time updating of the environment with unfolding events;

-   developing literature examples of analogous systems of comparable
    complexity;

-   comparing employed computational resources to those currently
    available with similar run times for case studies.

Disaggregated models that also track the motion of individual social
atoms are computationally intensive. The complexity of moving resources
and people is compounded by increasing granularity in data that allows
inclusion of finer behavioral dynamics, thereby increasing the
computational intensity. Given that dynamic planning and operational
support require large-scale simulations within narrow time windows,
reducing execution times is the most critical challenge and can be met
only with significant computing resources beyond desktop environments.
High-performance computing is, therefore, a necessary enabler for
simulations that generate multiple courses of action for a large social
region.

Testing the 31-million-agent model of Afghanistan's population
demonstrated the ability to computationally control and analytically
manipulate a system with the large number of agents (i.e., 10^8^)
necessary to model regions at the individual level. Simulations in
computationally tractable times can be performed with computational
costs that range from less than a dollar for simulations with simple
interactions to a few hundred dollars for the largest and most complex
simulations.

***Chapter 1: Evaluation of Data Availability and Worldwide Survey of
Data Quality***

**Research Objective**

The research plan for the ACSES project is provided in Appendix 1
--Research Plan. The objective for Task 1 was to identify and
characterize global data sources and the granularity needed to
synthesize and model populations at the level required to support the
simulation environment. The metrics for this task are

-   Demonstrate, or document the feasibility of, direct use of observed
    or simulated data to seed the behavioral dynamics governing
    heterogeneous agents representing the entire Human, Social,
    Behavioral, Cultural (HSBC) domain at each scale of aggregation.

-   Demonstrate or document the adequacy of available public domain or
    classified data sources that relate directly to individual human
    behavior as well as overall social or economic phenomena, with the
    objective of guiding the formulation of CAS-based models capable of
    generating actionable predictive insights and modeling surprising
    emergent behavior.

-   Demonstrate or document the adequacy of disparate, multiscale, and
    possibly dynamic data sources that can validate the causal patterns
    and the predictive insights extracted from CAS-based models for
    social or economic systems.

-   Demonstrate the feasibility of utilizing the observed and simulated
    data in a scalable fashion within a realistic modeling and
    simulation environment. Specifically, demonstrate the feasibility of
    enabling the online data-model feedback loop in an environment with
    more than a 100 million agents, with complex behavior based on
    realistic social or economic theory, and with disparate data feeds
    coming in at real-time or near real-time.

In summary, for the seedling effort, Task 1 seeks convincing evidence
that the data and related tools exist to construct a synthetic
population for the simulation tasks. As a first step, we assessed data
quality and then illustrated the concepts by constructing a synthetic
population. The criteria used to assess the data quality follow.

**Definition of Data-Quality Levels at the National Level**

The criteria for assigning a data quality level are based on the ability
to use the assembled data sources to build synthetic populations. Scores
of 0, 1, 2, and 3 are assigned to variables describing geographical
territories. This scale reflects the approximate difficulty in building
synthetic populations that support a wide range of potential social
theories.

Areas assigned the value 0 have no data available for building synthetic
populations. Examples of such areas include Antarctica and the
Australian Indian Territories.

Areas assigned a value of 1 have below-average data quality. In general,
these countries are characterized by infrequent or nonexistent census
activities and include the central African countries; countries whose
data submissions to the United Nations are under suspicion of gross
manipulation, such as Cuba and some Former Soviet republics; and a few
Central and South American countries. Conflict zones typically fall
within this category because of the transient nature of their
populations and the difficulty in collecting data in conflict zones.
Exceptions include areas where U.S. interest in the political and social
stability is high (e.g., North Korea, Iran, Iraq and Myanmar).

Areas assigned a value of 2 have average data quality. These countries
routinely submit data to the United Nations, and the data is generally
useable with only ordinary quality-assurance checking. Census surveys
and data reporting may be less frequent than in North America or the
European Union. This category includes most of the developing countries
and relatively small participants in world trade flows.

Areas assigned a value of 3 have better-than-average data quality.
Census, military structure, economic, social, and infrastructure data
are collected annually with known accuracy and precision. This category
comprises North America and the European Union, members of the OECD, and
major trading partners.

**Definition of Independent Variables**

The data required to build a synthetic population can be organized into
six broad dimensions---political, military, economic, social,
information, and infrastructure---that are important for understanding
how populations react and respond to changes in their environment.
Within each dimension, the ACSES social theorists surveyed various
models and cataloged the independent variables that would satisfy a
large fraction of the modeling and surveying needs. These variables are
listed in Appendix 2-- Data Sources for Independent Variables, where
representative sources are also presented to demonstrate source
availability.

**Assigning Data-Quality Levels at a Dimension Level**

Within a dimension of the independent variables, the following criteria
were used to assign the data-quality values.

*Political.* Almost all countries meet the criteria for data quality
level 1, which is identifiable national legislative, executive, and
judicial bodies, roles, and representation. Data at this level are of
equal or better quality than data in the CIA Fact Book. Data quality
level 2 is reached if data exist to interpolate or derive political
attitudes and beliefs. Regular polling, census, or anthropological
studies that define a time series of loyalties may provide this data.
Likewise, information on subpopulations that is derived from data on
social cleavages (as determined from social variables) may enable
sub-national detailing of political ideology, freedom of action, and
resources. Data quality level 3 requires that data be available to
describe or interpolate the regulation and actions of governmental and
nongovernmental organizations as well as their motivation, intent, and
objectives. Most close U.S. allies meet the criteria for data quality
level 3.

*Military.* The military dimension of a synthetic population can be
separated into two components: (1) the make-up, reliability, and
capability of the country's armed forces and (2) attitudes and beliefs
about a country's security. Values of 0 or 1 are assigned to areas of
active conflict for which little data exists on the make-up, structure,
or force level of combatant forces, the acceptance of these forces by
the population, or the popular attitudes and beliefs regarding these
forces in their roles as peacekeepers, humanitarian support, or a
positive influence on general well-being. A data quality level 2 is
assigned to countries participating in UN peacekeeping activities or to
countries where other data allows a social cleavage analysis of the
armed forces. A data quality level 3 is assigned to countries that have
entered into mutual security arrangements with the United States and to
countries that have been topics of a classified National Intelligence
Estimate to address these factors.

*Economic.* The data quality levels for the economic dimension are
highly dependent upon the occupational distribution of the population
and the granular distribution of economic activity within the country.
Therefore, the assigned quality level for each country was no greater
than the LandScan Global data quality level for that country. The
methodology for generating the LandScan Global data is described in
Appendix 3 -- LandScan Methodology. Members of the World Bank and
countries contributing data to the International Monetary Fund
development and statistics database typically have data that, when
combined with other sources, is sufficient to address economic welfare
and a leader's ability to allocate resources. Exceptions are nations
that have been the subject of classified National Intelligence Estimates
that address these factors. In those cases, a data quality level 3 has
been assigned.

*Social.* Data in the social areas can be considered a fusion of data
from other domains. However, we have called out this dimension
separately because many of the interesting social theories are highly
dependent on the variables in this domain. The social dimension depends
not only on this fused data and open sources of collected data (Appendix
2), but also is heavily dependent upon attitudes, beliefs, and cultural
values that can be developed only by Human Terrain methodologies. As a
proxy for these attitudes and belief mapping, we used Gallup World Poll
data and the Gallup methodology to make an open assessment of the
information available to populate the attitudes and beliefs of a
synthetic population. A data quality level of 1 was assigned if the
Gallup World Poll was not available within that country and supplemental
data sources were not available for human terrain mapping; a level of 2
was assigned if the country had only Gallup World Poll coverage; and a
level of 3 was assigned if the polling coverage was supplemented with
continuing coverage from additional sources. Appendix 4 -- Narrative
Description of Key Data Sources describes some of these supplemental
sources, the polling methodology, and some examples of the allocation of
attitudes and beliefs.

*Infrastructure*. The data quality levels for infrastructure were based
primarily on engineering sufficiency of the data to allow the performing
community to build models of the key infrastructure sectors listed in
Appendix 2. For most sectors, the necessary data exist within the U.S.
Department of Defense (DoD) mission planning community. This all-source
data is currently being supplemented by NGA's open-source infrastructure
resources. For example, NGA's Palanterra, a Web-based interface designed
to describe, assess, and depict physical features and geographically
referenced activities, integrates disparate stores of spatial data to
create a spatially and analytically enabled operational picture. Access
to the database can be gained at

https://**palanterra**.nga.mil/PalSec/jsp/JTXRequestAccess.jsps

The data quality level reflects a qualitative assessment of the data
available through Palanterra.

*Information.* The criteria for data quality in the information
dimension were based on a qualitative assessment of the ability to model
the telecommunications infrastructure sector. For most of the sectors,
the necessary data exists within the DoD mission planning community. The
open-source baseline data are available in the World Bank's Little Data
Book on Information and Communication Technology 2007, and this is
supplemented by resources like NGA's Palanterra.

**Maps of Data Quality by Dimension**

Country-by--country data quality levels as assessed using the criteria
above are shown in the following maps (Figure 1-1) for each dimension.
In Appendix 5, the values for each country's dimensions are presented in
tabular form.

**Figure 1-1**. Quality of data needed to build a synthetic population,
evaluated on a country-by-country basis for the following dimensions:
(a) political, (b) military, (c) economic, (d) social, (e)
infrastructure, and (f) information.

\(a\)

> ![](media/image1.png){width="5.44375in" height="4.215277777777778in"}

\(b\)

> ![](media/image2.png){width="5.377777777777778in"
> height="4.1722222222222225in"}

\(c\)

> ![](media/image3.png){width="5.620138888888889in"
> height="4.350694444444445in"}

\(d\)

> ![](media/image4.png){width="5.499305555555556in"
> height="4.223611111111111in"}

\(e\)

![](media/image5.png){width="5.366666666666666in"
height="4.147222222222222in"}

\(f\)

![](media/image6.png){width="5.366666666666666in"
height="4.147222222222222in"}

**The Afghan Example**

As an example, we constructed a synthetic population representative of
Afghanistan's 2006 population, with the ultimate objective of testing
the ability of various leadership theories to answer strategic
questions. One of these questions asks how to reduce the overall level
of terror incidents. In this case, the leadership theories require input
on loyalty, coercion, ideology, economic welfare, and security from
violence for each agent of the population. To geospatially assign
attributes for the agent's support for the leadership, the leadership's
resources, the agent's ideology, and the agent's degree of satisfaction
with the economic and security situations, databases and data sources
were employed according to Figure 1-2.

**Figure 1-2**. The flow of data source employment in construction of
the Afghan synthetic population

![](media/image7.wmf){width="5.998611111111111in"
height="2.911111111111111in"}

.

The first step in building the synthetic population was geospatially
locating the 31 million agents representing the Afghan population within
grids approximately one kilometer on each side. The LandScan Global
techniques (Appendix 3 -- LandScan Methodology) for disaggregating a
population produced the geographical population distribution illustrated
in Figure 1-3.

**Figure 1-3**. Geospatial distribution of agents representing the
Afghan population is represented on one square kilometer square cells.

![](media/image8.jpeg){width="5.346527777777778in"
height="4.174305555555556in"}

Assigning attributes to this disaggregated population was achieved by
fusing appropriate indicator databases. A diverse body of literature
describes the major Afghan political and social cleavages that can be
used to deduce ideology and perceptions of security against violence. We
began by consulting district profiles (example in Appendix 4 - Narrative
Description of Key Data Sources) collected through human-terrain-type
efforts by the Afghan Information Management Service (AIMS) from which
we can determine the ethnicity of each element in 206 of the 329 Afghan
districts. For the remaining 123 districts, the ethnic breakdown was
estimated by interpolating data on ethno-linguistic maps, such as in
Figure 1-4, that delineate the distribution of ethnic groups. Ethnic
data from adjacent districts was used for the interpolation when it was
available. Otherwise, data from nearby areas sufficed. Although many
granular maps exist, the National Geographic maps were used to
demonstrate the value of common maps of limited granularity.

**Figure 1-4**. Ethno-linguistic mappings used for ethnic interpolation
of ethnicities in missing districts. Source: National Geographic.

![](media/image9.jpeg){width="5.383333333333334in"
height="4.558333333333334in"}

Once the ethnicity for each of the 31 million agents was assigned, then
the ideology and attitudes toward the economic and security situation
were assigned to each social atom. Although very specific polling data
can be gathered in areas of high U.S. national interest, we used
existing Gallup World Poll data for this study to illustrate the
methodology. The methodology, poll summary, and potential Gallup
coverage are described in Appendix 4 -- Narrative Description of Key
Data Sources.

The attitudes within the Gallup study group seem to have significant
cleavages along ethnic (Pashtun, Tajik, and Other), location (north,
south), and combat proximity (war zone vs. nonwar zone) lines. A mapping
for war zone location is reproduced in Figure 1-5.

**Figure 1-5**. Perceived war zones. Perceptions of violence are
dependent upon the agents' locations relative to the war zone.

![](media/image10.jpeg){width="5.347222222222222in"
height="3.9583333333333335in"}

The indicator database for how economic welfare is perceived by the
individual and is tied to behaviors in support of leaders in the Afghan
case is highly dependent upon the social atom's proximity to opium
production (Figure 1-6). The Afghan example is simpler than most
countries where corruption indices might create usable indicator
databases and where economic activity under leadership control may be
more complex.

**Figure 1-6**. Areas of opium cultivation based on 2004 data from the
UN Office on Drugs and Crime. The district leaders' ability to control
economic resources correlates with the presence of opium cultivation.

![](media/image11.jpeg){width="5.4in" height="3.999212598425197in"}

The elements above, taken together, were the building blocks of our
synthetic population. Tasks 2 through 5 used this population for
simulations that could then be compared to real-world activities.

**Lessons Learned and Insights**

In the course of this study, a large number of sources were assembled to
acquire information in support of the flexible simulation model. From
this study, we have learned that recent government efforts coordinated
through the National Geospatial-Intelligence Agency have improved the
granularity and coverage of the data required by many social theory
simulations. Because motivations and intent of either individuals or
small groups are difficult to collect in real-time, databases of
populations' spatial distributions and behaviors require demographic
experts to apply data fusion, knowledge discovery, and demographic
modeling techniques to build synthetic populations.

Fortunately, these techniques exist, and active federal research
programs are further developing the techniques. Validation studies on
denied areas of the world are continuing to evolve, but a variety of
modeling techniques is providing new access to social behaviors in these
portions of the world.

In the effort to demonstrate, or document the feasibility of, direct use
of observed or simulated data to seed the behavioral dynamics governing
heterogeneous agents representing the entire HSBC domain at each scale
of aggregation, we have learned it is feasible. Although assigning
nonobservable beliefs and attitudes to individual social atoms remains
beyond the state-of-the-art, modern demographic techniques such as those
embodied within NGA efforts can build synthetic populations with a low
number of clones.

In this task, we also attempted to demonstrate or document the adequacy
of available public domain or classified data sources that relate
directly to individual human behavior as well as overall social or
economic phenomena, with the objective of guiding the formulation of
CAS-based models capable of generating actionable predictive insights as
well as modeling surprising emergent behavior. We learned that a
successful effort that includes both political and military HSBC domains
requires classified data sources, but synthetic populations can still be
constructed.

In addition, this study attempted to demonstrate or document the
adequacy of disparate, multiscale, and dynamic data sources that can
validate the causal patterns and the predictive insights extracted from
CAS-based models for social or economic systems. The classified data
sources concerning those areas suffering the greatest regional
instability are essential to the construction of synthetic populations.
Unclassified sources are adequate for demonstrating certain principles
and methodologies and future technical issues. However, the new
simulation systems will create demands for survey and data collection in
a fashion similar to the Army's Human Terrain collection efforts and the
Strategic Command's Human Network Attack capabilities.

As part of Task 1 we sought to ascertain the range of variables for
which data can be effectively gathered. A key insight from this
initiative is that it is possible to identify and assemble data sources
pertaining to many higher-level social concepts such as freedom,
corruption, poverty, and empowerment. In most instances these data are
calculated and disseminated only at the national level, but data fusion
and disaggregation techniques such as those used in this task can be
applied to these variables as well.

***Chapter 2: Agent-Based Modeling***

# Introduction

The goal of this project is to develop a simulation capability to
monitor, assess, forecast, and respond to adverse human behavioral
developments around the world in near real-time and to use the
simulations to compare the performance of agent-based models grounded in
modern social theory. We review the general agent-based approach in a
simulation of Afghanistan and then segue into a review of the three
incremental approaches to modeling agents. The chapter begins with a
description of the NetLogo simulation, including a description of the
country, agents, their rules (which are derived from the utility
function), outcomes, and learning.

# Social Theories

This section describes the social theories implemented in the simulation
along with the rationale for their use. The rationale involves the
domain of the theory (i.e., the social phenomenon it concerns) and
scientific justification for the theory.

The social theories implemented concern only the citizen agents because
a primary interest, if not *the* primary interest, of the ultimate users
of such a simulation model would be the allegiance of the population.
That is, the users, whether social scientists investigating social
theories or commanders in the field examining real-world options, would
want to know if ordinary citizens actively supported the insurgents,
actively supported the government, or neither.

Each theory pertains to one of three domains:

> 1\. The effect of leaders on the behavior of citizen agents
> (*leadership* theories)
>
> 2\. The effect of the behaviors of similar others (neighbors and
> community) on the behavior of citizen agents (*social mobilization*
> theories)
>
> 3\. The factors influencing how a citizen agent's situation and
> behaviors may lead to change in the agent's support for the leadership
> and in the agent's ideology (*psychological change* or *learning*
> theories)

Table 2-1 summarizes the theories implemented in the simulation, as well
as their domains, outputs, locations in the simulation, and parameter
settings that instantiate the theories. There are a total of nine
theories: three leadership theories, two social mobilization theories,
and four theories of psychological change, each of the last four
implementable for both change in support for a leader and change in
ideology. This means that 96
(3$$`<!-- -->`{=html}2$$`<!-- -->`{=html}4$$`<!-- -->`{=html}4)
combinations of theories are possible.

All of the theories are implemented via a model of the agent. The
leadership and social mobilization theories are instantiated with
parameter settings in the agent model, and the change theories determine
inputs to the agent model. Any model of how an individual agent makes
behavioral choices can be realized as maximization of a utility
function. The utility function incorporates social theories that model
human decision processes.

We chose to model at the level of the agent for several reasons. First,
it is possible. Any social process, any social system dynamics, must
occur through the behavior of individuals. Models of agents, therefore,
together with rules concerning interactions of those individuals, should
be able to reproduce system-level patterns. Second, it gives
flexibility. Modeling at the individual level allows for exploration of
the effects of particular events and individuals. Third, it facilitates
development of the simulation model. For example, the simulation model
currently allows the user to control the orders and resources of the
leadership by region.

In the future, it would be desirable to allow leadership to be
endogenous to the simulation. Citizen agents from the population would
be allowed to become leaders, at which point they would have a
disproportionately large effect on events. Processes such as this are
realistic and potentially consequential, and they would be
straightforward to model through an agent-based simulation.

**Table 2-1.** Theories implemented in the simulation model

+-------------+-----------+----------+----------+--------------------+
| **Theory**  | *         | **Output | **       | **Parameter        |
|             | *Domain** | Level**  | Location | Settings**         |
|             |           |          | in       |                    |
|             |           |          | Model**  |                    |
+-------------+-----------+----------+----------+--------------------+
| legitimacy  | l         | in       | utility  | high loyalty       |
|             | eadership | dividual | function |                    |
|             |           | behavior |          |                    |
+-------------+-----------+----------+----------+--------------------+
| coercion    | l         | in       | utility  | high coercion      |
|             | eadership | dividual | function |                    |
|             |           | behavior |          |                    |
+-------------+-----------+----------+----------+--------------------+
| rep         | l         | in       | utility  | high economics,    |
| resentative | eadership | dividual | function | violence, ideology |
|             |           | behavior |          |                    |
+-------------+-----------+----------+----------+--------------------+
| social      | social    | in       | utility  | high influence     |
| influence   | mob       | dividual | function |                    |
|             | ilization | behavior |          |                    |
+-------------+-----------+----------+----------+--------------------+
| resistance  | social    | in       | utility  | high repression    |
| to          | mob       | dividual | function |                    |
| repression  | ilization | behavior |          |                    |
+-------------+-----------+----------+----------+--------------------+
| so          | psyc      | input to | c        | soc \> 0           |
| cialization | hological | utility  | omponent |                    |
|             | change -- | function | of       |                    |
|             | to        |          | utility  |                    |
|             | ideology  |          | fu       |                    |
|             | &/or      |          | nction-- |                    |
|             | support   |          | ideology |                    |
|             | for       |          | &/or     |                    |
|             | leader    |          | loyalty  |                    |
+-------------+-----------+----------+----------+--------------------+
| homophily   | psyc      | input to | c        | delta (rate) \> 0, |
|             | hological | utility  | omponent |                    |
|             | change -- | function | of       | hom \> 0           |
|             | to        |          | utility  |                    |
|             | ideology  |          | fu       |                    |
|             | &/or      |          | nction-- |                    |
|             | support   |          | ideology |                    |
|             | for       |          | &/or     |                    |
|             | leader    |          | loyalty  |                    |
+-------------+-----------+----------+----------+--------------------+
| re          | psyc      | input to | c        | delta (rate) \> 0, |
| sults-based | hological | utility  | omponent | RB \> 0            |
|             | change -- | function | of       |                    |
|             | to        |          | utility  |                    |
|             | ideology  |          | fu       |                    |
|             | &/or      |          | nction-- |                    |
|             | support   |          | ideology |                    |
|             | for       |          | &/or     |                    |
|             | leader    |          | loyalty  |                    |
+-------------+-----------+----------+----------+--------------------+
| cognitive   | psyc      | input to | c        | delta (rate) \> 0, |
|             | hological | utility  | omponent | CD \> 0            |
|             | change -- | function | of       |                    |
|             | to        |          | utility  |                    |
|             | ideology  |          | fu       |                    |
|             | &/or      |          | nction-- |                    |
|             | support   |          | ideology |                    |
|             | for       |          | &/or     |                    |
|             | leader    |          | loyalty  |                    |
+-------------+-----------+----------+----------+--------------------+

# *The Leadership Theories*

The leadership theories concern the effect that the leadership of a
group has on the allegiance---pro-insurgent, pro-government or
neutral---of the members of the group. We implemented these theories in
the simulation model because considerable social theory says that
leadership has important effects on people's behavior and because
empirical reports concerning conflict in Afghanistan and Iraq, as well
as in many areas of social conflict throughout the world, also stress
the important effects of leadership. In particular, the effects of
leaders on the population almost certainly would be important elements
of many questions a user would want to ask of the simulation model. It
is hard to imagine any scenario of interest in Afghanistan, for example,
that would not involve the effects of leaders and would not consider
them consequential. The three leadership theories implemented are all
common views in social science of the effects leaders have. The theories
may be considered alternatives and compared, or they may be treated as
complementary and combined.

The *legitimacy* theory posits that a citizen agent wants to follow the
orders of the leadership simply because it is the legitimate leadership.
This idea is common in the social science literature. It corresponds to
the concept of authority either granted willingly by an agent, or
delegated or enforced (Coleman 1990). It fits the idea of legitimacy
granted to group members who have certain characteristics or demonstrate
certain abilities (Berger et al. 1998). It also can encompass Weber's
(1968 \[1922\]) concept of charisma, according to which some individuals
have qualities that induce people to follow them.

The *coercion* theory posits that a citizen agent wants to follow the
orders of the leaders because he or she believes they are likely to
punish those who do not obey or to reward those who do. This theory can
be traced back at least to Machiavelli, who advised in *The Prince*
(Machiavelli 1985 \[1513\]) that rulers could be most effective by
making their subjects fear them, and also has contemporary advocates
(e.g., Kiser and Linton 2002; Levi 1988). It fits the behaviorist
perspective in psychology (e.g., Sidman 2000) and deterrence theory in
criminology (Becker 1968; Stigler 1970).

The *representative* theory posits that a citizen agent follows the
leadership only if the leadership advocates what the citizen agent
otherwise wants. Thus, the leadership's orders have only a weak effect
on the behaviors of the population. This idea goes back at least to Marx
(1990 \[1867\]), who saw the government as merely implementing the
desires of the ruling class, but it also has been argued for recently,
for example, in the context of Latin American rebellions and popular
nationalism (Whitmeyer 2002; Wickham-Crowley 1991).

All three of these theories are likely to be useful to an operator using
the simulation model. The validity and effects of the legitimacy theory
are relevant, for example, to the question of how important it is to
work through traditional and legitimate leaders of the population in
combating insurgency. The validity and effects of the coercion theory
are relevant to the question of the likely effects of terrorist
activities by the insurgency and how to deal with local strongmen. The
representative theory is a strong alternative if the other two theories
are not acceptable.

***The Social Mobilization Theories***

The simulation model implements two social mobilization theories that
concern the effects of social influence on participation in collective
action (Table 2-1). These two theories are clearly relevant to the
allegiance--- pro-government, pro-insurgency, or neutral behavior---of
the population in a country with significant internal conflict. We
implemented these theories in the simulation model because considerable
empirical evidence shows that people's participation in collective
action is affected by the behavior of those around them and those close
to them. This is true around the world, for example in the United States
(Roscigno and Danaher 2004), in the former East Germany (Opp and Gern
1993), and in recent China (Calhoun 1994). Many theoretical models of
collective action exist, and most if not all of them incorporate the
effects of social influence (Oliver 1993; Ostrom 2007).

Inclusion of the social mobilization theories gives the simulation model
more social realism directly relevant to the outcome the simulation
generates, allegiance. People are not independent, autonomous actors,
but rather affect each other. This may mitigate or exacerbate the
effects of their situation and, as research has shown, is likely to be
responsible for nonlinear and emergent effects in social phenomena
generally (Coleman 1990) and in collective action in particular (Ostrom
2007). The *social influence* theory posits that a person is more likely
to join collective action the more his or her immediate friends do
(McPhail 1991, 1994). This concept originated in the study of riots. The
*repression* theory is complementary to the *social influence* theory.
It posits two effects. First, people are less likely to join a
collective action the greater the local presence of forces to repress
it. Second, in the presence of some repression, people are more likely
to join collective action the larger the proportion of people in the
observable population who are engaged in this action.

These effects are shown, for example, by studies of the overthrow of the
East German regime (Opp and Roehl 1990; Opp and Gern 1993). They are
also present in the critical mass idea that often collective action can
accelerate once a critical mass of people has joined (Marwell and Oliver
1993). This theory is likely to be used in tandem with one of the
leadership theories as well as with the social influence theory. Again,
the weights instantiating these theories will have to be reduced
proportionally as *w~R~* is raised. The effect of these adjustments in
the model is that (1) the citizen agent will tend to have a lower
utility for behaviors that are opposed by the forces controlling the
agent's locality, but (2) this effect will be ameliorated to the extent
that the agent observes other citizen agents engaging in the behaviors.

Both of these theories are likely to be useful to an operator using the
simulation model, especially in conjunction with one of the leadership
theories. Neither social influence nor repression is likely to initiate
pro-government or pro-insurgent activity. For that, some other cause is
needed, such as an order from the leadership. The repression theory
adds, however, the ability of local force to suppress unwanted activity,
and both theories bring in people's influence on each other, which
introduces the possibility that activities can spread across a
population. These features will increase an operator's ability to use
the model to assess the efficacy of measures and countermeasures.

***The Relationship between Social Theories***

The leadership theories and social mobilization theories are at the
level of the human actor; they concern the choice of behavior. The
change or "learning" theories are theories of psychological change and
are at a more micro level; they are internal to an individual. There are
four change theories for ideology and the same four for support for the
leadership. In each area, the four theories are alternatives. They can
be combined, but necessarily the more important one is, the less
important the others will be. The two areas, ideology and support for
the leadership, are completely independent. Thus, one theory could be
implemented in one area and a different theory in the other area. The
change theories are also not alternatives to the leadership or social
mobilization theories because they are at a lower level. The variables
the change theories affect feed into the utility function that
implements the actor theories.

The social mobilization theories are functionally alternatives to each
other and to the leadership theories, because they all concern what
values or motives affect behavior. Specifically, they are all
implemented as weights for values in the utility function, so if the
weight implementing the social influence theory is greater, the sum of
the weights applied to a leadership theory and the repression theory
must be lower.

Conceptually, however, the social influence theory, the repression
theory, and any given leadership theory are not alternatives. That is,
it is not the case that the more one theory fits an empirical situation,
the less the others do, which is the case between the three leadership
theories. Rather, the more important that one theory (say, social
influence) is in an empirical situation, the less important the other
theories (the leadership theory and the repression theory) will be. The
other theories may fit and help explain the situation but may just be
less important.

Finally, the effects of the change theories and the actor theories are
not independent, of course, in the sense that they are not additive. For
example, the change theories concerning support for the leader matter if
the legitimacy theory is implemented, but they do not matter much if the
representative theory is implemented.

# Utility Function

One of the key challenges was finding a way to allow multiple social
theories to participate in the same simulation so that could be compared
*in situ*, and without writing a great deal of new code. The linchpin
that ties together the various social models we incorporated is the
utility function.

Many forms of utility functions have been devised. We chose the
Cobb-Douglas utility function, which specifies utility as the product of
values (i.e., preferences, motivations) raised to a fractional power
such that the exponents sum to 1 \[Eqn (1)\]. An agent chooses to
perform one behavior, from a set of considered behaviors that has the
highest expected utility. This utility function has been used for
instantiating a variety of social theories (Coleman 1990) and has
several desirable features. It is simple. It is modular, thus easily
expandable to include additional values or motivations if they are
important for a theory.

Moreover, even if the number of values in the utility function rises to
a large number, any value can be turned off by setting its parameter to
0, so that if desired, users can operate with what is effectively a much
smaller utility function. Finally, the model takes into account the
important experimental finding that losses tend to affect people more
than gains (Tversky and Kahneman 1981). Mathematically, this means that
the second partial derivative of the utility, *U*, with respect to any
given value is negative. Eqn (1) gives the utility function that is
currently implemented for citizen agents in the simulation model:

$U = (1 - L)^{w_{L}}(1 - C)^{w_{C}}(1 - I)^{w_{I}}(1 - E)^{w_{E}}(1 - V)^{w_{V}}(1 - F)^{w_{F}}(1 - R)^{w_{R}}$.
(1)

*L* (loyalty to leader), *C* (coercion), *I* (ideology), *E* (economic
welfare), *V* (security against violence), *F* (influence of close
associates), and *R* (repression and social influence for defying
repression) are the deviations from an ideal state (or "motives")
corresponding to the seven different values currently implemented. The
*w'*s are the weights of different values in the utility function. The
weights are required to be nonnegative, to be less than or equal to 1,
and to sum to 1 (*w~L~* + *w~C~* + *w~I~* + *w~E~* + *w~V~* + *w~F~* +
*w~R~* = 1). Their particular values are parameters set by the user and
instantiate different social theories and combinations of theories, as
detailed in the descriptions of the theories in the following sections.

The user implements the legitimacy theory by setting the parameter
*w~L~* to a relatively high value, i.e., giving high importance to the
loyalty component of the utility function. This means that the citizen
agent will tend to have a lower utility for behavior the more the
behavior runs contrary to the leadership's orders.

The user implements the coercion theory by setting the parameter *w~C~*
to a relatively high value, i.e., giving high importance to the coercion
component of the utility function. This means that the citizen agent
will tend to have a lower utility for behavior the more it runs contrary
to the leadership's orders to the extent that the leadership has a high
level of resources.

The user implements the representative theory by setting the parameters
*w~I~*, *w~E~*, and *w~V~* to relatively high values, i.e., giving high
importance to the ideology, economic welfare, and security against
violence components of the utility function. This means that the orders
of the leadership, even if the leadership has ample resources, are
likely to have little effect on the behavior of the citizen agent
compared with the effects of the citizen agent's situation and own
predilections.

The user implements the social influence theory by setting the parameter
*w~F~* to a relatively high value, i.e., giving high importance to the
influence-of-close-associates component of the utility function. A
second parameter sets the radius of social influence affecting a single
citizen agent. This theory is likely to be used in tandem with one of
the leadership theories, but the weights instantiating the leadership
theory will have to be reduced proportionally as *w~F~* is raised. The
effect of these adjustments in the model is that the citizen agent will
tend to have a lower utility for behaviors the more they are contrary to
the behaviors of those close to the agent.

The user implements the repression theory by setting the parameter
*w~R~* to a relatively high value, i.e., giving high importance to the
repression component of the utility function. A second parameter sets
the radius of the area a citizen agent observes to see how prevalent a
pro-government or pro-insurgent behavior is.

We will show the feasibility of developing an evaluation,
characterization, and validation framework that, when fully developed,
will be able to provide guidelines to a decision maker regarding the
best choice of theory or combination of theories. The recommendation
would be based on an initial analysis of prevailing conditions in a new
country, answers provided by end users and subject matter experts to
targeted questions, as well as end user requirements.

# Geographical Presentation 

# The country is divided into six regions, and each region is divided into patches (Table 2-2). The regions are simple and somewhat arbitrary divisions of the country. The purpose of delineating six regions was to enable multiple leaders for the Pashtun tribe such that each Pashtun leader had a geographic-based influence on Pashtun agents. Therefore, the regions apply only to the Pashtun tribe. Other tribes have only one leader each, and those leaders have influence on their agents across the entire country. 

**Table 2-2**. The NetLogo regions

  ------------ ----------------------------------------- -----------------
  Region       Name                                      Number of Patches

  1            Northeast                                 155

  2            Mideast                                   195

  3            Northwest                                 93

  4            Midwest                                   92

  5            Central                                   199

  6            South                                     131
  ------------ ----------------------------------------- -----------------

# Agents

The system has five types of agents:

-   Afghan government soldiers (denoted by *S*)

-   Coalition forces soldiers (denoted by *C*)

-   Taliban (denoted by *T*)

-   Leaders (denoted by *L*)

-   Citizen agents (denoted by *I*). Citizens who are supportive of the
    Taliban become Taliban helpers (denoted by *TH*), whereas citizens
    who are supportive of soldiers/coalition forces become soldier
    helpers (denoted by *SH*). The rest are neutral and are denoted by
    *N.*

## Simulation Steps

The following is a description of the simulation that tracks the process
outlined in Figure 2-1, beginning at the Update step.

**Figure 2-1**. Conceptual flow chart of the simulation

![](media/image12.wmf){width="5.736805555555556in"
height="6.249305555555556in"}

## Update Step

In this step, all external events are inputted into the simulation.
These events include important political, military, and economic events.
Examples would include arrival or departure of some Afghanistan
government forces (AGF), coalition forces (CF), or Taliban, the death of
a leader, news that may inflame or pacify the public, etc. The events in
this step may lead to changes in the leaders' orders or their resources,
and the changes are then inputted into the system.

## Action Step

This step simulates the actions of AGF, CF, and Taliban on each patch.
These actions may be to fight, to make an organized move, to stay on the
patch but avoid fighting, or to flee. The decision of each group is
influenced by its own strength, its perception of the strength of the
opposing forces, and by the degree of support the group has from the
local population. Furthermore, each group is assigned a certain
effectiveness that depends on the quality of the group's training,
command and control, weapons, etc. A detailed description of this step
follows.

**Table 2-3**. Agent ability to identify other agents

  -------------- ----------------------------------------------------- ----------------------------------------------------- ----------------------------------------------------- ----------------------------------------------------- ----------------------------------------------------- --
  True Identity  Perceived Identity                                                                                                                                                                                                                                                            

                 Coalition Forces                                      Soldier Helper                                        Neutral                                               Taliban Helper                                        Taliban                                               

  Coalition      ![](media/image13.wmf)                                ![](media/image14.wmf){width="0.4583333333333333in"   ![](media/image15.wmf){width="0.4027777777777778in"   ![](media/image16.wmf){width="0.4583333333333333in"   ![](media/image17.wmf){width="0.3840277777777778in"   
  Forces                                                               height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       

  Soldier Helper ![](media/image18.wmf){width="0.4583333333333333in"   ![](media/image19.wmf){width="0.5277777777777778in"   ![](media/image20.wmf){width="0.4673611111111111in"   ![](media/image21.wmf){width="0.5277777777777778in"   ![](media/image22.wmf){width="0.4583333333333333in"   
                 height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       

  Neutral        ![](media/image23.wmf){width="0.4027777777777778in"   ![](media/image24.wmf){width="0.4673611111111111in"   ![](media/image25.wmf){width="0.4166666666666667in"   ![](media/image26.wmf){width="0.4673611111111111in"   ![](media/image27.wmf){width="0.4027777777777778in"   
                 height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       

  Taliban Helper ![](media/image28.wmf){width="0.4583333333333333in"   ![](media/image29.wmf){width="0.5277777777777778in"   ![](media/image30.wmf){width="0.4673611111111111in"   ![](media/image31.wmf){width="0.4979166666666667in"   ![](media/image32.wmf){width="0.44375in"              
                 height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       

  Taliban        ![](media/image33.wmf){width="0.3840277777777778in"   ![](media/image34.wmf){width="0.4583333333333333in"   ![](media/image35.wmf){width="0.4027777777777778in"   ![](media/image36.wmf){width="0.44375in"              ![](media/image37.wmf){width="0.375in"                
                 height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       height="0.25833333333333336in"}                       
  -------------- ----------------------------------------------------- ----------------------------------------------------- ----------------------------------------------------- ----------------------------------------------------- ----------------------------------------------------- --

Each group of agents is given the ability to identify other agents
correctly or incorrectly as shown in Table 2-3, where
![](media/image38.wmf){width="0.34305555555555556in"
height="0.25833333333333336in"} is the percentage of individuals from
group ![](media/image39.wmf){width="9.305555555555556e-2in"
height="0.17708333333333334in"}perceived to belong to group
![](media/image40.wmf){width="0.13472222222222222in"
height="0.20833333333333334in"} by individuals in group
![](media/image41.wmf){width="0.15486111111111112in"
height="0.17708333333333334in"}, e.g.,
![](media/image35.wmf){width="0.4027777777777778in"
height="0.25833333333333336in"} is the percentage of Taliban perceived
to be neutral citizens by soldiers. For example, if there are 100
coalition forces on a patch (true identity), then those 100 agents can
be perceived as coalition forces, soldier helpers, neutrals, etc. The
five percentages in each row need not add up to exactly 100%; if the sum
is more than 100%, then the perception is that the total is more than
reality; less than 100% would mean some agents are not being perceived.
We assume that each group, including soldiers, identifies its own
members correctly 100% of the time.

The percentages ![](media/image38.wmf){width="0.34305555555555556in"
height="0.25833333333333336in"} are set using a slider. Note that it is
assumed Afghan government forces will *always* be correctly perceived by
Afghan government forces, and these settings therefore do not have
sliders.

Each group is assigned an effectiveness that is computed on the patch
level. For example, the effectiveness of soldiers is computed at the
patch level according to

> ![](media/image42.wmf){width="4.125in" height="0.4673611111111111in"}
> (2)

where ![](media/image43.wmf){width="0.15486111111111112in"
height="0.25in"} is the weight associated with agent group
![](media/image44.wmf){width="9.305555555555556e-2in"
height="0.17708333333333334in"}.
![](media/image45.wmf){width="0.15486111111111112in" height="0.25in"}
depends on many attributes of group
![](media/image46.wmf){width="9.305555555555556e-2in"
height="0.17708333333333334in"}, including training, equipment,
effectiveness of command and control, and ability to bring additional
support to bear, etc. Future iterations may take into account "force
multiplier" effects of lopsided numbers; however, for now this assumes a
linear relationship of force effect to force size.

![](media/image47.wmf){width="0.16666666666666666in" height="0.25in"} is
the number of agents of group
![](media/image48.wmf){width="9.305555555555556e-2in"
height="0.17708333333333334in"} present on this patch. The weights are
set for each group using a slider called "force weights." Force weights
indicate the relative strength of each group.

The soldiers have a perceived effectiveness that other groups evaluate
according to

> Soldier-Force =![](media/image49.wmf){width="2.1666666666666665in"
> height="0.25in"} (3)
>
> Taliban-Force =![](media/image50.wmf){width="1.5694444444444444in"
> height="0.25in"} (4)
>
> Perceived-Soldier-Force
> =![](media/image51.wmf){width="2.2777777777777777in" height="0.25in"}
> (5)

In plain terms, the action that either the soldiers or the Taliban
choose depends on the perceived effectiveness of the opposing force
relative to the known effectiveness of their own force. The coalition
forces and the Afghanistan government forces always act together, i.e.,
they choose the same action. Therefore, the perceptions by these two
groups of the number of Taliban are averaged together to determine
perceived Taliban count (*PTC* below). *PTC* is then used to calculate
the perceived Taliban force, i.e., the number of perceived Taliban times
the force weight for the Taliban, plus the number of perceived Taliban
helpers times the force weight for Taliban helpers.

Once the perceived Taliban force is calculated, the soldiers calculate
their own soldier force, which is the number of CF and AGF times their
force weights, plus the perceived number of soldier helpers (PSHC) times
the SH force weight.

> ![](media/image52.png){width="4.613888888888889in"
> height="0.19791666666666666in"} (6)
>
> ![](media/image53.png){width="4.624305555555556in"
> height="0.19791666666666666in"} (7)
>
> ![](media/image54.png){width="1.46875in" height="0.34375in"} (8)
>
> ![](media/image55.png){width="5.009722222222222in"
> height="0.19791666666666666in"}(9)
>
> ![](media/image56.png){width="4.957638888888889in"
> height="0.19791666666666666in"} (10)
>
> Perceived-Taliban-Force =![](media/image57.wmf){width="2.19375in"
> height="0.25in"} (11)
>
> Total-Agents-Count-Sum
> =![](media/image58.wmf){width="1.2083333333333333in"
> height="0.20625in"} (12)
>
> Perceived-Effectiveness-Soldier
> =![](media/image59.wmf){width="1.5805555555555555in"
> height="0.20625in"} (13)

Once the soldiers calculate the soldier force and the Taliban force,
then they compare these two numbers to get a perceived soldier
effectiveness ("Perceived-Effectiveness-Soldier") that will range from
-1 to +1.

The perceived soldiers' effectiveness is computed by subtracting the
perceived Taliban force in the numerator instead of adding as was done
to compute the effectiveness of soldiers. Because this is a *perceived*
effectiveness, this measure is allowed to fall below zero to indicate
that a group may perceive itself to be at a severe disadvantage. This
differs from the calculation for real effectiveness, ES, which will
range from 0 to 1.

In determining the perceived effectiveness of the Taliban, perceptions
of the coalition forces and the Afghan government forces on a patch for
Taliban are averaged; for Taliban helpers, neutrals, and soldier
helpers, the perceptions of the coalition forces are used.

The soldiers choose a course of action based on the
"Perceived-Effectiveness-Soldier." They choose one of three actions:
flee, move/stay, or fight. The thresholds for these actions are set by
the sliders labeled "action choice." Essentially, if things look bad for
the soldiers, they'll choose to flee; if things look good, they will
choose to fight; somewhere in the middle, and they will choose to
move/stay. The soldiers (coalition forces and the Afghan government
forces) make this choice together, even though their force multipliers
and success rates may be different.

Once an action is chosen, the success rate of that action is based upon
the "success rate" sliders for the coalition, Afghan, and Taliban
forces, respectively. The "fight" success rate of the CF and AGF is
normalized on the basis of relative populations; for move and flee, both
the success rates and the determinations of outcome for these two
factions are calculated independently.

In some cases, these success rates are modified, depending upon the
combination of choices that the two sides make. For example, if the
soldiers choose to fight and the Taliban choose to flee, it is assumed
that the soldier's choice will impede the Taliban's choice; hence, a
negative adjustment to the success rate. The amount of the adjustment is
set by the slider labeled "action-modifier." In the matrices below, a
'-' sign indicates a negative adjustment and a '+' sign indicates a
positive adjustment. (Initiative is currently assigned at random.)

Soldiers (CF and AGF) and Taliban should have an additional choice
between "move" (to an adjoining patch) or "stay" (on the current patch).
However, we also thought that this choice should not be predicated on
the danger level previously calculated. Therefore, the choice
"move/stay" is further divided by the slider labeled "stay?" for each of
these three combatant factions. Note that the choice between move and
stay is no longer made by the CF and AGF agents combined, but rather
separately for each. Also, the success rate is separate for each.
Finally, "move" is distinct from "flee" in that all the agents attempt
to move to the *same* randomly-chosen (neighboring) patch, rather than
fleeing to *any* randomly-chosen (neighboring) patch.

**Figure 2-2**. Results of action choices

  --------------------------------------- -----------------------------------------------------
  ![](media/image60.jpeg){width="2.3in"   ![](media/image61.jpeg){width="2.308333333333333in"
  height="2.341666666666667in"}           height="2.341666666666667in"}

  --------------------------------------- -----------------------------------------------------

The grids in Figure 2-2 indicate the results of the action choice for
each side. T is the Taliban choice (left column and left side in each
cell) and S is the soldier choice (top row and right side of each cell).
The top row and left column indicate the choice of each side, but the
cells indicate the action; in two cases---both when the Taliban choose
move/stay and the soldiers choose fight---the Taliban are forced to
fight, but now with a negative adjustment to their success rate. For the
inner cells: A = flee, B = move/stay, and C = fight. Both the Taliban
and the AGF agents have similar sets of sliders for perception, and
identical formulae for perception, perceived effectiveness, etc.

## Data Step

The outcomes of the Action Step are recorded. This includes the
casualties, movement of agents, ideology of agents, mindshare, and
territorial control.

## Learning Step

Each agent has a tribal affiliation, a location within Afghanistan, and
an initial ideology. Afghan citizens may support one side or the other
based on which behavior maximizes their utility given by Eqn (1). In
this step, citizen agents adjust their ideology and loyalty to the
leaders based on the latest developments on their patch, given their
previous choices. This step incorporates the learning theories described
in the main body of this report. Agents' beliefs and attitudes change
depending on the information available and the situation on their patch,
the consequences they experience, and the beliefs and attitudes of other
agents around them. A detailed description of this step follows.

Each tribe has one or more leaders. Larger tribes such as the Pashtuns
have several leaders. Each leader has

-   resources ![](media/image62.wmf){width="0.4583333333333333in"
    height="0.20625in"}: 0 none, 1 maximum possible; set using a slider;

-   orders: -1=help Taliban, 0=be neutral, +1=help government/coalition
    forces;

-   ideology: currently, leaders' ideologies are reflected in their
    orders.

Initial ideology of citizens is in the range \[-1, +1\] where -1= fully
support the Taliban and +1=fully support the government. The ideology is
initialized with a normal distribution around a given mean for the tribe
or subtribal group (set by a series of sliders). Each agent is
responsive to exactly one leader.

For all of the equations concerning psychological change (learning),
boundaries are set at 0 and 1 for loyalty and at -1 and 1 for ideology.
Change can occur to push an agent's values up to those boundaries at
which point no further change in that direction (i.e., beyond the
boundary) is possible. This is realistic. Loyalty and ideology have
natural endpoints: complete loyalty or none; completely behind the
government or completely behind the insurgency. And there are people,
often many people, who are at the boundaries. Hence, change should be
able to push an agent until the agent reaches a boundary, and then the
agent can go no further. This is analogous to a physical process where a
boundary (e.g., a wall) exists that is independent of the process (e.g.,
some force) causing movement toward the boundary.

### Change in Loyalty 

> Let ![](media/image63.wmf){width="0.15486111111111112in"
> height="0.17708333333333334in"}= loyalty of the citizen agent to the
> leader. ![](media/image63.wmf){width="0.15486111111111112in"
> height="0.17708333333333334in"} is given by
>
> ![](media/image64.wmf){width="1.0416666666666667in"
> height="0.28055555555555556in"}
>
> ![](media/image65.wmf){width="0.29930555555555555in"
> height="0.19791666666666666in"}leader's order: -1=help Taliban, 0=be
> neutral, 1=help government;
>
> ![](media/image66.wmf){width="0.2916666666666667in"
> height="0.17708333333333334in"}agent's behavior: -1=help Taliban, 0=be
> neutral, 1=help government;
>
> ![](media/image67.wmf){width="0.13472222222222222in"
> height="0.17708333333333334in"} = agent *i*\'s perception of her
> leader's legitimacy: minimum of 0 and maximum of 1.
>
> Agent *i*\'s perception of her leader's legitimacy
> (![](media/image68.wmf){width="0.16666666666666666in"
> height="0.25in"}) changes at each time step according to
>
> ![](media/image69.wmf){width="4.152777777777778in"
> height="0.4979166666666667in"} (14)
>
> where ![](media/image70.wmf){width="0.23819444444444443in"
> height="0.25833333333333336in"} are weights assigned to each of the
> variables below.
>
> 1\. Cognitive dissonance effect (Festinger 1957): The agent changes
> her loyalty on the basis of the distance between her own behavior and
> the order of her leader. Thus, let
> ![](media/image71.wmf){width="0.28055555555555556in" height="0.25in"}
> be the speed with which cognitive dissonance operates. Then
>
> ![](media/image72.wmf){width="3.354861111111111in"
> height="0.4673611111111111in"} (15)
>
> The above equation pushes loyalty up by half of
> ![](media/image73.wmf){width="0.28055555555555556in" height="0.25in"}
> if the agent does what her leader orders, down by half of
> ![](media/image74.wmf){width="0.28055555555555556in" height="0.25in"}
> if she differs a little, and down by a whole
> ![](media/image75.wmf){width="0.28055555555555556in" height="0.25in"}
> if she does the opposite of what her leader orders.
> ![](media/image73.wmf){width="0.28055555555555556in" height="0.25in"}
> is set using a slider and ![](media/image76.wmf){width="0.75in"
> height="0.28055555555555556in"}.
> ![](media/image77.wmf){width="0.4166666666666667in"
> height="0.25833333333333336in"} is then forced on the range
> ![](media/image78.wmf){width="0.4583333333333333in"
> height="0.28055555555555556in"} using
>
> ![](media/image79.wmf){width="2.1958333333333333in"
> height="0.8333333333333334in"} (16)
>
> 2\. Results-based effect (Coleman 1990): If the agent's situation
> improves at a time step, the agent's loyalty to her leader increases,
> and the reverse. Thus, let
> ![](media/image80.wmf){width="0.20833333333333334in" height="0.25in"}
> be the speed with which results-based adjustment operates. Then
>
> ![](media/image81.wmf){width="2.8194444444444446in"
> height="0.25833333333333336in"} (17)
>
> where ![](media/image82.wmf){width="0.4027777777777778in"
> height="0.25in"} is agent *i*\'s welfare, which is a combination of
> the economic welfare
> (![](media/image83.wmf){width="0.16666666666666666in"
> height="0.17708333333333334in"}, from the utility function equation)
> and the security threat
> (![](media/image84.wmf){width="0.15486111111111112in"
> height="0.19791666666666666in"}). Economic welfare is described below.
> The security threat
> (![](media/image84.wmf){width="0.15486111111111112in"
> height="0.19791666666666666in"}) is calculated in the following way,
> using the four components of the security factor (*r~f~*, *r~h~*,
> *r~b~*, and *r~c~*):
>
> ![](media/image85.wmf){width="2.7916666666666665in" height="0.5in"}
> (18)
>
> Clearly, the 0.5 constant could be changed to something else, as long
> as it remains between 0 and 1.
>
> ![](media/image86.wmf){width="1.1854166666666666in"
> height="0.4673611111111111in"} (19)
>
> where ![](media/image87.wmf){width="0.17708333333333334in"
> height="0.25in"} is a constant that is currently set to 10, and
> ![](media/image88.wmf){width="0.2916666666666667in" height="0.25in"}
> is the number of deaths resulting from fighting in the previous time
> step.
>
> ![](media/image89.wmf){width="0.9430555555555555in"
> height="0.42569444444444443in"} (20)
>
> where ![](media/image90.wmf){width="0.15486111111111112in"
> height="0.19791666666666666in"} is the number of Afghan and coalition
> forces, ![](media/image91.wmf){width="0.13472222222222222in"
> height="0.17708333333333334in"} is the number of Taliban, and
> ![](media/image92.wmf){width="0.19791666666666666in"
> height="0.19791666666666666in"} is the number of citizens on the
> patch.
>
> ![](media/image93.wmf){width="1.2659722222222223in"
> height="0.4583333333333333in"} (21)
>
> The above are combined to compute
>
> ![](media/image94.wmf){width="2.6125in" height="0.2916666666666667in"}
> (22)
>
> ![](media/image95.wmf){width="0.7916666666666666in"
> height="0.25833333333333336in"}![](media/image96.wmf){width="0.55in"
> height="0.25in"}
>
> Note: The real-time inputs from the WITS data can be substituted into
> the security threat
> (![](media/image97.wmf){width="0.15486111111111112in"
> height="0.19791666666666666in"}) in one of two ways:
>
> i\) They can be substituted in for
> ![](media/image98.wmf){width="0.16666666666666666in"
> height="0.25833333333333336in"}using the formula:
>
> ![](media/image99.wmf){width="3.6020833333333333in" height="0.5in"}
> (23)
>
> ii\) They can be substituted in for *r~h~* using the formula:
>
> ![](media/image100.wmf){width="1.1854166666666666in"
> height="0.4673611111111111in"} (24)
>
> The security threat components with the WITS data added is
>
> ![](media/image101.wmf){width="3.4166666666666665in"
> height="0.2916666666666667in"} (25)
>
> To complete the substitution of the WITS data, the user should reduce
> the weight of either
> ![](media/image102.wmf){width="0.20833333333333334in"
> height="0.25833333333333336in"} or
> ![](media/image103.wmf){width="0.19791666666666666in" height="0.25in"}
> to zero and set the weight for
> ![](media/image104.wmf){width="0.34305555555555556in" height="0.25in"}
> to ![](media/image105.wmf){width="0.6111111111111112in"
> height="0.25in"}, such that
>
> ![](media/image106.wmf){width="1.7916666666666667in"
> height="0.25833333333333336in"} (26)
>
> The WITS data have a larger temporal and spatial effect than other
> measures of security threat. The focal patch of the effect is
> determined by the geospatial coordinates of the real event. The
> intensity at the moment and place of maximum effect are determined by
> the input data. The effect has both a temporal and spatial decay away
> from the moment and place of impact and has a maximum radius of effect
> around the point of impact, as set by the user.
>
> ![](media/image82.wmf){width="0.4027777777777778in" height="0.25in"}
> is calculated at time *t* as a ratio between the sliders for the
> economics and security weights,
> ![](media/image107.wmf){width="0.20625in" height="0.25in"} and
> ![](media/image108.wmf){width="0.20625in" height="0.25in"}, such that
>
> ![](media/image109.wmf){width="1.94375in"
> height="0.4673611111111111in"} (27)
>
> ![](media/image110.wmf){width="0.20833333333333334in" height="0.25in"}
> is set using a slider and
> ![](media/image111.wmf){width="0.6736111111111112in"
> height="0.28055555555555556in"}.
>
> ![](media/image112.wmf){width="0.42569444444444443in"
> height="0.25833333333333336in"} is then forced on the range
> ![](media/image78.wmf){width="0.4583333333333333in"
> height="0.28055555555555556in"} using
>
> ![](media/image113.wmf){width="2.3194444444444446in"
> height="0.8333333333333334in"} .(28)
>
> 3\. Homophily effect (Bourdieu 1984, Carley 1986): The agent's beliefs
> become more similar to the beliefs of agents like himself who are
> around him at each time step. Thus, let *δ*~H~ be the speed with which
> homophily-based adjustment operates. Then
>
> ![](media/image114.wmf){width="2.4166666666666665in"
> height="0.25833333333333336in"} (29)
>
> where ![](media/image115.wmf){width="0.34305555555555556in"
> height="0.25in"} is the mean loyalty to agent *i*\'s leader in the
> eyes of other followers in agent *i*\'s vicinity at time *t*.
>
> ![](media/image116.wmf){width="0.20625in" height="0.25in"} is set
> using a slider and ![](media/image117.wmf){width="0.69375in"
> height="0.28055555555555556in"}.
>
> 4\. Socialization (St. Augustine, 4^th^ century): This is the default
> theory, i.e., no change in agent's loyalty.
>
> ![](media/image118.wmf){width="1.0416666666666667in"
> height="0.25833333333333336in"} (30)

### Change in Ideology 

> Agent ![](media/image119.wmf){width="9.305555555555556e-2in"
> height="0.17708333333333334in"}'s ideology changes over time according
> to the following:
>
> ![](media/image120.wmf){width="4.166666666666667in"
> height="0.4861111111111111in"} (31)
>
> where ![](media/image121.wmf){width="0.25in" height="0.25in"} are
> weights assigned to each of the variables below.
>
> 1\. Cognitive dissonance effect (Festinger 1957): The agent's ideology
> changes in the direction of her own behavior. Let
> ![](media/image122.wmf){width="0.28055555555555556in" height="0.25in"}
> be the speed with which cognitive dissonance operates. Then
>
> ![](media/image123.wmf){width="2.7777777777777777in"
> height="0.42569444444444443in"} .(32)
>
> ![](media/image124.wmf){width="0.28055555555555556in"
> height="0.25in"}is set by a slider and
> ![](media/image125.wmf){width="0.7361111111111112in" height="0.25in"}.
> ![](media/image126.wmf){width="0.4166666666666667in"
> height="0.25833333333333336in"} is then forced on the range
> ![](media/image127.wmf){width="0.5416666666666666in"
> height="0.28055555555555556in"} using
>
> ![](media/image128.wmf){width="2.3229166666666665in"
> height="0.8333333333333334in"} (33)
>
> 2\. Results-based effect (Coleman 1990): If the agent's situation
> improves, the agent's ideology shifts toward whoever controls the
> territory. Thus, let
> ![](media/image129.wmf){width="0.19791666666666666in" height="0.25in"}
> be the speed with which results-based adjustment operates. Then
>
> ![](media/image130.wmf){width="3.20625in"
> height="0.25833333333333336in"} (34)
>
> where ![](media/image82.wmf){width="0.4027777777777778in"
> height="0.25in"} is agent *i*\'s welfare at time *t* and reflects
> economic welfare, ![](media/image83.wmf){width="0.16666666666666666in"
> height="0.17708333333333334in"} (from the utility function equation),
> and the agent's security,
> ![](media/image131.wmf){width="0.15486111111111112in"
> height="0.19791666666666666in"}. Economic welfare is described below.
> ![](media/image82.wmf){width="0.4027777777777778in" height="0.25in"}
> is calculated as a ratio between the sliders for economics and
> security weights, ![](media/image107.wmf){width="0.20625in"
> height="0.25in"} and ![](media/image132.wmf){width="0.20625in"
> height="0.25in"}:
>
> ![](media/image133.wmf){width="1.94375in"
> height="0.4673611111111111in"} (35)
>
> ![](media/image134.wmf){width="0.3333333333333333in"
> height="0.20625in"} denotes the faction in control of agent *i*\'s
> territory at time *t* (with the usual coding: -1=Taliban,
> 1=government, intermediate values indicate some degree of contested
> control).
>
> ![](media/image135.wmf){width="0.19791666666666666in" height="0.25in"}
> is set using a slider and
> ![](media/image136.wmf){width="0.6736111111111112in"
> height="0.28055555555555556in"}.
> ![](media/image137.wmf){width="0.42569444444444443in"
> height="0.25833333333333336in"} is then forced on the range
> ![](media/image127.wmf){width="0.5416666666666666in"
> height="0.28055555555555556in"} using
>
> ![](media/image138.wmf){width="2.3645833333333335in"
> height="0.8333333333333334in"} (36)
>
> 3\. Homophily effect (Bourdieu 1984, Carley 1986): The agent's beliefs
> become more like the beliefs of others like himself. Thus, let *γ*~H~
> be the speed with which homophily-based adjustment operates. Then
>
> ![](media/image139.wmf){width="2.4027777777777777in"
> height="0.28055555555555556in"} (37)
>
> where ![](media/image140.wmf){width="0.34305555555555556in"
> height="0.25833333333333336in"}is the mean ideology of agents in agent
> *i*\'s territory at time *t*.
> ![](media/image141.wmf){width="0.20625in" height="0.25in"} is set
> using a slider and ![](media/image142.wmf){width="0.69375in"
> height="0.28055555555555556in"}.
>
> 4\. Socialization (St. Augustine, 4^th^ century): This is the default
> theory, i.e., no change in adult agent's ideology.
>
> ![](media/image143.wmf){width="1.0833333333333333in"
> height="0.25833333333333336in"} (38)

## Utility Step

At this step, given the updated ideology and loyalty from the learning
step together with the economic, coercion, violence, repression and
social influence variables, each agent computes the relative utility of
each of the three behavior choices he or she has: be neutral (0), help
the AGF and CF (+1), or help the Taliban (-1). The utility function for
citizen agents is given by the Cobb-Douglas function in Eqn (1). This
function is evaluated three times at each time step for each agent,
representing the three behavior choices each agent considers. A detailed
description of this step follows:

![](media/image144.wmf){width="0.16666666666666666in"
height="0.19791666666666666in"} = coercion experienced by the agent from
the leader. ![](media/image144.wmf){width="0.16666666666666666in"
height="0.19791666666666666in"} is given by

![](media/image145.wmf){width="1.0833333333333333in"
height="0.28055555555555556in"} (39)

where ![](media/image146.wmf){width="0.6111111111111112in"
height="0.20625in"} is the relative magnitude of the resources that can
be brought by the leader to bear against an agent. *R* is set using a
slider.

![](media/image147.wmf){width="0.13472222222222222in"
height="0.17708333333333334in"}= force exerted by the difference in
ideology between the agent and the course of action he is considering.
![](media/image148.wmf){width="0.13472222222222222in"
height="0.17708333333333334in"} is given by

> ![](media/image149.wmf){width="0.9027777777777778in"
> height="0.28055555555555556in"} (40)

where ![](media/image150.wmf){width="0.7972222222222223in"
height="0.20625in"} denotes the ideology of the agent , -1=fully
pro-Taliban, +1= fully pro-government.

![](media/image151.wmf){width="0.2916666666666667in"
height="0.17708333333333334in"}economic welfare experienced by the
citizen agent. ![](media/image152.wmf){width="0.16666666666666666in"
height="0.17708333333333334in"} is given by

> ![](media/image153.wmf){width="1.7048611111111112in" height="0.25in"}
> (41)

where ![](media/image154.wmf){width="0.23819444444444443in"
height="0.20833333333333334in"} denotes the best possible feasible
economic situation for an agent,
![](media/image155.wmf){width="0.23819444444444443in"
height="0.20833333333333334in"}denotes the worst possible economic
situation for an agent, and
![](media/image156.wmf){width="0.25833333333333336in"
height="0.17708333333333334in"} denotes the economic situation the agent
expects by following the given considered behavior. If the citizen agent
belongs to an area where opium is grown (a 38% chance on "opium"
patches), then ![](media/image157.wmf){width="0.375in"
height="0.20833333333333334in"}\$2000; if the citizen agent belongs to
an area where opium is not grown, then
![](media/image157.wmf){width="0.375in"
height="0.20833333333333334in"}\$300; and the worst economic situation
all agents expect is
![](media/image158.wmf){width="0.23819444444444443in"
height="0.20833333333333334in"}=\$50.

![](media/image159.wmf){width="0.2916666666666667in"
height="0.19791666666666666in"}violence welfare experienced by the
citizen agent. ![](media/image160.wmf){width="0.16666666666666666in"
height="0.19791666666666666in"} is given by

![](media/image161.wmf){width="0.4979166666666667in"
height="0.4673611111111111in"} (42)

where

![](media/image162.wmf){width="1.3333333333333333in"
height="0.4583333333333333in"} .(43)

![](media/image163.wmf){width="0.19791666666666666in"
height="0.25833333333333336in"} is the worst possible value of
![](media/image164.wmf){width="0.16666666666666666in" height="0.25in"}.

Repression operates only if the citizens are helping the side that is
outnumbered on their patch. If the considered behavior is neutrality or
helping the predominant force on their patch, no repression occurs
(![](media/image165.wmf){width="0.4166666666666667in"
height="0.19791666666666666in"}). If repression occurs, it is less
effective the more that other citizens in some circle around the
deciding agents are defying repression. Thus, citizens are less likely
to defy factions that have a relatively strong presence on their patch,
especially when that faction has little support among other citizens in
the general area. Let
![](media/image166.wmf){width="0.16666666666666666in"
height="0.17708333333333334in"}= effectiveness of repression by armed
forces (Taliban or Afghan forces) at the patch level.
![](media/image167.wmf){width="0.16666666666666666in"
height="0.17708333333333334in"} is computed as follows:

> ![](media/image168.wmf){width="1.4166666666666667in"
> height="0.4673611111111111in"} (44)

where

![](media/image169.wmf){width="0.19791666666666666in" height="0.25in"}=
the number of government soldier agents in the focal agent's patch,

![](media/image170.wmf){width="0.20833333333333334in" height="0.25in"}=
the number of coalition soldier agents in the focal agent's patch,

![](media/image171.wmf){width="0.19791666666666666in" height="0.25in"}=
the number of Taliban insurgent agents in the patch, and

![](media/image172.wmf){width="0.17708333333333334in" height="0.25in"}=
the total number of citizen agents in the patch.

Positive values of ![](media/image173.wmf){width="0.15486111111111112in"
height="0.13472222222222222in"} indicate that the AGF and CF outnumber
the Taliban, whereas negative values indicate the opposite. The
magnitude of ![](media/image173.wmf){width="0.15486111111111112in"
height="0.13472222222222222in"} indicates the degree to which one
faction outnumbers the other.
![](media/image174.wmf){width="0.16666666666666666in"
height="0.17708333333333334in"} is then given by

> ![](media/image175.wmf) ,(45)

where ![](media/image176.wmf), the proportion of citizen agents in a
larger geographical area (one or more patches) performing
behavior![](media/image177.wmf).

The first factor in Eqn (45) is 0 unless the considered behavior opposes
the dominant force on the patch. The second factor increases with the
predominance of the fighters of one side and decreases the more citizens
in the given area are performing the behavior this particular agent is
considering.

For example, suppose that for a given patch,
![](media/image169.wmf){width="0.19791666666666666in"
height="0.25in"}=0,
![](media/image170.wmf){width="0.20833333333333334in"
height="0.25in"}=0,
![](media/image171.wmf){width="0.19791666666666666in"
height="0.25in"}=200,
![](media/image172.wmf){width="0.17708333333333334in"
height="0.25in"}=50, and all citizens were neutral in the preceding time
step. Then ![](media/image178.wmf){width="1.4569444444444444in"
height="0.19791666666666666in"}. For behavior
![](media/image179.wmf){width="0.16666666666666666in"
height="0.17708333333333334in"}=+1 (support soldiers)

> ![](media/image180.wmf){width="4.592361111111111in"
> height="0.8708333333333333in"}.

Suppose 30 citizens support the Taliban. Then for behavior
![](media/image181.wmf){width="0.4861111111111111in"
height="0.17708333333333334in"} (support Taliban)

> ![](media/image182.png){width="5.082638888888889in"
> height="0.4270833333333333in"}
>
> ![](media/image183.png){width="2.59375in" height="0.1875in"}
>
> .

For the above example, if
![](media/image184.wmf){width="0.6111111111111112in" height="0.25in"},
for support soldiers ![](media/image185.wmf){width="0.75in"
height="0.25in"} ![](media/image186.wmf){width="0.9118055555555555in"
height="0.25in"}0.815, whereas for support Taliban,
![](media/image187.wmf){width="1.5416666666666667in" height="0.25in"},
which implies that for this patch, repression hinders support for
soldiers but does not hinder support for the Taliban.

For the influence of close associates, a quantity represented by
![](media/image188.wmf){width="0.625in" height="0.25in"} in the utility
function, let ![](media/image189.wmf){width="0.3645833333333333in"
height="0.25in"} the deviation of an agent's behavior from the behavior
of agents within a certain radius of the focal agent in the previous
time step (i.e., ![](media/image190.wmf){width="0.3645833333333333in"
height="0.20625in"}.
![](media/image191.wmf){width="0.17708333333333334in"
height="0.17708333333333334in"} is computed as follows:

> ![](media/image192.wmf) (46)

For example, if there are ![](media/image193.wmf) on a patch and 100 of
them supported the soldiers last time and 20 supported the Taliban in
the last time step, then for "support Taliban" behavior
![](media/image194.wmf) , for "neutral" behavior ![](media/image195.wmf)
and for "support the soldiers" behavior ![](media/image196.wmf). If
![](media/image197.wmf), then for "support soldiers"
![](media/image198.wmf) ![](media/image199.wmf) 0.83, for "neutral"
![](media/image200.wmf) whereas for "support Taliban,"
![](media/image201.wmf) 0.90, which implies that for this patch,
influence works to increase support for soldiers relative to Taliban.

## Behavior Step

In this step, each agent picks the behavior that maximizes his or her
utility, which was computed in the Utility step. Every Taliban helper
and soldier helper has a force weight that indicates his or her relative
contribution to the battles. Therefore, this aggregate behavior of the
citizen agents influences the subsequent action choices for the CF, AGF,
and Taliban by affecting the perceived effectiveness for each. It also
contributes to the real effectiveness of each, which is used to
determine the Action step's outcomes.

## Check Step

In this step, the outputs from the simulation (i.e., territorial
control, hardship, and mindshare) from the current step are compared to
those from the previous steps to determine if all of the outcomes have
become sufficiently stable to terminate the simulation. The length of
the simulation run will depend on the frequency and the significance of
the events and changes inputted in the Update step. If the purpose of a
run is training and testing the relationship between simulation outputs
and real-world events, then we are constrained to using the putative
relationship between the simulated and real-world clock. In this case
the stabilization criterion is no longer used because these experiments
constrain the total number of simulated time steps that are allowed to
elapse to represent the seven months that transpired real-world.

# NetLogo Simulation Outputs

***Territorial Control***

This describes the control at the patch level and is computed as
follows:

![](media/image202.wmf){width="2.361111111111111in"
height="0.4673611111111111in"} (47)

where ![](media/image203.wmf){width="1.1041666666666667in"
height="0.25in"} is the effectiveness of soldiers and
![](media/image204.wmf){width="0.4979166666666667in" height="0.25in"}
that of the Taliban.

***Hardship***

![](media/image205.wmf){width="4.166666666666667in" height="0.5in"} (48)

Here clip-to-range is a "squashing" function used to keep hardship
values between 0 and 1. Economics and security sliders range from 1 to
10 and can be used to select the relative importance of these two
aspects of pressure that agents are facing.

***Mindshare***

Mindshare refers to which side the citizen agents support on the
aggregate. It is calculated on the patch level based upon how many of
the citizen agents choose to support the government, stay neutral, or
support the Taliban. The aggregate of this attribute is used to track
and measure certain regional effects. Mindshare that is heavily tilted
one way or the other can also influence the outcome of combat. Mindshare
is calculated as follows:

> ![](media/image206.wmf){width="3.55in" height="0.6652777777777777in"}
> (49)

# The Simulation Scenario

These specific simulations are based on the situation from 2005 to 2007
in the Ghazni region of Afghanistan, about halfway between Kabul and
Kandahar. The region was chosen because of a high level of Taliban
activity in that period and a counterinsurgency operation (Operation
Maiwand) by the Afghan government and coalition forces in June 2007. The
Taliban use removal and intimidation of the local leadership as a tactic
to increase their control over the population. As one of our input
variables, therefore, we use assassinations or attempted assassinations
of local leaders. Our output variable will be the allegiance of the
citizen population.

# *Calibration*

The simulation is trained on data covering seven months in 2007
(3/15/2007 to 10/15/2007, to avoid a seasonal effect in violence
unaccounted for by our simulation). This year was selected because it
contains a good variety of input data events on which to train the
simulation.

The three significant components of the calibration process, in which
key parameter values are tried and evaluated, are the inputs, the
outputs, and the correlation measure between the simulation and the
real-world data.

## Inputs {#inputs .unnumbered}

> The simulation accepts both static inputs and variable inputs. The
> static inputs are those that come directly from data, including the
> initial population settings as well as events that were known to
> transpire during the simulation period. The variable inputs are
> controls that we perturb during calibration to identify useful
> settings.

### *Static Inputs* {#static-inputs .unnumbered}

> The static inputs to the simulation are detailed in other sections of
> this document. Briefly, they include

-   geography

-   population per patch (including religious affiliations and tribal
    memberships)

-   hand-selected political events (assassinations of prominent figures;
    see below); not all political events have direct analogues within
    the current simulation, but assassinations do, and all were
    incorporated into the event stream

-   perceptive acuity for both factions: this is the likelihood of each
    faction correctly identifying its own units and its opponents'
    units, expressed in a matrix of sliders within the NetLogo
    simulation

-   effectiveness measures for both factions

-   leaders\' resources and standing orders (coarse allegiance)

-   periodic infusion rates of new soldiers and Taliban

#### Regarding Assassination

> These are the specific assassination events that are introduced into
> the simulation:[^1]

-   April 16^th^, 2007 -- In Andar, Ghazni, Afghanistan, assailants
    launched rockets near a high school, causing no injuries or
    damage. The attack targeted the governor of the province, who was
    attending a road construction inauguration event. No group claimed
    responsibility, although it was widely believed the Taliban was
    responsible.

-   June 1^st^, 2007 -- In Khwaja Umari, Ghazni, Afghanistan, armed
    assailants abducted the Chief of Gomal District from his house. The
    Taliban claimed responsibility.

-   June 2^nd^, 2007 -- In Angor Village, Jaghuri, Ghazni, Afghanistan,
    armed assailants stormed the house of a police chief then fired upon
    and killed five civilians. The Taliban claimed responsibility.

-   August 3^rd^, 2007 -- Near Ghazni, Ghazni, Afghanistan, assailants
    abducted a provincial government official. The Taliban claimed
    responsibility.

-   September 3^rd^, 2007 -- Near the Leewani Bazaar, in Qareh Bagh,
    Ghazni, Afghanistan, assailants kidnapped two civilian relatives of
    a female legislator. The Taliban claimed responsibility.

> The simulation assumes that Taliban assassination of pro-government
> leaders will have the effect of moving the aggregate leadership toward
> a pro-Taliban stance. Each act of aggression by the Taliban against
> that leader will move the leader's order toward -1 and away from +1.
>
> Furthermore, the leader's order is adjusted according to which side
> (the Taliban or the government) controls the Ghazni patches. Control
> is measured using the number of, and the force weights for, CF, AGF,
> and Taliban, such that
>
> Soldier-Force =![](media/image49.wmf){width="2.1666666666666665in"
> height="0.25in"}
>
> Taliban-Force =![](media/image50.wmf){width="1.5694444444444444in"
> height="0.25in"}
>
> Control = ![](media/image207.png){width="2.0208333333333335in"
> height="0.3541666666666667in"}
>
> where the *n* values are the number of different factions present, the
> *e* values are the effectiveness of each faction, and the S, C, and T
> subscripts refer to the factions themselves.
>
> The leader's order is adjusted by control using the following
> function:
>
> ![](media/image208.wmf){width="2.486111111111111in" height="0.25in"}
>
> To understand this equation and why Taliban control does not influence
> the order, think of the status quo ante as the baseline: the
> leadership\'s order with no assassinations by the Taliban and no
> government troop surge. Then the Taliban and the government each have
> an activity available to them to affect the order: the Taliban can
> assassinate and the government can initiate a troop surge. If the
> surge fails militarily, meaning that the government fails to achieve
> control, then the leadership\'s order should remain unaffected, that
> is, return to the presurge status quo. (The rationale that the Taliban
> somehow seem more like \"winners\" by that failure so the leadership
> should shift to them was not assumed, because a failed surge could
> just as easily make a larger surge likely, and the leadership would
> not become more pro-Taliban in that case.)
>
> In other words, control in the area by the coalition forces can affect
> a leader's orders, but control by the Taliban does not. These
> equations are incorporated into the flow diagram in Table 2-4.

**Table 2-4**. Simulation procedures within the conceptual flow diagram

+-------------------------------------------+--------------------------+
| Simulation Procedures                     | > Conceptual Flow        |
|                                           | > Diagram                |
+-------------------------------------------+--------------------------+
| **1)** Regenerate new agents **-- add     | > Start,                 |
| surge**                                   | > ![](m                  |
|                                           | edia/image209.wmf){width |
| Time decay of WITS violence per patch     | ="0.34305555555555556in" |
|                                           | > height=                |
| **1)** Input new WITS data **-- input     | "0.19791666666666666in"} |
| assassination data; update leader's       |                          |
| order**                                   | -   Set sliders          |
|                                           |                          |
| Small chance of civilians moving to       | -   Initialize           |
| neighboring patch                         |                          |
|                                           | -   **1) Update step**   |
| Record fighter-agent deaths (previous     |                          |
| turn deaths: "violence-last-turn")        | -   Action step          |
|                                           |                          |
| Calculate influence and repression for    | -   **2) Data step**     |
| utility function                          |                          |
|                                           | -   Learning step        |
| Calculate real and perceived              |                          |
| effectiveness                             | -   **3) Utility step**  |
|                                           |                          |
| Commence battle, or troops may move if no | -   **4) Behavior step** |
| battle                                    |                          |
|                                           | -   Advance time or stop |
| Record fighter-agent deaths (deaths this  |                          |
| turn: "violence-this-turn")               | > *t* = *t+*1            |
|                                           |                          |
| **2)** Store control, mindshare, and E^+^ |                          |
| for each patch **-- store control for     |                          |
| Ghazni region; adjust leader's order      |                          |
| based on control**                        |                          |
|                                           |                          |
| Calculate partial security threat for     |                          |
| learning theory                           |                          |
|                                           |                          |
| Run learning theory module, which affects |                          |
| agent's support for leaders and ideology  |                          |
|                                           |                          |
| **3)** Calculate remaining factors for    |                          |
| utility function **-- use adjusted        |                          |
| leader's order from above**               |                          |
|                                           |                          |
| **4) Run utility function**               |                          |
|                                           |                          |
| Refresh and visualize effects             |                          |
+-------------------------------------------+--------------------------+

### *Variable Inputs (Key Parameters)* {#variable-inputs-key-parameters .unnumbered}

> The variable inputs differ among the three approaches to the
> simulation described in the body of this document.

### *Concerning Time and Scheduling* {#concerning-time-and-scheduling .unnumbered}

> Note that the time scale is one of the parameters we allow to vary
> over the runs. We hope to identify a setting that best contributes to
> the realism of the simulation output while making no claim that the
> resulting time scale is psychologically or sociologically motivated,
> only that it was useful.
>
> Later in this document, we describe stabilization criteria that are
> used to terminate runs. These criteria are appropriate in the context
> described later, but here, we are training and testing the
> relationship between simulation outputs and real-world events, so we
> are constrained to using the putative relationship between the
> simulated and real-world clock. Hence, these experiments constrain the
> total number of simulated time steps (ticks) that are allowed to
> elapse, to represent the seven months that transpired real-world.
>
> The political events are, therefore, scheduled to correspond to ticks
> on the simulation clock. Table 2-5 shows the exact time steps used for
> events in the model runs. This is just an example; clearly, the
> experiment includes more than seven events in the simulations.

**Table 2-5**. Model event time steps

  -------- --------- -------- -------- ----------- -------- -------- ---------
           **Event   **#2**   **#3**   **Surge**   **#4**   **#5**   **End**
           #1**                                                      

  **3      11        26       27       37          47       58       68
  days**                                                             

  **7      5         12       12       16          21       25       35
  days**                                                             

  **14     3         6        6        8           11       13       23
  days**                                                             
  -------- --------- -------- -------- ----------- -------- -------- ---------

## Outputs {#outputs .unnumbered}

> The critical output is the *mindshare*, or the net level of support
> that a region has for the coalition government. Briefly, mindshare is
> computed using the proportion of the general citizenry that is charged
> (helping either the Taliban or the coalition solders).

# Three Incremental Agent Designs

We pursued three options for describing the agents in our simulation,
with the overriding goal of finding a description that produces
simulation results most predictive of the real Afghanistan. The specific
goals were as follows:

1.  Generate three agent descriptions

    a.  One description should have entirely static agents.

    b.  One description should support adaptation.

    c.  One description should represent free exploration.

2.  Run these three scenarios.

3.  Evaluate the results.

4.  Recommend an agent-based modeling solution based on the preceding
    efforts.

The three agent descriptions were embodied in three approaches, each an
independent prototype (system) but all sharing a common base
infrastructure. Each approach built upon (added functionality to) the
previous approach:

1.  The first approach used fixed values of attributes that describe the
    agents and their environment and then evaluated how faithfully the
    results represented the real world.

2.  The second approach began with what we perceived were reasonable
    initial attribute values, but the model could learn and adapt over
    time to adjust these values.

3.  The third approach began with random attribute values and was
    calibrated by a distributed genetic algorithm (GA) to favor those
    values in simulations that more faithfully represented the real
    world.

Each of the next sections summarizes one of these approaches and how
each approach was motivated, designed, implemented, run, and evaluated.

# *Approach 1 -- Fixed Attribute Values*

This was the most direct case of the three. The values for the important
parameters were fixed for each run, the simulation was conducted, and
then the results were compared to what was observed in the real
Afghanistan. For all of these approaches, the benchmarking against the
real Afghanistan was conducted using an assumption that the mindshare
produced by the simulation should positively correlate with the number
of terrorist incidents recorded in real Afghanistan as reported in the
Worldwide Incidents Tracking System, or WITS, database;[^2] see Appendix
2 for an expanded discussion on precisely how this correlation is built.

These are the key parameters we perturbed per fixed simulation run,
including their discrete values:

-   troop surge magnitude: 0, 50, 100, 200, 500 troops

-   time scale: 3 days/tick, 7 days/tick, 14 days/tick

-   leadership theories: legitimacy, coercion, representative

-   influence: on, off

-   repression: on, off

*Troop surge* is a parameter because this time period within 2007
included Operation Maiwand. Although the number of real coalition and
Afghanistan government soldiers deployed is known, we experimented with
different troop deployment settings to calibrate the model\'s
understanding of how many effective simulated troops this represents.
The three leadership theories were combined with *influence* and
*repression*, which attenuate the utility-function weights for the
leadership theories as described in the appendices.

We evaluated all 180 combinations of these settings and selected the
combination of parameter values that did the best job of producing
outputs that matched real-world observations. *NB*: "doing the best job"
was identified across multiple runs from clustering of the results and
identifying common themes within well-performing groups.

## Generalizing across Parameter Settings

> We wished to know which settings worked best for the five parameters
> that we varied. For each parameter, the entire 180-run set was divided
> into partitions on the basis of that parameter's values, and the
> correlation was averaged among all members of each partition (Table
> 2-6). Having previously analyzed the effect of the number of periods
> on the correlation, we divided the 215-day window into 109 periods,
> because that figure represents a compromise between achieving
> steady-state (by using more periods) and minimizing computational
> time. In our analysis, no loss of generality ensued from using only
> 109 periods.

**Table 2-6**. Simulation results by run

  ------------------ ------------- ------------ --------------------------
  **Parameter**      **Setting**   **Mean**     **Standard Deviation**

  **influence**      **0**         **0.0761**   **0.0735**

  influence          1             -0.0773      0.0929

  **repression**     **1**         **0.0318**   **0.0465**

  repression         0             0.0241       0.0974

  **social theory**  **L**         **0.2426**   **0.0301**

  social theory      C             0.0399       0.0911

  social theory      R             -0.1962      0.0414

  **surge**          **0**         **0.2158**   **0.0486**

  surge              50            0.0958       0.0767

  surge              100           0.0753       0.0707

  surge              200           -0.0196      0.0947

  surge              500           -0.1244      0.1383

  **time scale**     **3**         **0.0887**   **0.1155**

  time scale         7             -0.0397      0.0488

  time scale         14            0.0505       0.0712
  ------------------ ------------- ------------ --------------------------

> We also conducted an analysis to determine which parameter setting
> combinations produced means that were statistically significant (Table
> 2-7).[^3]

**Table 2-7**. Statistically significant parameter settings

  --------------- ------------ -------- -------- -------- -------- --------
  Parameter       Settings     90.0%    95.0%    99.0%    99.5%    99.9%

  repression      (0, 1)       No       No       No       No       No

  influence       (0, 1)       Yes      Yes      Yes      Yes      Yes

  surge           (50, 500)    Yes      Yes      Yes      Yes      Yes

  surge           (50, 200)    Yes      Yes      Yes      Yes      Yes

  surge           (200, 500)   Yes      Yes      Yes      Yes      Yes

  surge           (100, 500)   Yes      Yes      Yes      Yes      Yes

  surge           (100, 200)   Yes      Yes      Yes      Yes      Yes

  surge           (0, 500)     Yes      Yes      Yes      Yes      Yes

  surge           (0, 50)      Yes      Yes      Yes      Yes      Yes

  surge           (0, 200)     Yes      Yes      Yes      Yes      Yes

  surge           (0, 100)     Yes      Yes      Yes      Yes      Yes

  surge           (50, 100)    No       No       No       No       No

  time scale      (7, 14)      Yes      Yes      Yes      Yes      Yes

  time scale      (3, 7)       Yes      Yes      Yes      Yes      Yes

  time scale      (3, 14)      Yes      Yes      No       No       No

  social theory   (L, R)       Yes      Yes      Yes      Yes      Yes

  social theory   (C, R)       Yes      Yes      Yes      Yes      Yes

  social theory   (C, L)       Yes      Yes      Yes      Yes      Yes
  --------------- ------------ -------- -------- -------- -------- --------

> These results suggest that the best class of parameter settings we can
> recommend from these 180 runs is as follows:

**Table 2-8**. Recommended parameter settings

  ----------------------------------------- -----------------------------
  Parameter                                 Setting

  influence                                 off

  social theory                             legitimacy

  surge                                     0

  time scale                                3
  ----------------------------------------- -----------------------------

> The value of *repression* does not appear to matter significantly in
> these examples. Interpreting these results is challenging, because the
> argument here is only that we can identify single parameter settings
> that tend to increase the mean correlation between simulated mindshare
> and the number of WITS incidents within Ghazni. Saying that "influence
> = off (0.0761 +/- 0.0735) performs better than influence = on (-0.0773
> +/- 0.0929)" does not make any claim about what the higher correlation
> means. We take it as given that a higher correlation is more
> representative of what occurred in the real world, but we make no
> statement about how to map results observed in the simulated
> environment to outcomes one would expect.

## Analysis

> Among the runs in this approach, the largest separation between values
> was in the social theory parameter. Legitimacy performed (on average)
> significantly better than did either coercion or representative. For
> the legitimacy theory, and only for this theory, the violence weight
> is set to zero, meaning that the leaders' orders have more weight in
> the utility function than does any other single variable. Once
> assassinations are factored in, the positive correlation between
> mindshare and the number of WITS incidents follows naturally.
>
> ![](media/image210.png){width="4.968055555555556in" height="0.1875in"}
>
> where L is loyalty, C is coercion, I is ideology, E is economics, V is
> violence, F is influence, and R is repression. With the current inputs
> to the simulation, only three variables in the utility function really
> matter for changing allegiance: loyalty (L), coercion (C), and
> violence (V). Ideology and economic situation do not vary. Collapsing
> these variables into a single constant allows us to write a degenerate
> form of the utility function:
>
> ![](media/image211.png){width="2.5520833333333335in"
> height="0.1875in"}
>
> Assassinations translate into effects primarily on the leadership's
> orders and secondarily on the leadership's resources and those affect
> choice through loyalty and coercion. Letting \|A\| be the number of
> assassinations that have occurred within the simulation, R be the
> amount of resources the leader can bring to bear, and O be the
> leader's current order:
>
> ![](media/image212.png){width="0.7291666666666666in"
> height="0.23958333333333334in"}
>
> ![](media/image213.png){width="0.84375in"
> height="0.23958333333333334in"}
>
> Rewriting the utility function, expanding the loyalty and coercion
> terms produces
>
> ![](media/image214.png){width="3.645138888888889in"
> height="0.40625in"} ,
>
> where η is the degree to which agents perceive the leader to be
> legitimate. Because the factor weights sum to 1.0 by definition,
> decreasing *w*~V~ typically correlates with an increase in *w*~L~ and
> *w*~C~; thus, as the weight of violence goes down, the more the
> leadership's orders and resources matter. This suggests that, by the
> end of the simulation run (when the number of assassinations has
> plateaued), the utility function has become approximately
>
> ![](media/image215.png){width="2.6145833333333335in"
> height="0.40625in"}
>
> .
>
> Allowing *w*~V~ to approach zero makes this
>
> ![](media/image216.png){width="1.8645833333333333in"
> height="0.40625in"}
>
> .
>
> This form is almost entirely dominated by the leader's order, which
> has become so close to -1.0 as to be indistinguishable from that
> value. This means that the utility function---once the violence weight
> approaches zero and a sufficient number of (WITS) assassinations has
> occurred---would be expected to produce mindshare outcomes that
> correlate positively with the number of WITS incidents, at least near
> the end of the simulated time period.
>
> Note that the higher correlations generated when the violence weight
> is low are primarily artifacts of assumptions made about the data and
> their impacts. Specifically, we assumed that assassinations changed
> orders (i.e., a positive relationship existed). When the loyalty (and
> possibly coercion) weights are relatively high, the simulation
> produces a positive correlation between orders and allegiance as
> described above. Lastly, within the WITS data, other WITS incidents
> also correlate positively with assassinations, probably because both
> are aspects of terrorist violence. (We separated the two and used the
> former as inputs and the latter to evaluate outputs for purposes of
> calibration and sensitivity analysis.) We therefore would expect the
> positive correlation between WITS incidents and allegiance, especially
> when the violence weight is low.

# *Approach 2 -- Learning*

Once the initial, static system was complete, we sought to expand the
dynamic range of the simulation by adding adaptation at the per-agent
level. We did this by building a second prototype system that
incorporated four theories of psychological change, or learning
theories: cognitive dissonance, results-based, homophily, and
socialization. The theories affect two agent attributes that enter into
the utility function, namely support for the leadership and ideology. We
present both the theory and the results from implementation of these
theories within the NetLogo simulation.

## Learning Theory

> These theories add an important capability to the simulation for the
> social scientist or the operator on the ground who is interested in
> long-term changes and effects. The population's ideology and support
> for the leadership, and the ability to change these, have long been
> considered important operationally, as reflected in the goal of
> "winning hearts and minds." Reflecting their empirical importance,
> these two attributes are important in the leadership theories in the
> simulation model. Support for the leadership feeds into loyalty, a
> value that is especially important in the legitimacy theory. Ideology
> is emphasized in the representative theory.
>
> From the theoretical side as well, it is valuable to add these
> theories of change in attributes. In the short term, it may be
> acceptable to take agents' attributes as fixed, but research in
> psychology and social psychology tells us that in the long run,
> attributes may change, especially psychological attributes such as
> attitudes. People's beliefs and attitudes may change because of
> information they receive and consequences they experience, as a result
> of the beliefs and attitudes of those close to them, and even as a
> consequence of their own behavior (Ajzen 2001, Petty et al. 1997). In
> fact, these effects have been shown specifically for the two
> attributes in question, support for leadership (Berger et al. 1998)
> and ideology (Nisbett and Ross 1980).
>
> With respect to the two attributes, the theories operate identically
> although independently in the simulation, so we describe them only
> once. In each theory, each attribute has a separate weight parameter
> that the user sets to dictate the strength of its effect in the
> simulation. In addition, one parameter specifies the speed at which
> the change in support for the leadership can occur, and a parallel
> parameter specifies the speed for change in ideology.
>
> *Cognitive dissonance* theory (Festinger 1957, Nisbett and Ross 1980)
> posits that an individual resolves a contradiction between a behavior
> and a belief or attitude by changing the belief. In the simulation,
> therefore, this means that if an agent's behavior runs contrary to the
> leadership's order, the agent will lower his or her loyalty to, or
> support for, the leader; if the agent's behavior is in accord with the
> leadership's order, his or her support for the leader will increase.
> This is important to include in the simulation because the variety of
> values in the citizen agent's utility function may lead the agent to
> undertake a behavior that contradicts the leadership's orders, the
> agent's ideology, or both. Empirically, this certainly occurs as well.
>
> The *results-based* theory (Coleman 1990) posits that an individual's
> beliefs and attitudes shift in response to how well he or she is
> doing. In the simulation, therefore, if an agent's situation improves,
> the agent's ideology shifts toward whichever side is in control of the
> agent's locality, and toward the opposite side if the agent's
> situation deteriorates. Likewise, the agent's support for the
> leadership increases if the agent's situation improves and decreases
> if it deteriorates. This matches the idea in the field of lowering
> unemployment and improving the local infrastructure to gain the
> loyalty and shift the ideology of the population.
>
> The *homophily* theory (Bourdieu 1984, Carley 1986) posits that an
> individual's beliefs and attitudes become more similar to the beliefs
> and attitudes of those close to and similar to the individual. This is
> an attitudinal version of the effects of social influence seen for
> behavior in the social mobilization theories, and it has similar
> empirical support, such as attitudes in the former East Germany (Opp
> and Gern 1993). In the simulation, an agent shifts his or her support
> for the leadership toward the average support of similar others in the
> vicinity. Likewise, the agent shifts his ideology toward the average
> ideology of similar others in the vicinity.
>
> Finally, the *socialization* theory posits that an individual's
> attitudes do not change in adulthood. Theoretically, this can be
> because attitudes are set in early adulthood, as proposed by the
> Jesuit motto, "Give me a child until he is seven, and I will give you
> the man," or because they are heritable (Olson et al. 2001), or both.
> In the simulation, this means that the agent does not change his or
> her support for the leadership and does not change his or her
> ideology. This theory can be combined meaningfully with another change
> theory to effect the idea that the support for the leadership or the
> ideology may be changeable, but only up to a point. This accords with
> empirical evidence that beliefs and ideologies are often stubborn and
> difficult to change.

## Implementation of Learning

> The learning theories were put in place to affect two key variables in
> the simulation: the support of the citizenry for their leaders, and
> the change in the ideology of the general populace. Each of these
> influences has the same general form:
>
> ![](media/image217.png){width="1.0520833333333333in"
> height="0.15625in"}
>
> That is, the value at time *t*+1 is equal to the value at time *t*
> plus some mitigated change (the lower-case delta is the maximum change
> allowable per one time step; the upper-case delta is the recommended
> change from the learning theory).

## Results from Implementing Learning

> Because both support and ideology share similar (high-level)
> functional form, testing these two learning theories was most easily
> accomplished by varying the value for δ, the maximum allowable change
> resulting from learning in any single time step (Table 2-9). When δ=0,
> the learning theories are effectively turned off, and the simulation
> degenerates into the system presented as Approach 1 in the preceding
> section.

**Table 2-9**. The additional evaluation series

  ------------------------------------------ ------------------------------------------------------
  ![](media/image218.png){width="0.5625in"   ![](media/image219.png){width="0.6041666666666666in"
  height="0.17708333333333334in"}            height="0.17708333333333334in"}

  0.0                                        0.2

  0.2                                        0.0

  0.2                                        0.2
  ------------------------------------------ ------------------------------------------------------

> Additional experimentation---not documented here---led us to the
> conclusion that delta values greater than 0.2 did not provide any
> additional benefit, as larger values often made the simulation too
> volatile to be useful. The results of varying these parameters across
> multiple replications are reported in Figure 2-3.
>
> **Figure 2-3**. The results of varying ideology and leadership support
> parameters across multiple replications: (a) δ*~support~* = 0,
> δ*~ideology~* = 0.2; (b) δ*~support~* = 0.2, δ*~ideology~* = 0; (c)
> δ*~support~* = 0.2, δ*~ideology~* = 0.2.
>
> \(a\)

![](media/image220.jpeg){width="5.669444444444444in"
height="4.252083333333333in"}

> \(b\)
>
> ![](media/image221.jpeg){width="5.669444444444444in"
> height="4.252083333333333in"}
>
> \(c\)
>
> ![](media/image222.jpeg){width="5.669444444444444in"
> height="4.252083333333333in"}
>
> These plots suggest that the model may have slightly more expressive
> power when ideology---but not support for the leader---is allowed to
> change according to the learning theories (adaptation). It is
> important to note, however, that these three distributions are not
> significantly different from one another.

## Analysis

> Both support (loyalty) and ideology are top-level terms in the utility
> function, and both have four separate influences: cognitive
> dissonance; results-based effect; homophily; and socialization. Two of
> these theories have different impacts on the two variables:

1.  *Cognitive dissonance*

    a.  updates *support* by moving the citizen in closer agreement with
        his leader

    b.  updates *ideology* by adding a momentum term to the agent's own
        behaviors

2.  The *results-based effect*

    a.  updates *support* on the basis of whether violence is nearby

    b.  updates *ideology* using only terms unrelated to violence, but
        rooted in our definitions of agent-based welfare

> Homophily and socialization share direction and influence between
> support for leadership and changes in ideology, and hence should not
> contribute to the observed difference in mean correlation observed
> here.
>
> The differences in the update procedures suggest a possible reason for
> the difference in mean correlation: because violence is sporadic in
> the simulations, its influence on support will also be sporadic;
> ideology is free of this chaotic influence. Additionally, updating
> ideology based on the agent's own momentum provides for yet another
> more consistent driver of the agent's behavior. These two effects
> combined may allow the agents to pursue a more consistent course of
> action with respect to ideology than with respect to support for
> leadership, providing a better foundation for the positive
> correlation.

# *Approach 3 -- Calibration via a Genetic Algorithm*

After completing the first two systems, the team moved on to the third
and most flexible prototype system. In this approach, we chose to free a
number of the simulation parameters, specifically those related to the
social theories of leadership, and to allow a free search of the space
by a distributed genetic algorithm (GA). We will review the technical
design, the results and analysis, and directions for future work.

## Technical Design of the GA

> The core technical challenge is that the NetLogo platform on which the
> simulation is based is decidedly single-threaded, which stymies
> parallelism. To work around this, we constructed our GA system so that
> each processing node runs exactly one instance of the NetLogo
> simulation (in headless mode; see the NetLogo documentation at
> <http://ccl.northwestern.edu/netlogo/docs/>). A master node is always
> responsible for managing the central distribution of work and
> collecting results from the processing nodes. We dedicated three
> 8-core machines to the GA, running principally at night and during
> off-hours.
>
> Because NetLogo is a Java application, we used a Java-based
> distribution framework for managing the GA: The Distributed Parallel
> Programming Environment for Java from IBM Alphaworks
> (<http://www.alphaworks.ibm.com/tech/dppej>). This saved considerable
> effort, because we were able to merely subclass elements that had
> already been defined to manage the parallelization.
>
> The specifics of the GA are as follows:

1.  The population size was dictated by the number of processing nodes
    available, typically between 16 and 24 chromosomes (at one
    chromosome per processing node).

2.  Generations were synchronized by the master node.

3.  Each chromosome was a vector of social theory (leadership) parameter
    values. Upon evaluation, each chromosome was instantiated in a
    headless instance of NetLogo, the simulation was run for the
    requisite 64 time steps, and the correlation between mindshare and
    WITS incidents was computed and returned---normalized to
    (1+CORR)/2---as the fitness value.

4.  When computing the next generation, we used single-point crossover
    (80%) and simple Gaussian mutation (20%).

5.  The GA was stopped after completion of 100 generations; the members
    of the final generation were reported as the GA results, along with
    their fitness values.

6.  A single run required almost 13 hours of clock time using 24
    parallel computing nodes.

## Results from the GA

> After a number of trials, we found that individuals like the one
> reported below are representative of the best-performing chromosomes
> discovered by the GA:

**Table 2-10**. Best performing chromosomes

  ------------------------------------------------- ---------------------
  Loyalty weight                                    0.346

  Coercion weight                                   0.249

  Ideology weight                                   \< 0.001

  Economics weight                                  0.096

  Violence weight                                   0.000

  Influence weight                                  0.167

  Repression weight                                 0.141

  Correlation                                       0.152

  Fitness (1+CORR)/2                                0.576
  ------------------------------------------------- ---------------------

> The course of the simulation is expressed in the following two charts
> (Figure 2-4) taken directly from the NetLogo instance.
>
> **Figure 2-4**. Course of simulations

  ------------------------------------------------------ ------------------------------------------------------
  ![](media/image223.wmf){width="2.9256944444444444in"   ![](media/image224.wmf){width="2.9256944444444444in"
  height="1.4131944444444444in"}                         height="1.4131944444444444in"}

  ------------------------------------------------------ ------------------------------------------------------

> The left chart displays the composition of the Ghazni population over
> time. Most notable in this chart is the surge of soldiers
> corresponding to operation Maiwand. The right chart displays the two
> series we are trying to correlate: mindshare and the number of WITS
> incidents within Ghazni.
>
> The most significant finding is that the weights for both ideology and
> violence are essentially zero. This is consistent with our findings in
> Approach 1, in which the legitimacy theory of leadership was
> identified as the best (average) performer; the legitimacy theory also
> specifies that the violence weight should be zero. Furthermore, even
> though the correlation between mindshare and the number of WITS
> incidents is low (0.152), the correlation between mindshare and the
> leader's orders is high (0.977), again consistent with our
> hypothesis---developed within the analysis of Approach 1, above---that
> this is an artifact both of the nature of the model that is embedded
> within the simulation as well as the data with which we are testing
> the model.
>
> It is interesting to note the difference between mindshare and control
> in the simulation for this winning chromosome. Consider these two
> images directly from the NetLogo simulation (Figure 2-5).
>
> **Figure 2-5**. The difference between mindshare and control in the
> simulation

  ------------------------------------------------------ ------------------------------------------------------
  ![](media/image225.wmf){width="2.9881944444444444in"   ![](media/image226.wmf){width="2.9881944444444444in"
  height="2.9881944444444444in"}                         height="2.9881944444444444in"}

  Mindshare                                              Control
  ------------------------------------------------------ ------------------------------------------------------

> Ghazni, loosely represented by the red oval superimposed on the two
> images, has been strongly influenced toward a pro-Taliban mindshare,
> yet it remains firmly in control of the coalition and pro-Afghan
> government.
>
> We also conducted, but do not report here, additional work in which we
> ran the GA assuming that simulated mindshare and the number of WITS
> incidents were anti-correlated. Expectedly, a different set of
> parameter values bubbled to the top of the list of most-fit
> chromosomes. Of greater interest, though, was the fact that those runs
> also highlighted effects on mindshare resulting from the Maiwand surge
> that were not visible when the correlation was presumed to be
> positive: when the troops surged, the populace responded by rallying
> in a pro-government manner.

## Analysis of the GA

> The GA demonstrated the following advantages over both Approach 1
> (static, predefined test cases) and Approach 2 (static cases with
> learning/adaptation):

1.  The combinations of parameter values that we explored were
    significantly more varied than those we had hand-selected for
    Approach 1. Flexibility in choosing parameter combinations is
    especially important in a search algorithm operating in a search
    space as significantly multimodal as this simulated Afghanistan.
    Adaptation succeeds best only when it can overcome the exceedingly
    difficult problem of avoiding local minima, but without knowing the
    exact bounds on solution quality and volatility, a grid-based search
    is not appropriate.

> It is not reasonable to claim that the GA does any better job of
> recreating ground truth as observed in the real-world Afghanistan than
> does the set of discrete, static tests employed in Approach 1.
> Instead, the claim is simply that, not knowing precisely how well
> these simulations reproduce what happened in the real world, we find
> an advantage within the GA implementation because the technique is
> independent of the arbitrary constraints that underlie the selection
> of discrete simulations tested in Approach 1. The GA, then, is subject
> to far fewer social theory assumptions than is either of the preceding
> approaches.
>
> While it is certainly possible that this flexibility is not required
> for this specific simulation, especially if there is some innate
> resilience within the simulation to change in light of varied
> parameter settings, the generality of the approach has merit.

2.  Even though the parameter values were unconstrained, as mentioned in
    the previous point, the results we achieved were consistent with the
    conclusions drawn in Approaches 1 and 2: the simulation performs
    most successfully when violence weight is low.

3.  Although the number of potential candidates (chromosomes) to
    evaluate is much higher with a GA than with either of the other two
    approaches described in this document, the confidence we have in the
    corroborating results among the three methods makes this increased
    computational effort worthwhile.

## Recommendations

Because of the properties listed above, this third approach--- in which
a rule-based agent system adapts to extend its own behaviors, and yet
can explore a more varied search space in a manner that produces
findings that are consistent with our spot-checking of discrete cases---
should allow us to conduct a more resilient investigation across the
parameter search space within social science simulations.

## Future Work for GA

Our work leads to the following conclusions and recommendations:

1.  NetLogo was initially selected for our simulation platform, because
    it is easy for noncomputer scientists to use effectively.
    Unfortunately, being single-threaded is a significant drawback.
    Though we were able to overcome this difficulty, we strongly
    recommend using an alternate, more parallel-friendly agent-based
    simulation platform.

2.  The top performers after 100 generations did not yield very
    significant correlation values. Many more of the chromosomes, even
    in the final population, produced negative correlations instead of
    positive ones. Combined with the previous analysis that showed more
    favorable simulation results when the violence weight is low, this
    suggests that the data we are using to calibrate/train the
    simulations may not be well suited for this task. It does *not*,
    however, reflect poorly on the calibration method itself.

3.  A larger number of chromosomes should be evaluated. The 2400 we
    tested were too few to feel confident that we have explored a
    significant portion of the search space. Significantly greater
    computing power will be required to do this.

Computing the correlation between mindshare and WITS incidents within
the NetLogo simulation, although convenient, was probably not the most
effective way to gauge performance. Instead, the database we have put in
place to capture NetLogo simulation data per tick should be used as a
standard platform for performing the interpolation and aggregation of
data required to assemble the correlation statistics. Unfortunately,
this requires much deeper integration between the components.

**Insights**

This task offered several insights and key lessons learned:

-   It is possible - even within a simulation that relies heavily on
    stochastic processes -- to evaluate multiple replications of
    discrete combinations of parameter values to identify optimal
    simulation settings and trends.

-   The simulation can exploit the underlying social models. This is a
    direct consequence of the equations embodying the social models,
    rather than an emergent behavior of the society of agents.

-   Adaptation can increase the verisimilitude of the running model in
    some cases.

-   The performed comparisons suggest that the third (genetic algorithm)
    approach to agent behavior performed the best: it explored the most
    granular search space, and produced results that were entirely
    consistent with earlier findings.

***Chapter 3: Real-Time Updating***

# Introduction

Simulations can be given several sets of alternative data, thereby
enabling what-if analysis. A sufficiently robust simulation might
actually be able to predict certain events, or learn to predict events
over time (though such a capability is well beyond the scope of the
initial project). However, the universe of potential outcomes must be
constrained by evolving real-world events and as such, is limited to the
universe of realistic options.

To incorporate these real-world events, a method was needed to collect
data, transform it to the simulation semantics, and insert it as an
event at the appropriate time in the simulation. For agent-based
simulations, this implies that each of the agents would need to be
stopped at the appropriate simulation time to allow for the insertion of
the event. The techniques for managing the execution of simulation
processes are well understood. We explain these techniques next and then
apply them to agent-based simulation. Later in the report, we review the
mechanisms available for harvesting external data and using them as
simulation events.

## Computer Simulation Systems

In a broad sense, we can divide computer-based simulation systems into
two general classes. The first class, which is frequently called
*analytic simulation*, intends the solution to a problem or experiment.
Distribution of the simulation processes is a means to address
scalability or performance issues of monolithic sequential simulations.
In agent-based simulations, the simulation processes correspond to
atomic units of composition. The natural decomposition of the simulation
into groups of agents is an effective means of distributing the
computation across processors. This, however, implies that the
simulation processors will need to coordinate their progress so that the
agents' processes are synchronized.

The second class of systems, which are called *interactive simulations*,
embraces virtual reality, training simulators, and computer games. As
one might expect, a major emphasis in distributed interactive simulation
(DIS) is on creating a compelling, realistic, or entertaining
distributed virtual environment (DVE) for human interaction. A
relatively recent topic in interactive simulation is the melding of
live, virtual, and constructive (LVC) objects into a blended (so-called
"seamless") environment.

The differences in the design priorities of the two classes of
simulation directly influenced the development of message-passing
protocols used to exchange data during a simulation execution. For
example, a natural result of the analytic simulation perspective was a
straightforward definition of correctness based on the answer provided
by a sequential simulation execution, such as is done in our NetLogo
simulations. Using this definition, regulating the delivery order of
simulation messages is an important primary concern. Indeed, NetLogo
implements a mechanism that ensures the agents stay in lock step (at the
same tick) at all times. In contrast, the development of distributed
virtual environments was not encumbered by such a strict notion of
computational correctness. The overriding performance objective was
providing real-time interaction for the users. It was convenient for
designers of these systems to largely ignore the ordering of messages in
favor of binding simulation time to the real-world (wall-clock) time.

### *Analytic Simulation*

The first concepts of distributed analytic simulation appear in
literature in the mid-1970s (Chandy and Misra 1978). The foundational
research by Chandy and Misra focused on a distributed systems
perspective of simulation (Chandy and Misra 1979). They presented
concepts that paralleled the traditional notions of correctness for
distributed computation. A fundamental test of a distributed computation
algorithm was comparison to a sequential computation. Thus, a
distributed simulation could be considered correct if its results were
equivalent to those produced by a sequential execution of the same
simulation. Synchronization techniques were designed to ensure
sequential equivalence.

While preliminary notions of distributed simulation were being
developed, additional models of message-passing distributed systems (not
necessarily simulations) were evolving. Partial-ordering concepts, such
as "happens-before" and "causality" (Lamport 1978), increased the
asynchrony possible in an execution by removing certain constraints on
delivery order. Using these concepts, several paradigms evolved as
methods for ordering messages during an execution. The term
*time-management* refers to general ways in which simulation processes
coordinate message delivery to provide a correctly ordered distributed
execution. Fujimoto provides an excellent overview of the evolution of
parallel and distributed simulation and explains the important classes
of time-management algorithms (Fujimoto 1990).

### *Interactive Simulation*

Quite apart from the development of analytic simulations, a different
form of distributed simulation was developed to enhance the quality of
human-computer interaction. Military efforts in distributed interactive
simulation were primarily a means of extending the usefulness of
training simulators. (More about the military role in the development of
distributed simulation is explained later.) Concurrent with the
military-sponsored training initiatives, the computer-graphics community
was extending the concepts of virtual reality to include multiple
participants. The first articles describing distributed virtual
environments appeared in the early 1990s, notably at the 1993 IEEE
Virtual Reality Annual Symposium. Several independent efforts pursued a
variety of architectural alternatives including client-server (Signh et
al. 1994, Funkhouser 1995), peer-to-peer (Carlsson and Hagsand 1993),
and independently replicated environment data (Macedonia at al. 1994).
Overviews of (1) early development and (2) various paradigms of
distributed virtual environments were provided by Stytz (1996) and
Macedonia (Macedonia and Zyda 1997), respectively.

In these forms of distributed simulation, the simulation processes
provide a means of real-time interaction with users (or user
applications). Consequently, suspending a simulation execution is
undesirable and frequently impossible. This highlights a fundamental
difference between analytic and interactive simulation. Whereas analytic
simulations may enforce strict message order constraints, many real-time
simulations do not synchronize their event processing at all, due to the
overhead required. Practically, this means that messages in real-time
distributed simulation are processed as soon as possible, in the order
received. We call this *receive-order* (RO) message processing.

### *Simulation Execution Models*

The differences between analytic and interactive simulation paradigms
can be seen in the models of execution of simulation processes. Table
3-1 shows simplified examples of processing loops for analytic
(time-managed) and real-time, interactive (time-stepped) simulations.
Two fundamental differences are apparent in the example process models:
(1) incoming message order and (2) wait condition. In time-managed
execution, the messages are delivered in *timestamp order* (TSO). In
time-stepped execution, incoming messages need not be delivered in any
particular order. It follows that, in time-managed execution, a process
must wait until the correct order of incoming messages can be
determined. A real-time, time-stepped execution, on the other hand,
simply waits until the wall-clock time has advanced to the next time
step (which we commonly refer to as ∆t).

The real-time, time-stepped execution model shown suffers from two
problems that are avoided in time-managed simulations. Since the sender
and receiver of a message proceed independently, messages can arrive
arbitrarily long after they are sent. Some protocols, therefore, include
mechanisms to compensate for latency in certain types of messages.
*Dead-reckoning*, for example, can be used to extrapolate the state of
an object from a late message.

A more significant problem in the real-time, time-stepped execution
model stems from the lack of a delivery order. During an execution,
messages received independently from two logical processes (LPs and
agents, for example) may convey information that is causally related.
Simply explained, a message A, sent by process 0, may have *caused* some
message B, sent by process 1. Without a delivery ordering mechanism, it
is possible for processes (other than 0 and 1) to receive messages in
either order AB or BA. The order represented by BA is called a
*causal-anomaly* and is a significant concern in the analysis of
distributed simulation systems.

The parallels between real-time, distributed simulation and the ACSES
architecture are important. In our system, we can treat the sources of
external data as a sort of independent, real-time subsystem. The order
of events in the real-world is fixed; however, the order that they are
received and processed by our agent-based simulation must be regulated
by our architecture.

## Sources of External Data 

Data for insertion into the simulation are available from several
interesting sources, e.g., online sources like RSS feeds, news wire
services, and even Google alerts. Generally, we hoped to use these
sources to inform us of new developments in the modeled area of
interest. Though the methods developed here are novel, connecting
real-world data to a running simulation system is not a new idea. In the
defense-related distributed simulation world, connecting simulations to
test ranges can provide additional complexity and realism to those
environments. The Test and Training Enabling Architecture (TENA)
provides standard methods for integrating many types of real-world
hardware, range equipment, and simulations. Figure 3-1 shows the logical
structure of a TENA system.

**Figure 3-1**. Logical structure of a TENA system.

![](media/image227.png){width="5.999305555555556in"
height="4.108333333333333in"}

In a paper on the use of data-driven simulation for social sciences,
Kennedy and Theodoropoulos suggested architecture for using agent-based
simulations connected to real-world sensors (Kennedy and Theodoropoulos
2005). This is shown below. They went on to suggest that the models of
the world within the simulation could be driven by these sensor agents;
however, they did not specify a methodology. The system they proposed
includes a feedback loop, of sorts, in that they also suggest the
possibility of "effectors" that could alter the state of the real-world
based upon some chosen course of action.

**Figure 3-2**. Data-driven agents

![](media/image228.png){width="5.777777777777778in" height="6.375in"}

Weather simulations provide another parallel to the ACSES program goals.
In the LEAD project, Plale and others (2005) describe a process for
updating forecasts based on simulations where the current conditions are
constantly changing.

**Figure 3-3**. A process for updating forecasts (Plale 2005)

![](media/image229.png){width="6.259027777777778in"
height="1.0944444444444446in"}

Figure 3-3 shows their process flow. Currently, computer models for
forecasting weather must be rerun with new data at each observation.
This process takes time and does not capitalize on the previous
computation.

**Figure 3-4**. Comparing models for updating forecasts

![](media/image230.png){width="5.999305555555556in"
height="5.732638888888889in"}

(Source http://www.esl.lsu.edu/quicklinks/hurricanes/2006/ERNESTO/)

### Moreover, a set of computer models may produce widely varying predictions, as is illustrated by the numerous models shown in Figure 3-4. The figure shows the independent predictions of a set of models using the same initial conditions. Even though some of these predictions ended up being more accurate than others, no effective way existed to incorporate the observed accuracy of a particular model into subsequent runs. This problem parallels our issues with ACSES predictions.

### *Autocoding of Free Text and Real-Time Event Generation*

Our presumption, in the study of insertion of real-time events, was that
the best sources for relevant data would be online sources. We assumed
that news feeds, Google alerts, and media websites would provide near
real-time information that was otherwise unavailable. We also understood
that other, nonpublic sources might be available, such as remote
sensors, command-and-control systems, and similar military-type inputs.

Although the format and meaning of non-public sources is fairly well
understood (typically using prescribed protocols for information
exchange), the content of public sources is much more difficult to deal
with. For this reason, we examined the state of the art in free text
processing.

So-called automatic coding methods are designed to take textual
descriptions of events and create equivalent event data, constrained to
a limited, precise vocabulary. Much of this work is familiar to the
social science community, as it has been used to analyze large sets of
historically significant events. We examined the Kansas Event Data
System (KEDS) and its successor, Textual Analysis By Augmented
Replacement Instructions (TABARI), both of which use an extended form of
World Event Interaction Survey (WEIS) encoding. The Integrated Data for
Event Analysis (IDEA) typology was also examined.

The purpose of autocoding is primarily to replace the labor-intensive
manual coding of data required for analysis. Effective autocoding,
therefore, is judged with respect to the time, effort and accuracy of a
similar job by a human. A good autocoding effort can be applied to vast
amounts of event data efficiently. The effort associated with autocoding
is nicely typified online by the KEDS team (excerpt from
<http://web.ku.edu/keds/index.html>):

**STEP 1: Locate and reformat a set of machine-readable texts**

**STEP 2: Develop the initial coding dictionaries**

**STEP 3: Fine-tune the dictionaries**

**STEP 4: Autocode the entire data set**

**STEP 5: Aggregate the data for statistical analysis**

The suggested steps in the KEDS process are significant to us in three
important ways. First, they presume the ability to reformat
machine-readable texts. For our systems, many such sources of text
exist. We would like to be able to concurrently exploit many sources of
information, but the methods for reformatting the various texts are not
the same. For example, the structure of a data feed from AP wire may not
be the same as from CNN. We would require an additional step to
reconcile the various formats. Moreover, some of the structure or style
of the different sources may produce different coding for the same
event.

Second, the autocoding process expects that dictionaries can be derived
for all the significant events in the system. This may be a reasonable
assertion for a limited, retrospective analysis. For a running,
continuous update, the dictionary would also need to be modified. For
example, simple changes to terminology, like "suicide-bomber" being
altered for a while to a less-precise "homicide-bomber," can create
ambiguity in the parsing of text. Practically, this means that a human
would have to scan each incoming text to ensure that the dictionary
accounted for the terms used and that the definitions were consistent.

Third and perhaps most importantly, step 4 and 5 imply that the proper
way to analyze and understand the success of the autocoding is in a
cumulative, statistical analysis. This makes sense, because the success
of autocoding is measured in aggregate and does not depend on successful
encoding of one or two very critical events. In our scenarios, the
correct encoding of certain critical, possibly unexpected events is key
to our success.

## Anticipated Benefit from Runtime Input

Keeping the execution of the simulation bound to real-world data has
clear analytic advantages. As in the examples for weather prediction, we
can use the data to ensure our simulation does not become obsolete.
Automating the process of incorporating new data would allow the
simulation to automatically re-run any execution results that were
invalidated by new events. This facility has several other possible
uses.

-   *What-if analysis* -- By implementing a general-purpose external
    data input we need not be tied to specific devices or sources of
    real-world information. We can, if we choose, create fictitious data
    to perform what-if analysis of the modeled system. By creating
    future events and inserting them into the running simulation, we
    can, for example, assess the impact of an assassination attempt, or
    the outcome of an election. We can also explore the impact of
    reordering events, such as withdrawal from different parts of a
    region.

-   *Validation* -- With a sufficiently robust simulation, we should be
    able to use certain observations about the population to verify that
    the simulation is progressing correctly. By measuring and comparing
    some attribute of the modeled population and the simulation
    population, we should be able to determine a metric of convergence
    or divergence between the two. It should be noted, however, that
    agent-based simulations of social systems can often exhibit
    dynamical properties that are not easily compared. \[See
    Gintis (2007) for a detailed explanation.\]

-   *Goal states for adaptive control* -- Another use of external data
    is as a goal state for a learning algorithm. By allowing the
    simulation to explore different states and transitions, the system
    may be able to discover one or more configurations that produce the
    observed event. Similar to hidden Markov modeling, this allows the
    system to reproduce the behavior seen in the real-world, while not
    being prejudiced toward one specific behavioral mode. The approach
    may help validate possible assertions concerning the governing
    dynamics of the real-world population.

-   *Surrogates for difficult models* -- Some objects or activities in
    the modeled system may be difficult to construct. Simulation of rare
    events and of complex interdependent behaviors is challenging and
    can be computationally intractable. Further, a system may have
    certain key actors, like leaders or government officials, whose
    actions cannot be predicted by a simulation, but whose impact is
    critical. In such circumstances, an external input can serve as a
    sort of script for that individual's behavior. By relating external
    events to that actor's agent, the agent can generate behaviors
    appropriately interleaved with other agents. The events can simply
    be played back, or can be used to trigger a state that will produce
    a different response to other agents. External events can be an
    effective way to augment agents with complex behavior, without
    computational costs.

# Problem Description and Conceptual Model

We began the task with an understanding of what would be required to
insert events into an executing agent-based simulation. The two basic
issues are synchronization and mapping. The *synchronization problem*
involves regulating the simulation execution such that the event can be
inserted at the right point in the computation. The *mapping problem*
deals with determining how an event, with explicit content and meaning
in the real-world, should manifest itself as a change to the state of
the simulation. In this section we describe these problems more
explicitly for our context and provide an abstraction for addressing
them.

## The Synchronization Problem 

All discrete event simulations have a synchronization model, used to
order the processing of events. In simple, monolithic simulations, this
may simply be a queue of events. In more complex simulations, the
progress of the simulation is related to an internal clock or counter.
The value of the clock is referred to as simulation time. To insert an
external event, we have to create a relationship between simulation time
and real-world time and create a device for processing the event at its
indicated time-stamp. In NetLogo, the only notion of time is the "tick"
or time step. The progress of a simulation is measured in these ticks.
Any number of logical operations can happen between ticks, and the
advancement of ticks can be controlled manually. The tick counter can
also be reset to zero. It seems natural to exploit the tick function in
addressing the synchronization problem in NetLogo.

Maintaining relationships between independent clocks is explored in
detail in Lamport (1978). In this situation, we need to do two things:
(1) create an equivalence between a tick value in the NetLogo simulation
and a real-world time and (2) create a method for relating the
advancement of ticks to real-world time. The first part creates an
"anchor time" in the real-world that can relate to a specific tick value
(initially zero, of course). Given this anchor, the second part is
simply a way of determining one clock value from the other. For equal
time steps, we can use a linear function to express this relationship:

wall-clock = anchor-time + tick \* timestep

With a constant time step, this relationship can be used throughout the
simulation. Unfortunately, it is frequently necessary to change the size
of a time step during a simulation. Simply modifying the value of time
step in the above equation will not work, because calculations have
already been performed at the previous time step and cannot be scaled.
Without additional changes, simply allowing the value of time step to
change would have the effect of an instantaneous change in time, forward
or backward. To solve this, one can reset the anchor time to the current
wall-clock time.

We created an extension that maps the tick to clock time by specifying a
scale factor. Further it provides a call that retrieves events relevant
to the current time step for processing.

**Figure 3-5.** Two-step transformation process

![](media/image231.png){width="5.395833333333333in"
height="4.091666666666667in"}

## The Mapping Problem 

Fundamentally, the need to insert external events implies that the
events were not modeled within the simulation, or that they did not
occur in the simulation execution. The challenge, therefore, is to
determine what effect those external events would have had on the
simulation state. In a sense, the event insertion mechanism is a
surrogate for models of phenomena. The problem become relating the
external description of the event to a change in the simulation state.
This is depicted in Figure 3-5.

The mapping problem is related to the study of linguistics, ontologies
and semiotics. Mapping from real-world semantics to simulation semantics
is a form of universal translation. General solutions to the mapping
problem are broadly understood to be impractical. Overly restrictive
approaches will not provide much utility either, because they would need
to be implemented for each type and source of data. Our approach,
therefore, attempts to allow arbitrary external data and semantics,
while easing the burden of custom code creation.

In determining an approach, the first observation we make is that it is
intractable to try to assess all possible state changes from all
possible events (or apprehensions of events). So, we assume that all of
the events that can possibly occur can be categorized into sets of
events for which the impact to the population will be about the same for
any member. This forms an important first step: mapping free or
unconstrained text to categorical data. Autocoding utilities, such as
KEDS and TABARI, perform this transformation. In those cases, the events
are transformed into WEIS codes. The approach we pursue here is not
restricted to any particular code set, and IDEA or arbitrary coding
would work just as well.

### *Channels*

The result of categorization is simply a vector of data values and a
real-world timestamp. We refer to this vector as a *channel*. A channel
delivers a specific class of event and is associated with a particular
source. In practice, it will be necessary to refer to a channel by some
means, so we might allow an arbitrary label. Thus we can define a
channel:

**channel** name att-list;

For example, a channel delivering events parsed from Reuter\'s newsfeed
parsed by TABARI would be

**channel** reuters weis-code subject direct-object goldstein-wgt;

### *Event Handlers*

This structured input format is the entry point for the second step of
the process, the event handler. Since the category data is not yet
related to the simulation semantics, we must allow the user to specify
how this data will change the simulation. This implies a choice of
mapping logic based on the channel and event type. By defining a mapping
function available to a channel together with the channel descriptor, we
have an attribute signature that can be used to select the appropriate
function. In essence, we can use the values of the event attributes to
determine the appropriate mapping logic.

The handlers themselves perform the heavy-lifting of the mapping
process. They must deal with both the categorical data and the
simulation data. We implemented a way of calling arbitrary simulation
code upon the processing of an event. This allowed the person
incorporating the external data source to express the needed changes in
the semantics of both the external data and the simulation models. The
method for doing this is an event handler, which specifies both the
source data to be used and the transformation processes for the events.

event event-label (\*\<attribute_matching_logic\>) {\<java_code\>}

For example, the code below specifies that an event with any value for
the first attribute (location) and with a value of 10 for the second
attribute (amount) will highlight the location of that event.

channel stressBombs location amount;

event psychologicalStress (\*, 10) {

sim.highlight( realtime.geocode.MapManager.geoCodeX ( event.location ) ,
realtime.geocode.MapManager.geoCodeY ( event.location ))

}

### *Transforms*

In defining the mapping process, it was apparent that some
transformations would appear frequently. Notice the geocode example
above. To accommodate arbitrary transforms without the need to recompile
NetLogo, we added the notion of a transformation as part of the mapping
logic. A transform is a function that performs a mapping between a
symbol in an event to a construct in the semantics of the simulation
itself (in NetLogo, a set of agents or patches). For example, Address()
would be a transform that mapped \"Golden Gate Bridge\" to (x,y) in
simulation space, while Near() would transform \"Golden Gate Bridge\"
into a set of agents. These are intended to be primitives, and therefore
transforms are simply signatures that map via native calls to the
running simulation.

**transform** name event-attribute list **to** simulation-type **with**
native-call

A transform that would be useful for the above example might be

**transform** Address subject direct-object **to** location **with**
realTime.googleAddressLookup;

Simulation-type can be one of the following:

-   agent

-   agents

-   location

-   locations

Transforms can return a null value if the particular set of symbols does
not map to an event. By default, a transform is assumed to map to all
possible values of an attribute it is concerned with. For example,
Address maps to \"Golden Gate Bridge\" but could also map to \"Joe
Random.\" An event that handles \"collapsed\" for a bridge probably will
not have the desired behavior for the event \"Joe Random collapsed at
work today.\"

transform Address subject, direct-object in \["San Fransisco", "Golden
Gate Bridge", "Bay Area"\] to location with realTime.staticPlaceMap;

# Implementation and Examples

Our implementation allows NetLogo models to accept synchronized,
external inputs from a variety of sources. We have included extensions
to NetLogo that implement time keeping, facilitate geocoding of
locations to latitude-longitude (lat-long) coordinates, and prescribe
external data sources and transformations for event data. Figure 3-6
shows the components of our extensions. The timer or wall-clock
extension allows a user to specify a relationship between ticks and the
cpu clock. Geocode uses the Google maps API to do easy transformation of
location names to lat-long coordinates. It communicates directly with
google.com and therefore requires internet connectivity for geocoding.

**Figure 3-6**. Notional geocoding construct

![](media/image232.png){width="5.990277777777778in"
height="3.5097222222222224in"}

Mapping data and inserting the events are performed by the simframe
extension. This extension allows users to register channels with the
simulation, as sources of external data. These sources may be simple
files, or they may be URLs, indicating a remote data source. Simple
Object Access Protocol (SOAP) requests, a web standard, are used to get
events from the external data source. In both cases the event data must
be preformatted and include a timestamp. The format expected by simframe
is compatible with the output of TABARI. An example of an event file is
shown below.

\# DATE ( LOCATION, TYPE, IMPACT )

01192004 (\"Kandahar,afghanistan\",\"massacre\",73)

01272004 (\"Kabul,afghanistan\",\"bombing\",14)

06022004 (\"Badghis,afghanistan\",\"massacre\",5)

06072004 (\"Logar,afghanistan\",\"assassination\",1)

06072004 (\"Khowst,afghanistan\",\"bombing\",4)

06102004 (\"Konduz,afghanistan\",\"massacre\",16)

03272004 (\"Kabul,afghanistan\",\"massacre\",4)

01122004 (\"Mazar-e Sharif,afghanistan\",\"massacre\",1)

01112004 (\"Paktia,afghanistan\",\"traffic accident\"1)

01052004 (\"Kandahar,afghanistan\",\"bomb threat\",0)

01042004 (\"Kabul,afghanistan\",\"false alarm\",0)

02022004 (\"Khowst,afghanistan\",\"bomb threat\",0)

01192004 (\"Badakhshan,afghanistan\",\"massacre\",5)

01242004 (\"Jalalabad,afghanistan\",\"false alarm\",0)

Example event file

An example SOAP event server is also provided to illustrate how easy it
can be to set up an external data source for simframe. In the example,
the SOAP server simply reads a file and provides the events, but it
should be clear that the file data could be replaced with any data
available on that server. Thus, instrumenting a sensor or remote website
to provide data feeds is a straightforward job.

The channel descriptor file, in addition to containing the source
information, also includes the logic for selecting a transformation and
performing the update to the simulation.

event-attributes location eventtype eventId

event-types string string numeric

channel http://localhost:8080/soap/servlet/rpcrouter 1

transform numeric realtime.simframe.geoCodeLon ( string )

transform numeric realtime.geocode.MapManager.geoCodeLat ( string )

transform numeric realtime.geocode.MapManager.geoCodeX ( string )

transform numeric realtime.geocode.MapManager.geoCodeY ( string )

event psychologicalStress (\*, 0) {

sim.highlight( realtime.geocode.MapManager.geoCodeX ( event.location ) ,
realtime.geocode.MapManager.geoCodeY ( event.location ))

}

event terror ("bombing", \*) {

sim.set_WITS_data(realtime.geocode.MapManager.geoCodeX ( event.location
) , realtime.geocode.MapManager.geoCodeY ( event.location
),event.eventId)

}

Example channel descriptor file

Descriptions of the example NetLogo simulations are provided in Appendix
7. Each simulation includes an explanation of the function of the
extension it demonstrates, as well as instructions on how to use it in a
more complex simulation.

## Autocoding and External Data Sources

Our insights regarding autocoding are that the technology requires a
robust and temporally expressive ontology of events and the ability to
characterize and extract the relevant information in computationally
tractable times. Moreover, problems of reconciling and collating events
and capturing multiple perspectives of a single event is difficult.

To handle the volume of documents that could explain relevant events,
documents and cable documents and knowledge discovery tools are rapidly
evolving. Currently employed systems include the PACOM deployed
ORNL-developed Piranha system, although other systems are also in use.

Piranha was first installed at PACOM's Virtual Information Center in
1997. More recently, ORNL has worked with PACOM to improve the
performance of Piranha by utilizing parallel processing in 2005-- 2006
to address the document volume issue illustrated within this chapter.
Piranha was subsequently installed at multiple intelligence center (IC)
locations. Piranha has been developed and trained to search and retrieve
real-time information from sources such as news agencies and the
Internet. It looks at thousands to millions of documents and in a matter
of minutes selects the 50 to 100 most relevant documents for in-depth
analysis.

The real-time streams of data to the ACSES system may be of similar
size, and relevant events must be quickly selected for insertion into
the simulation. Piranha can be deployed to retrieve real-time
information, de-duplicate, and screen data, and an
information-extraction software component can be inserted to extract
events from the data produced by Piranha. These events can be
incorporated into the ACSES simulation as real-time updates. If analysts
are to manually capture these events, this knowledge discovery approach
is crucial to making real-time updating feasible.

Any negative insights about autocoding are more than offset by our
discovery of the wealth of this type of coded data already available.
Several government and nongovernment agencies provide large data sets of
world events, frequently geocoded to the region or city. A few online
sources can be tapped in real-time. Notable among these is
[http://www.globalincidentmap.com](http://www.globalincidentmap.com/), a
private endeavor that continuously monitors and reports on various types
of events.

## Mechanics of Event Insertion

The intermittent nature of important events necessitates a certain
execution model. This is commonly known as optimistic execution. The
utility of the online analysis is driven by the ability to predict. This
means executing into the future. The arrival of a new event (in the
present) will require the simulation to be rerun from the point of the
new event forward. If we require the exploration of many alternative
futures (an important feature, of course), then the simulation system
will need to manage many states, separate threads of execution, and
outcomes. The management of this type of predictive simulation
capability is beyond the scope of current analytic simulation engines.

## Specifying Affected Agents

We have realized that the agents affected by an event can be specified
in two important ways: (1) by a geographic region and (2) by
communication-connection pattern. For geographic impact, the simple
solution is to place an effect at a position and let the agents sense
them based upon their distance. For more sophisticated propagation, we
must determine a way to select agents on the basis of their connections
to media or to each other, then meter the impact taking into
consideration the characteristics of the connection network.

## Future Work

The primary remaining work is to investigate and tune the algorithms for
affecting the agents for each type of event. Our current implementation
simply "irritates" all agents in the specified region. A better model
will discriminate on the basis of agent alignment, the cause of the
event, and its nature. We are hopeful that events like those provided by
the Worldwide Incidents Tracking System (WITS) or the GlobalIncidentMap
can be used to show realistic changes to the motivation and subsequent
behavior of a population.

We also hope to explore more sophisticated analytical techniques
suggested by the need to insert events and rerun simulations. Because
this will produce a large space of possible futures, it is important to
be able to efficiently process and analyze the data, gleaning insights
quickly as the situations change.

## Insights

In this chapter, we detailed the design and implementation of a method
for insertion of external data into a running simulation. Our
contribution to the state of the art is most significant in our
generation of code to support arbitrary online sources of data. We have
implemented a web-services-based method for creating and connecting to
any data source that can be converted to appropriate categorized events.
This means that we can potentially tap into any available online feed,
through either source or local conversion. Additionally, of course,
we've augmented the NetLogo toolset and proved the viability of our
approach.

The most interesting problem presented by the work to date is the
efficient simulation of vast alternative spaces, updated in real-time.
To *efficiently* manage this type of computation, one needs to address
resource management of memory, processing power, disk space, network
bandwidth, and more. To do this *effectively*, the simulation must also
be analyzed in real-time to provide clues concerning the relative
importance or redundancy of a particular vignette. This places new and
interesting requirements upon high-performance computing applications.

To handle the volume of documents that could explain relevant events,
documents and cables and knowledge discovery tools are crucial to allow
feasible real time updating. Currently employed systems can be deployed
to retrieve real-time information, de-duplicate, and screen data, and an
information-extraction software component can be inserted to extract
events from the data produced by Piranha. These events can be
incorporated into the ACSES simulation as real-time updates.

Other challenges suggested by this work include the analysis of
temporally indeterminate Kripke structures (a way of expressing a
computation as a state diagram, modified to include equivalence
relationships, and critical attributes for disambiguation of states) and
exploring the viability of generating complex adaptive system (CAS)
covering spaces (for example, to determine how many runs of a simulation
are needed to have confidence that the possibilities are understood).

***Chapter 4: Accounting for Surprise***

# Introduction

The research plan for the ACSES project is provided in Appendix 1 --
Research Plan. A detailed description of our methodology for training
the agents is provided in Chapter 2. Briefly, we evaluated (1)
real-world data sources, their quality and availability (Chapter 1); (2)
a distributed GA for training the simulation (calibrating) based on a
subset of this data in static form (Chapter 2); and (3) the feasibility
and technical challenges for utilizing real-time (nonstatic) data in the
simulation (Chapter 3). For a detailed description of the NetLogo CAS
simulation, please refer to Chapter 2: The NetLogo Simulation.

This chapter will document our efforts to identify and explain surprise
in the CAS simulation of Afghanistan. We also explore causal factors for
these behaviors through a partial-order temporal analysis of the state
space of our model. Appendix 8 reviews methods and issues surrounding
the construction of plain-text narratives from event-level simulation
data. Appendix 9 details the extended toolset we have created for more
direct and intuitive manipulation and analysis of the NetLogo model.

# Surprise: An Overview

The purpose of a CAS simulation is necessarily two-fold: to explain the
causal reasons for known significant events and to predict unknown
significant events. In the first case, we must know the environment and
the events in order to hypothesize the causes; in the second case, we
create a likely or interesting environment, and then we use the
hypothesized causes to see what will happen.

Many of the underlying causes in a CAS are things that, by their nature,
cannot be readily measured. So we create agents that have certain
properties--- both attributes and rules--- and then we calibrate the
attribute values and the rules so that the outputs of the system match
the macro-level events that we can measure. These refined attribute
values and rules become our hypothesis to be used in the prediction
stage.

The surprise is also twofold: if we are successful, we have deduced the
underlying causes of the events, which we did not know previously;
furthermore, given these underlying causes, we can find out what will
happen in a different environment, either under new conditions or during
simulated courses of action. The key element in "surprise" is that we
have measurable, macro-level events that are not explicitly programmed,
but rather occur implicitly. These events are therefore defined by the
continuous interaction of all the attributes and values at the agent
level. It is this agent-level interaction that produces the emergent,
macro-level behavior we term "surprise."

In our model of Afghanistan, we have created attribute values for each
agent that define such things as support for leaders, economic
incentives, influence from peers, and reactions to violence. All of
these factors, and others, combine together in a utility function that
each agent runs for itself, thereby choosing which behavior seems to be
best suited for that agent, under the terms we have defined. The choices
are individual ones, but they also provide feedback for other agents,
and for the environment. This feedback then becomes another factor in
the next round of agent decisions.

In the following sections we link this description of surprise to
specific examples taken from our experiments with the Afghanistan model,
as implemented in the NetLogo environment. The first section describes
the macro-level agent behavior of clustering and explores some possible
causal determinates for this behavior, as it is not explicitly
programmed into the model. This clustering is representative of *social
behavior emergence*; that is, it is a self-organizing phenomenon that is
a consequence of the agents' individual actions and changes in state as
they react to other agents and to the environment. These threshold
responses are measured against different levels of external stimuli, as
represented by discrete input parameters for various scenario modeling.
Again, the thresholds that are evident in these results are not
explicitly accounted for in the set of rules that govern the agents'
behavior.

We then move beyond identifying and explaining surprising behavior. That
is not to say that the results from this section do not also represent
emergent behavior; they do. But here we present the prediction phase of
the experimental design. As mentioned previously, once we understand the
causal relationships and have satisfactorily calibrated and validated
the model, we can use that understanding to discover what will happen
under certain conditions. As such, in this section we describe a set of
simulation runs that are aimed at evaluating specific courses of action
(COAs) that would be relevant for a commander and his or her area of
responsibility---in this case, the Ghazni region.

# Agent Clustering

Our model of Afghanistan has two types of agents: civilians and
fighters. Although the civilian agents are further differentiated by
their choice of affiliation (i.e., whether they are pro-government,
neutral, or pro-Taliban), they are essentially the same type of agent
across all three categories. Conversely, the fighter agents have
fundamentally different attribute-values, depending on which group they
belong to, and they cannot move between groups. These three groups are
Taliban forces, Afghan government forces (AGF), and coalition forces
(CF). The Taliban forces make up the "Taliban" faction; the CF and AGF
collectively make up the "soldier" faction. These fighter agents can
choose at each time step whether to fight, move, stay (on the current
patch), or flee. They make this action choice based on their perception
of their own effectiveness relative to the local *perceived*
effectiveness of the opposing force.

These simple rules produce effects in the system that are not explicitly
accounted for, but that occur as a natural and predictable consequence
of these rules. One such effect is that both the soldiers and the
Taliban will begin to group together. The fighter agents are more likely
to survive in large groups. Hence, larger groups will emerge in the
system. No rule explicitly tells the fighter agents to group together;
it just happens.

Below are images and statistics from a single simulation run. The brown
dots represent Taliban agents, the green dots are soldier agents, and
the other colors represent the civilians---blue for civilians who choose
to be pro-government, yellow for those who remain neutral, and red for
those who are pro-Taliban. The first image \[Figure 4-1(a)\] is from the
initial setup for the run. The civilians are placed according to
demographic information on Afghanistan, while the soldiers and Taliban
(1500 agents each) are allocated across Afghanistan randomly. Notice
that the placement or grouping of the green or brown agents follows no
discernable pattern.

**Figure 4-1**. Simulation results at (a) initialization, (b) 50 time
steps, (c) 100 time steps, (d) 150 time steps, and (e) 200 time steps

\(a\)

![](media/image233.png){width="4.4006944444444445in"
height="4.566666666666666in"}

\(b\) (c)

![](media/image234.png){width="3.0in" height="3.1131944444444444in"}

![](media/image235.png){width="2.990972222222222in"
height="3.1131944444444444in"}

\(d\) (e)

![](media/image236.png){width="3.0in" height="3.1131944444444444in"}

After 50 time steps in the simulation, some patterns begin to emerge. Of
course, many of the Taliban and, to a lesser extent, the soldiers have
disappeared from the simulation. This die-off of agents occurs in the
early stages of the simulation run as, presumably, many of the weak,
isolated agents have been killed. The Taliban do not have the same force
strength or success rates as the CF agents. Thus, their die rate is more
pronounced. Note that in this simulation run, 50 additional Taliban
agents are injected into the system randomly at each step, while 7 CF
agents and 8 AGF agents are also injected. This is done to preserve a
level of stability in the number of these agents, which facilitates the
study of emergent patterns during the course of the simulations.

After 100 steps, we can see a continuation of the same patterns from 50
time steps. The number of each faction has somewhat stabilized, meaning
the number of new agents injected in each step approximately matches the
number of agents killed in each step. At this time step, 515 Taliban
agents and 778 combined soldier forces (CF and AGF agents) remain, down
from an initial 1500 each.

At time steps 150 and 200, we note that areas of particular stronghold
have emerged for both factions, particularly for the Taliban. At time
step 150, the injection of new agents was turned off for both factions,
to see the continuing effects with no new agents placed randomly across
Afghanistan. In the end, 478 Taliban and 631 soldiers remain, with the
majority of the latter (442) being the more powerful CF agents.

As we can see, the Taliban agents dominate mainly in the Eastern-Central
area, but they also control the region that extends northwest from
there, up to the northern border of Afghanistan.

During five additional independent simulation runs, the same simulation
parameters were used, but the simulation was extended over a few hundred
more time steps. Furthermore, the infusion of new agents was manipulated
so that the number of each faction at the end of the simulations would
approximately equal the initial settings, i.e., 1500 for each faction.
Images of the results are shown in Figure 4-2.

**Figure 4-2**. Simulation results from (a) Run 1, (b) Run 2, (c) Run 3,
(d) Run 4, and (e) Run 5

(a)![](media/image238.png){width="2.7930555555555556in"
height="4.247916666666667in"}

\(c\)

\(b\)

![](media/image239.png){width="2.7916666666666665in"
height="4.239583333333333in"}

\(d\)

![](media/image241.png){width="2.7215277777777778in"
height="4.121527777777778in"}

\(e\)

![](media/image242.png){width="2.7131944444444445in"
height="4.1305555555555555in"}

Now we can see more clearly that the Taliban seems to always dominate
the Eastern-Central portion of the map, but sometimes the Taliban's
control extends northwest from there, and sometimes it extends
southwest. In Figure 4-2(a) and, to a much lesser extent, in Figure
4-2(e), the Taliban presence seems to form two distinct groups rather
than a single large group. Nevertheless, these runs support the theory
that the Taliban agents will tend to cluster together to increase their
chance of survival, a trait that is not explicitly programmed into the
simulation.

The process of analyzing these results is like that for any similar
simulation. Because many of the effects that we see are not explicitly
programmed, we must explore the results and try to determine causality,
which will hopefully give us insight into the real situation that we
have attempted to model.

For example, we can clearly see that in all five runs above, the
Eastern-Central area is always dominated by the Taliban. Two
hypothesized reasons for this effect are (1) this area tends to have the
highest population density and (2) this area tends to have the most
Taliban support among the civilians. In terms of population, further
investigation shows that a small but distinct correlation exists between
population size and the number of Taliban agents (Pearson correlation =
0.227, significant at the 0.01 level). Moreover, all 15 of the
highest-density patches have some Taliban agents present (anywhere from
3 to 20 per patch) and no soldier agents. In the 60 highest-density
patches, only 12 have any soldier presence at all, with two of those
cases showing that the Taliban still outnumber the soldiers. We
speculate that the difference in perception ability between the Taliban
and the soldiers (particularly the soldiers who are CF) is the main
influence for this trend. Because the soldiers tend to wrongly identify
civilians as Taliban, they perceive a larger Taliban force than really
exists in the high-density areas, simply because these areas have more
civilians. Therefore, the soldiers will decline to engage whatever
Taliban are there, allowing the Taliban to grow stronger in these
high-population areas.

In terms of Taliban support among the population, we can clearly see
\[Figure 4-2(a)\] that even early on, high-density areas are also areas
of greatest density for Taliban helpers (in red). The role this plays is
difficult to measure, however, because a feedback mechanism is at work
here. Clearly, as the number of Taliban increases, the number of Taliban
helpers also increases. The Taliban helpers number the lowest in Figure
4-2(a) (2157) and Figure 4-2(c) (2041). The other three runs have 3401,
3119, and 2868 Taliban helper agents \[Figures 4-2 (b), (d), and (e),
respectively\]. Notice also that across all five simulation runs,
Taliban helper agents seldom reside in places that do not have a Taliban
presence.

Remember that all five of these runs have the same number of Taliban and
soldiers (within +/- 5), yet the relative positions of Taliban and
soldiers within Afghanistan can create a range of 1500 or so among the
Taliban helper agents (assuming that the disposition of these forces is
the main causal factor in the civilians' choices; all other settings
remain the same, but stochastic processes may account for at least some
of the variation between runs).

# Threshold Response

In this section we explore what effects different surge levels of
coalition forces in the Ghazni region would have on the simulation,
particularly how it would influence the number of soldier helpers. We
ran this experiment with one set of settings from the experimental
design changing only the size of the surge. Those setting are: Time
Scale = 3; Social Theory = Representative; Influence = On; Repression =
Off. Also, the number of CF and AGF at startup was 500 each, and the
number of Taliban agents was set to 1000. To match our experimental
design, we concentrated on the Ghazni region, i.e., the surge takes
place in Ghazni. No agents were injected at each time step, so the
Ghazni region had only its share of the initial soldier forces
supplemented by the subsequent surge of coalition forces.

We started with a surge level of 40 and increased that by 10, up to 110.
Each of these surge levels was run 60 times. We also ran three different
mid-range surge levels per interval, 30 times each (i.e., we ran surge
levels of 42, 45, 48 and 52, 55, 58, etc., up to 102, 105, and 108). The
complex interactions and stochastic mechanisms inherent in the system
produce a wide variance within each set of runs, and conclusions are
thus difficult to ascertain. However, some trends do emerge as we graph
the analysis.

We defined a surge as successful if the number of soldier helpers at the
end of the run was greater than the number of soldiers. Figure 4-3 is a
plot of the number of runs that met the criterion for a successful
surge. In this graph, the count results from runs of 30 repetitions are
doubled to normalize with results from runs of 60 repetitions.

**Figure 4-3**. Results of surge experiment

![](media/image243.png){width="4.749305555555556in"
height="3.7993055555555557in"}

According to our results, when the surge level reaches approximately 70
soldiers, the number of successes increases dramatically. With a few
exceptions, when the surge is less than 70, the number of successes
tends to be around 10 to 15 out of every 60 runs; when the surge level
is greater than 70, then the success rate tends to be 25 to 30 out of
every 60 runs. Some threshold is apparently reached at the 70-soldier
surge level that allows the system to greatly increase the success rate.

This is not the only threshold-type response we can see with this data.
When one considers all the main surge values---40, 50, 60, 70, 80, 90,
100, and 110---in terms of how many soldier helpers exist at the end of
each run, we can construct a histogram for each surge level and compare
the results across the levels (Figure 4-4).

**Figure 4-4**. Results as a function of run history

![](media/image244.png){width="4.698611111111111in"
height="4.698611111111111in"}

At a surge level of 40, most of the simulation runs result in somewhere
between 0 and 40 soldier helpers in the Ghazni region, with very few
runs resulting in more than 40 soldier helpers. The same is generally
true for surge levels of 50 and 60. At surge levels of 70 and 80, far
fewer runs end up with the number of soldier helpers in the 0--40 range,
and more runs fall in the 41--80 range. We can also see an increase in
runs that result in 120--300 soldier helpers. Finally, on the back left
wall of the 3D histogram, we see that the highest surge levels usually
end up with either a very low number of soldier helpers or a very high
number of soldier helpers; rarely, do these surge levels result in a
number of soldier helpers in the middle ranges.

In general, we can see that the trend pushes toward a greater and
greater degree of separation between success and failure. At the higher
surge levels, this threshold is more pronounced, resulting in either a
low or a high number of soldier helpers, but rarely somewhere in
between.

# Evaluating Command Options for Ghazni in 2006

Another set of experiments was used to evaluate COAs for the Ghazni
region. For this set of simulation runs, we used the following settings
that resulted from the calibration and validation procedures (using 2007
and 2005 data respectively) as outlined in Chapter 2.

**Table 4-1**. Experimental settings , Set 1

  ------------------------------------------ ----------------------------
  Parameter                                  Setting

  influence                                  off

  social theory                              legitimacy

  surge                                      0

  time scale                                 3
  ------------------------------------------ ----------------------------

Repression was also turned on, not because it was significant, but
because it had to have a setting, and turning repression on rather than
off afforded a marginal (if not statistically significant) gain. We ran
60 replications for each of six combinations of the following settings:

**Table 4-2**. Experimental settings, Set 2

  ------------------------------------------ ----------------------------
  Parameter                                  Setting

  Economics                                  Off/On

  Surge Level                                0/200/400
  ------------------------------------------ ----------------------------

The economics parameter is a refinement to the model that allows for
nonmilitary intervention into the region. With economics on, the agent
value for E+ is changed from 300 to 1000, such that each agent in Ghazni
will get an additional economic benefit for choosing a pro-government
stance. The surge level parameter works as before, injecting new CF
agents into Ghazni at time step 37. As this experiment is aimed at
predicting the outcomes for these COAs during 2006, we used the
following assassination data, taken from the WITS information for the
Ghazni region:

-   3/18/2006 - 1 non-official public figure and 4 civilians killed in
    armed attack by suspected Taliban in Ghazni, Ghazni Province

-   5/21/2006 - 1 non official public figure kidnapped and killed, 1
    civilian kidnapped by Taliban in Ghazni, Ghazni

-   5/25/2006 1 cleric kidnapped and killed in armed attack by suspected
    Taliban in Andar, Ghazni

-   6/15/2006 1 government Official kidnapped and killed, 2 civilians
    kidnapped by suspected Taliban in Qareh Bagh, Ghazni

-   7/05/2006 - 1 government employee kidnapped by Taliban in Ghazni,
    Ghazni

The following table shows the time-step translation for these dates,
using the time-scale of 3 days per simulation time step:

**Table 4-3**. Time-step translation

  ---------- --------- --------- --------- --------- --------- -------- ---------
             3/18/06   5/21/06   5/25/06   6/15/06   7/05/06   Surge    End

  Time step  1         22        23        30        37        37       62
  ---------- --------- --------- --------- --------- --------- -------- ---------

The resulting mindshare for these experiments is summarized in Figure
4-5, with the bars representing the number of runs (out of 60) that had
a resulting mindshare for the Ghazni region within the indicated ranges:

**Figure** **4-5**. Resulting Ghazni mindshare (a) without surge, (b)
with surge set at 200, (c) with surge set at 400, (d) with economics
turned off, and (e) with economics turned on

\(a\)

![](media/image245.wmf){width="4.247916666666667in" height="3.15625in"}

\(b\)

![](media/image246.wmf){width="4.121527777777778in"
height="3.0541666666666667in"}

\(c\)

![](media/image247.wmf){width="4.247916666666667in"
height="3.1444444444444444in"}

\(d\)

![](media/image248.wmf){width="4.497916666666667in"
height="3.245833333333333in"}

\(e\)

![](media/image249.wmf){width="4.876388888888889in" height="3.46875in"}

These experimental results show a significant correlation between (1)
mindshare and surge level, and (2) mindshare and economics. Upon closer
examination of these results, we can form two preliminary conclusions
regarding our two COA parameters. First, even though a surge---as
expected---does improve mindshare in the Ghazni region, we note that
there are diminishing returns for surge levels. That is, the improvement
in mindshare for the first 200 CF agents is more than the subsequent
improvement in mindshare for the second 200 CF agents. (This holds true
for both cases, i.e., whether economics is turned on or off.)

The second conclusion indicated by the data is that neither the economic
incentives nor the troop surges are, by themselves, enough to guarantee
that all 60 runs will result in an average mindshare for the Ghazni
region that is above zero. In fact, only the combination of economics
turned on and a troop surge of 400 is enough to meet this criterion.

Because the model does not currently include information about the
*cost* of different interventions, it is not yet possible to make any
cost-optimized strategy recommendations; the model will always favor
sending in more troops and investing more money in Afghanistan. So while
the base recommendation is that the United States is best advised to
invest economically and militarily in Afghanistan, these recommendations
are not tempered by the cost of the interventions and must be weighed
accordingly. The general template of evaluating COAs using a simulation
grounded in social science theory, however, remains in place as a
valuable tool.

# Insights

Within Chapter 4, we demonstrated the feasibility of explaining and
predicting significant events in a complex environment, utilizing a
social simulation such that command responses can be reasonably assessed
in terms of their effectiveness for positively affecting outcomes. We
have illustrated this by creating a CAS (complex adaptive system) model
of Afghanistan on the order of \~10,000 agents, each of which has its
own rules and attributes that take into account social, economic, and
military factors at the local level. Through the experiments detailed in
this chapter we have identified two distinct types of surprising, or
emergent, behavior, and we have used those results as a basis for
evaluating various COAs. Appendix 8 discusses some of the issues
involved in generating a natural-language narrative of these evaluations
for presentation to nonexpert consumers of the model outputs. Appendix 9
demonstrates visual-analytic tools that not only allow for more direct
manipulation of the model, but also help to extract and aggregate
various outputs of the system for identifying causal relationships of
the emergent behaviors. Finally, we have produced a distributive GA
(genetic algorithm) that allows an automotive process for training the
system based on real-world data.

In conjunction with the real-time update capability and automated
calibration through a distributed GA (Chapter 3 and Chapter 2,
respectively), this framework demonstrates the ability to create a
realistic simulation that allows for testing and implementation of these
social theories. The CAS model has demonstrated the emergent properties
that constitute "surprise:" aggregate, nonlinear events that can be
validated using data as identified in Chapter 1. Furthermore, we have
created the visual-analytic tools to explore the state space of the
model, which greatly facilitate identifying causal factors inherent in
these surprising events, and we subsequently used these factors to make
COA predictions for 2006 in the Ghazni region.

***Chapter 5: Required Computational Complexity***

**Introduction**

The objective of Chapter 5 is to illustrate the feasibility of operating
such a complex agent-based simulation system that can generate courses
of action in computational times much less than 24 hours. The approach
to this task included

-   estimating the maximum hypothetical complexity of the agents that
    provides for each dimension of the HSBC, incorporating elements of
    modern social theory;

-   characterizing the maximum complexity of the environment that allows
    for real-time updating of the environment with unfolding events;

-   developing literature examples of analogous systems of comparable
    complexity;

-   comparing employed computational resources to those currently
    available with similar run times for case studies.

Disaggregated models that also track the motion of individual social
atoms are computationally intensive. The complexity of moving resources
and people is compounded by increasing granularity in data that allows
inclusion of finer behavioral dynamics, thereby increasing the
computational intensity. Given that dynamic planning and operational
support require large-scale simulations within narrow time windows,
reducing execution times is the most critical challenge and can be met
only with significant computing resources beyond desktop environments.
High-performance computing is, therefore, a necessary enabler for
simulations that generate multiple courses of action for a large social
region.

Testing the 31-million-agent model of Afghanistan's population
demonstrated the ability to computationally control and analytically
manipulate a system with the large number of agents (i.e., 10^8^)
necessary to model regions at the individual level. Simulations in
computationally tractable times can be performed with computational
costs that range from less than a dollar for simulations with simple
interactions to a few hundred dollars for the largest and most complex
simulations.

The current generation of societal modeling and simulation uses existing
architectures that are based on linking independently developed models
to predict effects resulting from specific triggering events. These
systems are characterized by the following attributes:

-   They are modular systems with each module (model) optimizing one or
    two elements of the HSBC domain. Multiple models are then linked
    together to cover the HSBC spectrum. The degree of aggregation in
    the models does not permit sensitivity analysis with respect to the
    causes of surprising changes in human behavioral dynamics without
    prohibitive computational power or analytical time.

-   Current agent-based models allow the agents to interact within their
    environment without constraints. The models generate a large number
    of potential futures but fail to quantify the probability of any
    single stream of future events. They incorporate little or no modern
    social scientific theory or analysis to adjust the rule base to
    mimic real-world observations. To generate realistic behaviors
    consistent with real-world observations, the entire range of HSBC
    theories must be leveraged to inform the simulation rule sets.

-   These systems employ a flexible architecture to combine models from
    a variety of constituencies, thereby permitting a community (M&S
    community, alliance community, interagency coordination and
    coalitions) to jointly experiment and understand intracommunity
    interactions. However, current models and simulations provide no
    capability for real-time or near real-time data updates as
    surprising events unfold or rapid changes occur in societal
    dynamics.

-   These systems cannot conduct a sensitivity study of key causal
    effects of an observed change in a region's stability in reasonable
    computing times. They provide only limited capability for analysts
    to select streams of future events important for the question at
    hand. Current systems are difficult for analysts to intuitively
    comprehend and easily control.

Many recent advances permit us to consider the next generation of
modeling and simulation systems that can support the ability to
anticipate adverse human behavioral developments and that can evaluate
alternative courses of action to mitigate the impact of these
developments on U.S. national security interests. They include

-   new approaches to multi-agent systems that allow us to envision
    goal-oriented agents and event responses that span the HSBC domain,
    as well as modern frameworks and architectures that support models
    using agents that obey rule sets informed by modern social theory;

-   advances in high-performance computing (HPC) resources that can
    assist computationally intensive cases when large numbers of agents
    are required to adequately describe a regional stability and when
    aggregation obscures causal factors;

-   emergence of architectures that can handle 10^8^ agents,
    disaggregated to the level of individuals behaving according to
    modern social theories within community scales important to regional
    stability, while still providing useful predictions in
    computationally tractable times.

This research thrust has as its central goal the ability to simulate
population behavior in a way that anticipates adverse future
developments and supports alternative course of action assessments to
mitigate them. The objective of the proposed research is to develop the
next generation of social simulations that integrates advances in social
science theory and computational sciences. This new generation of
societal simulation will be characterized by the following attributes:

-   These systems will be independent of the linkage of separate models
    (i.e., a political model linked to an economic model, linked to an
    infrastructure model, etc.) that are limited by aggregation and by
    unrealistic behavior rule sets.

-   The new systems will be able to conduct sensitivity studies of key
    causal effects of an observed change in a region's stability
    (surprises) in reasonable computing times.

-   These systems will allow analysts to intuitively comprehend and
    control the simulation of future event streams and will facilitate
    analysis of historical precursors.

    The objective of this chapter is to provide a summary of the
    insights from the current effort to (1) demonstrate or document the
    ability to computationally control and analytically manipulate a
    system with the large number of agents (i.e., 10^8^) necessary to
    model regions at the level of the individual agent and (2) complete
    "what if" scenarios with clock times of much less than one day. The
    deliverable for this task included a literature survey of systems of
    comparable complexity or systems that can be extended to the size of
    systems required by the ultimate ACSES program.

    **Addressing the Computational Complexity Issue**

    The granularity of the data required to describe HSBC factors
    important to regional stability places stringent constraints on the
    computational system. Description of most geographic regions will
    require on the order of 10^8^ agents with complexity assumptions
    dictated by the richness of the data processing underlying the HSBC
    theoretical framework.

    The objective of Task 5 is to illustrate the feasibility of
    operating such a complex agent-based simulation system that can
    generate courses of action in computational times much less than 24
    hours. Because the simulation results are required to be actionable,
    longer computation times may not allow the simulation to execute
    within the commander's decision cycle. The approach to this task
    included

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-   estimating the maximum hypothetical complexity of the agents that
    provides for each dimension of the HSBC, incorporating elements of
    modern social theory;

-   characterizing the maximum complexity of the environment that allows
    for real-time updating of the environment with unfolding events;

-   developing literature examples of analogous systems of comparable
    complexity;

-   comparing employed computational resources to those currently
    available with similar run times for case studies.

    Within the literature, three important studies have described the
    construction of agent systems as well as construction of those
    systems needed to manipulate more than 100 million agents. The first
    system study (Germann et al.) modeled the propagation of avian
    influenza. Germann's model distributed a synthetic population of 281
    million individuals among 65,334 census tracts to closely represent
    the actual population distribution according to publicly available
    2000 U.S. census data. The second study also modeled the global
    transmission of avian influenza. The model, MIDAS, used 300 million
    agents, but these agents were simpler in design than the Germann
    agent. The third study, undertaken at the Brookings Center on Social
    and Economic Dynamics (CSED), reported development of a flexible,
    large-scale, distributed agent-based model that is capable of
    simulating up to four billion agents.

The basic agent-based computational model (ABM), unique for its scale
and adaptability, has a 300-million-agent implementation specific to the
United States. This spatially and demographically realistic
implementation is based on the 2000 U.S. census data, contains a 4000 ×
4000 travel matrix (encoding a Gravity Model), and is resolved at the
level of the census track. This model, named the U.S. National Model,
was developed as a tool for applied public policy research.

The ABM methodology was successfully applied to a wide variety of policy
questions, including infectious disease transmission, public health
challenges such as obesity and smoking, and civil violence. In brief,
these \"virtual societies\" are typically characterized by the
following:

1.  *Explicit space*: Events transpire on an explicit space, such as an
    n-dimensional lattice, a social network, an organization, or, as in
    the U.S. National Model, an explicit map.

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2.  *Heterogeneous autonomous individuals*: Every single agent is
    explicitly modeled and tracked. They each differ in myriad
    ways---genetically, culturally, by social network, by their rules of
    behavior, by health status---all of which may evolve or adapt
    endogenously over time depending on their autonomous interactions
    with other agents and with their environment.

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```
3.  *Local interactions*: The traditional assumption of mass action
    kinetics is eschewed in favor of a more realistic depiction of local
    social contact dynamics in discrete and explicitly represented
    social units (the home, the workplace, etc.).

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```
4.  *Bounded rationality*: Unlike most traditional models, individual
    agents do not have perfect information, and they do not optimize. As
    with real people, they rely on simple heuristics, community norms,
    and biased and partial reporting in their decision-making.

Agent-models generate extremely high volumes of clean data that are
analyzed statistically to yield very high-fidelity characterizations of
their behavior, sensitivities, bifurcation points, etc. This innovative
technique is particularly well suited to applied public policy research
for three reasons: it allows rich and diverse human behaviors to be
incorporated rigorously, it facilitates interdisciplinary research and
stakeholder participation by providing an easily communicated common
framework, and the computational environment provides a test-bed for
public policy interventions that is both timely and cost-efficient.

The agent-based U.S. National Model now on an unprecedented scale allows
analysis of the complex interactions between a particular policy and the
environment that it is intended to affect. While the model is currently
being used to design and test optimal policies to mitigate and contain
an influenza pandemic in the United States, research teams continue to
expand the core model's functionality with the goal of creating a
spatially and demographically realistic global agent-based model.

Although at least three systems exist that provide applicable
benchmarks, this calculation will compare the hypothetical ACSES system
to the Germann system to provide a measure of the computational
resources required to manipulate the model. Therefore, all comparisons
associated with this task will benchmark against the Germann agents and
simulation system.

The conceptual layout of the generic ACSES agent is shown in Figure 5-1.

**Figure 5-1.** The conceptual design of the agent is built with seven
conceptual modules

Because the purpose of the individual modules (excluding the cognitive
module) is to provide a flexible framework to test emerging social
theories, it is not possible to calculate the computational requirements
of each module yet. However, the size of the individual modules must be
large enough to ingest information and provide a metric to the cognitive
module so that the agent can evaluate whether a change of state is to
occur. As each of the six other modules receive inputs from sensing
their environment, the algorithms embedded in the cognitive module will
calculate the agents' change in state based upon this newly received
information. Modern theories will inform the algorithms within each
module.

The cognitive module represents the controller of the agent and after
receiving the metrics from each of the six other modules, "decides"
whether the agent undergoes a change in state. How complex each
individual agent (and in turn, each of the seven modules) must be to
actually generate these metrics is an on-going topic of research.
However, the comparison of emerging computational capabilities will
require a hypothesized estimate of the computational size of the seven
modules.

To scale the computational requirements, we will assume an enhanced
agent with maximized complexity based on the PMFServ design of Silverman
et al. This hypothetical agent design assumes we use modules that
reflect elements of modern social theory and is sufficiently flexible
that current and emerging theories can be tested without rebuilding the
system from first principles. We will assume this
hypothetical-design-basis agent will have the modules based on an
ontology of current modern social theory in each of the political,
military, economic, social, information, and infrastructure (PMESII)
domains as well as a cognitive module for balancing and trade-offs so
that the agent may take action depending upon its updated environment.
We will then compare this modified Silverman-style agent to the Germann
agent to estimate the ACSES system's computational requirements.

The agent used in the Germann study is shown in Figure 5-2. This agent
is much simpler than those expected to be required for the ACSES system.
Although the system provides for a synthesized population operating
according to information received in a relatively sophisticated manner,
the framework envisioned below does not provide for trade-offs between
the various domains, such as the six domains in the ACSES design. One
would expect that Germann's model and the ACSES's political, economic,
military, etc. modules would be of similar complexity. Therefore, one
can imagine an ACSES agent comprising the Germann model reproduced six
times plus a cognitive model (to provide the trade-offs among the six
domains) that is at least as complex as any of the other modules. The
computational intensity is that required for an agent consisting of the
seven modules (cognitive plus six elements).

**Figure 5-2.** Germann model notional architecture

![](media/image250.wmf){width="3.4756944444444446in"
height="3.2020833333333334in"}![](media/image251.wmf){width="4.132638888888889in"
height="2.8756944444444446in"}

Starting with the cognitive module, Silverman et al. created goal,
standards, and preferences (GSP) trees that hold the values that compose
the agent's cognitive (or emotion) module and applies the hierarchy to
evaluate the state of the world and the actions of others. They also are
the motivators for the agent's own actions and give rise to event stress
(failed values) as well as subjective utilities for next action choices.
One wants to design the GSP trees so that each possible action in the
scenario will impact some branch of one or more of the GSP trees. GSP
tree examples are shown in Figure 5-3.

**Figure 5-3.** Example goal, standard, and preference tree

  ------------------------------------------------------- -------------------------------------------- -------------------------------------------------------
  ![](media/image252.jpeg){width="1.9722222222222223in"   ![](media/image253.jpeg){width="2.06875in"   ![](media/image254.jpeg){width="2.1729166666666666in"
  height="3.0in"}                                         height="3.0in"}                              height="3.0in"}

  ------------------------------------------------------- -------------------------------------------- -------------------------------------------------------

Once the GSP tree structure is settled, one begins the process of
assessing how important each branch is to a given archetype in the
scenario. Empirical information is organized into evidence tables
through a modified content analysis process by breaking statements into
simpler units with one theme (replicating statements when necessary),
adding additional fields, namely reliability and relevance, and then
sorting.

Although the Silverman model above reflects only one of a number of
possibilities as far as a cognitive module based on modern social
theory, most cognitive modules are composed of some hierarchy upon which
the metrics from the other six modules are weighted and evaluated
against criteria for a transition of state. However, if we compare the
agents used in the ACSES modules to those used in the Germann study, the
complexity of any one of the seven modules would be on the same order as
the complexity of the agents Germann used. In the Germann study, the
agents were programmed to move and interact with their fellow agents
according to observed population mobility models, but in reality, the
agents were simple state machines.

Germann's simulation of pandemic flu propagation employed a model
population of 281 million individuals distributed among 65,334 census
tracts to closely represent the actual population distribution according
to publicly available 2000 U.S. census data. Each tract was in turn
organized into 2000-person communities. The model ran in cycles of two
12-hour periods (\"day\" and \"night\"), during which individuals would
associate within seven contexts (\"mixing groups\"). In six of these
contexts (households, household clusters, preschools, playgroups,
schools, and work groups), relatively close person-to-person association
regularly occurs. Additionally, \"neighborhoods\" and \"communities\"
provide unspecified contexts (e.g., shopping malls) within which
occasional casual person-to-person association occurs. Daytime contacts
occur in neighborhoods and communities as well as in the age-appropriate
setting, and nighttime contacts occur only in households, household
clusters, neighborhoods, and communities. U.S. census data on
tract-to-tract worker flow is used to model the commute of working
adults to their workplaces, thus accurately capturing the short- to
medium-distance population mobility important for disease spread. In
addition, each individual took occasional long-distance trips (three per
year on average), lasting between 1 day and 3 weeks (4.1 days on
average), matching Bureau of Transportation statistics.

Germann's simple model of long-range travel can be extended to account
for different types of travel (e.g., business or leisure) or groups of
travelers (such as a family) or to explicitly incorporate the airline
network structure.

To model the introduction of pandemic influenza into the United States,
Germann assumed that impenetrable borders are either prohibitively
expensive or impossible to create, and that international air travel is
the dominant mode of influenza introduction from outside the United
States. Consequently, a small random number of incubating individuals,
equivalent to 0.04% of arriving international passengers, is introduced
each day at each of 14 major international airports in the continental
United States. The simulation covered 180 days, roughly the length of a
U.S. influenza season. We assume that, because of the uncertainty in
diagnosis of influenza infections and the sporadic nature of the early
stages of an outbreak, a cumulative number of 10,000 symptomatic
individuals nationwide is required to trigger a nationwide pandemic
alert.

Similar to Germann's approach, the ACSES's Afghanistan example starts
with the distribution of a model population. The Afghanistan population
in 2003 was estimated by the United Nations at 23,897,000. In that year
approximately 3% of the population was over 65 years of age, with
another 43% of the population under 15 years of age. The country had 107
males for every 100 females in 2003. According to the UN, the annual
population growth rate for 2000--2005 was 3.88%, with the projected
population for the year 2015 at 35,473,000. The population density in
2002 was 42 per sq km (110 per sq mi).

The Population Reference Bureau estimated that 22% of the population
lived in urban areas in 2001. The capital city, Kabul, had a population
of 2,454,000 in that year. Other major population centers included
Kandahar, 339,200; Mazār-e Sharif, 239,800; and Herat, 166,600.
According to the United Nations, the urban population growth rate for
2000--2005 was 6.9%. These figures are unreliable, however, because many
city dwellers have left their urban homes for refuge in rural areas.
Approximately 20% of the population is nomadic.

Afghanistan spans a geographic area of 647,500 square kilometers. As
documented within Landscan Global, the population can be distributed
among 1 sq km sections, and the nomadic population can be distributed
based on historic migratory patterns. The number of agents per section,
or district, varies from 10^5^ in Kabul to less than 20 in the outlying
mountainous areas.

Rather than the seven mixing groups assumed for the Germann example, we
expect that the Afghanistan example will need approximately 22 contexts,
or mixing groups, to describe how individuals can associate with and
influence each other.

Although each individual may interact with any member of his or her
mixing group, to assume that everyone within a mixing group interacts
with every member within the country is an unreasonable aggregation when
estimating the maximum complexity necessary to explain causes of
regional stability. Communication is neither instantaneous across
regions nor received in a uniform manner within the community. The
maximum number of communities can be represented by 650,000 census
tracks multiplied by the 22 mixing groups.

The table below compares the complexity of the hypothesized system with
the complexity of the Germann system. As illustrated in this table, one
would expect that the hypothesized system would be approximately 7 times
more complex than the Germann system. If we assume quadratic scaling of
the computational requirements, then the number of CPU years required to
run a ACSES simulation is 7^2^ or 49 times that of a simulation using
the Germann system.

**Table 5-1.** Complexity comparison between ACSES and Germann
simulation systems

  -------------- ----------------------------- ----------------------------- -------------- --------------
  System         Number of agents              Census tracts                 Mixing groups  Number of
                                                                             per tract      modules per
                                                                                            agent

  Germann et al. 2.8$$`<!-- -->`{=html}10^8^   6.5$$`<!-- -->`{=html}10^4^   7              1

  Hypothesized   2.4$$`<!-- -->`{=html}10^7^   6.5$$`<!-- -->`{=html}10^5^   22             7
  ACSES system                                                                              
  -------------- ----------------------------- ----------------------------- -------------- --------------

A typical production run as reported by Germann was performed on 256
central processing units (CPUs) of a 2048-processor Intel Xeon 2.4-GHz
cluster with Myrinet interconnect. A typical computational run took
8--12 h to complete a simulation of 180 days. In all \~ 200 production
runs were performed, amounting to a cumulative \~ 70 CPU years of
computer time. On average, a Germann production run would require about
125 CPU days (70 CPU years × 365 days per year/200 runs) to complete.
The computational time estimate is approximately 6250 CPU days for
running a single "what if" scenario. This assumes the ACSES agent is
approximately 50 times more computationally complex than the Germann
agent.

To compare this value to existing computational resources available at
ORNL, we consider the Cray XT4 named Jaguar comprising 124 cabinets
containing more than 11,700 dual-core AMD processors. The system
achieved 101.7 teraflops on the Linpack benchmark, which is more than 85
percent of its theoretical peak of 119 teraflops. By this calculation, a
scenario would require approximately 12.8 hours to complete.

Jaguar has at least one more upgrade in its future. That upgrade will
more than double Jaguar\'s performance to a peak of 250 teraflops. By
late 2008, a new one petaflops Cray \'Baker\' system will be installed.
Both machines will employ the upcoming quad-core AMD Opteron processors.
If one petaflop machine could be dedicated to this effort, then a
typical scenario would be calculated in about 75 minutes.

Before the impending upgrade, the existing Jaguar system would be able
to perform the expected ACSES simulations in clock times significantly
less than one day. The actual time that would be required would depend
on the actual agent design, the time period within the simulation
necessary to observe emergent behavior, and the overheads associated
with these simulations.

**Computational Costs**

Fiscal feasibility can be assessed not only from the estimated
computational time of a maximum-size, 100-million-agent system but also
from the cost of individual simulations. Surveying rental costs of HPC
systems, IBM and Sun Systems have quoted 1024 processors racks on
Blue-Gene-type systems (similar in capability to the Jaguar system) that
can be rented on the basis of only those processors actually used and
the time they are engaged. Although lower costs are usually negotiated,
most bids cluster around one dollar per processor hour. At this rate,
the simulation of 31 million agents (approximate 2006 population of
Afghanistan) would require approximately \$0.08 per simulation time
step.

To illustrate the maximum computational time and costs, a calculation
was performed for the most complex agent envisioned for the ACSES
system. Renting individual racks and paying only for those processors
actually used, each of the most complex scenarios would require
approximately \$1250 in computational time for a 1000--time-step
simulation. A simulation with 3-day time steps for six months would cost
on the order of \$75. Even at the extreme end of computational
intensity, these costs are well within the costs of similar simulation
programs currently under development.

Between these two extremes is a three-dimensional trade space that
trades speed of simulation, simulation costs, and observed emergent
behavior. Compromises to allow the synthetic populations to fit into
existing computational platforms are no longer mandatory.

The lesson learned from this calculation is that compromises taken in
the past to aggregate populations because small platforms were employed
to perform social simulations can be eased through HPC applications.
These applications will expand the opportunity to leverage human terrain
results within a simulation environment and will offer new options for
strategic planners.

Disaggregated models that also track the motion of individual social
atoms are computationally intensive. The complexity of moving resources
and people is compounded by increasing granularity in data that allows
inclusion of finer behavioral dynamics, thereby increasing the
computational intensity. Given that dynamic planning and operational
support require large-scale simulations within narrow time windows,
reducing execution times is the most critical challenge and can now be
met with significant computing resources beyond desktop environments.
High-performance computing is, therefore, a necessary enabler for
simulations that generate multiple courses of action for a large social
region.

The resulting insight is that HSBC simulations are an HPC computing
problem in the same sense that weather prediction is an HPC problem; the
computations can be done in hours or days (not months, like some \"big
physics\" problems), but the need to meet an \"actionable\" deadline
imposes a completion time of minutes or hours and, consequently, a need
for HPC. The cost and availability of these resources have now placed
these resources within the reach of the combatant commands.

**Simulation Setup of the ORMAC System**

Although the feasibility of performing disaggregated simulations has
been demonstrated, the benefits of disaggregation in forecasting
outcomes are not well-defined. An experiment was performed to gain some
insights about the additional information available to commanders from
disaggregated simulations. Two independent simulation systems ran a
simple Afghanistan example using leadership theory at different levels
of aggregation. A NetLogo system used about 10^4^ agents, and an ORMAC
simulation used approximately 31 million agents. Although the synthetic
populations and the underlying theories were identical in the two
systems, minor adaptations to the NetLogo simplifying assumptions were
used to operate at the disaggregated level of the individual. The
following section provides a description of the ORMAC adaptations to the
NetLogo simulation implementation and synthetic population data
generation.

***Summary of the Adaptation of the Social Theories for the Comparison
Experiment***

Three categories of social theories have been proposed for exploration
by this research effort: leadership, learning, and social mobilization.
Here, we will focus only on the computational aspects of these theories.

Although the implication and interpretation of the leadership theories
and the social mobilization theories are different, their computational
implementation can be achieved by using a single utility function:

$U = (1 - L)^{w_{L}}(1 - C)^{w_{C}}(1 - I)^{w_{I}}(1 - E)^{w_{E}}(1 - V)^{w_{V}}(1 - F)^{w_{F}}(1 - R)^{w_{R}}$.
(1)

This utility function encompasses seven factors: *L* is the agent's
loyalty to the leader, *C* is the coercion factor, *I* is ideology, *E*
is economic welfare, *V* is security against violence, *F* is the
influence of close associates, and *R* is repression and social
influence for defying repression. *L*, *C*, *I*, *E*, *V*, *F*, and *R*
fall in the range \[0,1\]. The weights are required to be nonnegative,
to be less than or equal to 1, and to sum to 1: *w~L~* + *w~C~* +
*w~I~* + *w~E~* + *w~V~* + *w~F~* + *w~R~* = 1. The overall
computational goal is to identify the behavior value (B) that would
allow a citizen agent to maximize the value of its utility function.

Let O denote the order of the leader (+1: pro-government, 0: neutral,
and -1: pro-Taliban), B denote the behavior of a citizen agent (+1:
pro-government, 0: neutral, and -1: pro-Taliban), $\overline{B}$ denote
the average behavior of the citizen agents within a certain radius of
the focal agent ($\overline{B} \in \lbrack - 1,1\rbrack$), $\alpha$
denote the relative magnitude of the resources that can be brought by
the leader to bear against an agent ($\alpha \in \lbrack 0,1\rbrack$),
and P denote the position of a citizen agent
($P \in \lbrack - 1,1\rbrack$). Eq. (1) can be rewritten as the
following:

$\begin{array}{r}
U = \\
\left\lbrack 1 - \frac{\left| (O - B) \right|}{2} \right\rbrack^{w_{L}} \\
\left\lbrack 1 - \frac{\alpha \times \left| (O - B) \right|}{2} \right\rbrack^{w_{C}} \\
\left\lbrack 1 - \frac{\left| (P - B) \right|}{2} \right\rbrack^{w_{I}} \\
(1 - E)^{w_{E}} \\
(1 - V)^{w_{V}} \\
\left\lbrack 1 - \frac{\left| \left( B - \overline{B} \right) \right|}{2} \right\rbrack^{w_{F}} \\
(1 - R)^{w_{R}}
\end{array}$ . (2)

The effect of any of the seven factors in Eq. (2) can be turned off by
setting the associated weight to 0. When *w~F~* and *w~R~* are set to 0,
this utility function reflects the leadership theories. The lack of
theories providing guidance for simulating the behavior of armed forces
and their mobility patterns has led to the decision of disabling the
repression factor (R) by setting *w~R~* to 0 in our current simulation.

Learning theories were introduced to demonstrate potential changes in a
citizen agent's ideology and loyalty over time. Incorporating learning
theories into Eq. (2), we obtain the following new utility function:

$\begin{array}{r}
U(t) = \left\lbrack 1 - \lambda_{i}(t\frac{) \times \left| \left( O_{i}(t) - B_{i}(t) \right) \right|}{2} \right\rbrack^{w_{L}} \\
\left\lbrack 1 - \frac{\alpha_{i} \times \left| \left( O_{i}(t) - B_{i}(t) \right) \right|}{2} \right\rbrack^{w_{C}} \\
\left\lbrack 1 - \frac{\left| \left( P_{i}(t) - B_{i}(t) \right) \right|}{2} \right\rbrack^{w_{I}} \\
(1 - E_{i}(t))^{w_{E}} \\
(1 - V_{i}(t))^{w_{V}} \\
\left\lbrack 1 - \frac{\left| \left( B_{i}(t) - {\overline{B}}_{i}(t - 1) \right) \right|}{2} \right\rbrack^{w_{F}} \\
(1 - R_{i})^{w_{R}}
\end{array}$ , (3)

where $\lambda$ is a citizen agent's perception of his leader's
legitimacy ($\lambda \in \lbrack 0,1\rbrack$). The notation
"*variable~i~ (t)"* is interpreted as "the value of variable of citizen
agent *i* at time *t*." For instance, ${\overline{B}}_{i}(t - 1)$ refers
to the average behavior value in citizen agent *i*'s territory at time
$(t - 1)$.

The four learning theories are cognitive dissonance, results-based,
homophily, and socialization (no changes over time). These theories
govern parameters$\lambda_{i}(t)$and $P_{i}(t)$. More details on
learning theories can be found in Chapter 2: Agent-Based Modeling. Based
on data source availability, we implemented the cognitive dissonance and
the homophily theories.

If the cognitive dissonance theory is applied, $\lambda_{i}(t)$and
$P_{i}(t)$are calculated by Eq. (4) and Eq. (5), respectively.

$\lambda_{i}(t) = \lambda_{i}(t - 1) + \frac{\delta_{\text{CD}} \times \left\lbrack 1 - \frac{5}{2} \times \left| O_{i}(t - 1) - B_{i}(t - 1) \right| + \frac{1}{2} \times \lbrack O_{i}(t - 1) - B_{i}(t - 1)\rbrack^{2} \right\rbrack}{2}$
(4)

$P_{i}(t) = P_{i}(t - 1) + \frac{\gamma_{\text{CD}} \times \left\lbrack B_{i}(t - 1) - P(t - 1) \right\rbrack}{2}$
(5)

The values of $\delta \in \lbrack 0,1\rbrack$ and
$\gamma \in \lbrack 0,1\rbrack$ indicate the speed with which cognitive
dissonance operates.

If the homophily theory is applied, $\lambda_{i}(t)$and $P_{i}(t)$are
updated by Eq. (6) and Eq. (7), respectively.

$\lambda_{i}(t) = \lambda_{i}(t - 1)(1 - \delta_{H}) + {\overline{\lambda}}_{i}(t - 1) \times \delta_{H}$
$$ (6)

$P_{i}(t) = P_{i}(t - 1)(1 - \gamma_{H}) + {\overline{P}}_{i}(t - 1) \times \gamma_{H}$
(7)

To ensure that $\lambda_{i}(t) \in \lbrack 0,1\rbrack$and
that$P_{i}(t) \in \lbrack - 1,1\rbrack$, the values are truncated as
follows:

$\lambda_{i}(t) = \begin{array}{r}
\left\{ + \text{1           if   λ}_{\text{i }}(t) > 1 \middle| \right.\ \left\{ \lambda_{i}(t)\text{        if  0} \leq \lambda_{i}(t) \leq 1 \middle| \right.\ 
\end{array}$ (8)

$P_{i}(t) = \begin{array}{r}
\left\{ + \text{1             if   P}_{\text{i }}(t) > 1 \middle| \right.\ \left\{ P_{i}(t)\text{          if  -1} \leq P_{i}(t) \leq 1 \middle| \right.\ 
\end{array}$ (9)

In summary, the ORMAC simulation calculates the value of B~i~(t)
($B_{i}(t) \in \left\{ - 1,0,1 \right\}$) for each citizen agent i that
allows her to maximize the utility function described in Eq. (3) at time
t.

***Design of the ORMAC Agent***

The simulation uses only one type of agent, the citizen agent. Each
agent has both geographical attributes and mandatory attributes required
by the social theories. These attributes are described below. The
techniques used to build the synthetic population were described in
Chapter 1: Evaluation of Data Availability and Worldwide Survey of Data
Quality.

> **Geographical Attributes**
>
> The first step in building the synthetic population was geospatially
> locating the 31 million agents within grids approximately one
> kilometer on each side using the LandScan Global techniques for
> disaggregation. At the end of this step, the following attributes were
> assigned to each agent: district_id, cell_id, agent_id, x_coordinate,
> and y_coordinate. The x and y coordinates of an agent were the same as
> the coordinates of the center of the cell to which the agent belonged.
> The 31 million agents were distributed as shown in Figure 1-3 (in
> Chapter 1) .
>
> An agent\'s ideology (P) is determined from the Gallup World Poll,
> which reports the percentage of individuals, by three ethnic group
> designations, that are pro-government, pro-Taliban, and neutral. Among
> Pashtuns, these ideological affiliations vary depending on whether the
> agent resides in the war zone. Among Tajiks and all other ethnic
> groups, ideological affiliations do not vary geographically.
>
> **Table 5-2.** Mindshare from Gallup World Poll

+-----------------+-----------+---------------+------+----------------+
|                 | Pashtun-  | Pashtun-      | T    | Other          |
|                 |           |               | ajik | Ethnicity      |
|                 | War Zone  | Not War Zone  |      |                |
+-----------------+-----------+---------------+------+----------------+
| Pro-Government  | 53        | 70            | 71   | 95             |
+-----------------+-----------+---------------+------+----------------+
| Neutral         | 33        | 16            | 27   | 4              |
+-----------------+-----------+---------------+------+----------------+
| Pro-Taliban     | 14        | 14            | 2    | 1              |
+-----------------+-----------+---------------+------+----------------+

> Attitudes seem to have significant cleavages along ethnic (Pashtun,
> Tajik, and other), location (north, south), and combat proximity (war
> zone vs. nonwar zone) lines. A map of war zone location is reproduced
> below. The order of the leader (O) in the district is set to 1 if the
> district is in the nonwar zone, and the order is set to 0 if the
> district is in the war zone because we believe that leaders in the
> nonwar zone are more likely to be pro-government, while leaders in the
> war zone are under pressure from the Taliban and are more likely to
> take a neutral stand. The violence factor (V) is derived from the map
> below and the Gallup Poll. In the war zone, 32% of poll participants
> said that they felt safe, while 68% of those in the nonwar zone felt
> safe. Therefore, V=0.68 for agents in the nonwar zone, and V=0.32 for
> agents in the war zone.
>
> The indicator database for how economic welfare is perceived by the
> individual and is tied to behaviors in support of the leader in the
> Afghan case is highly dependent upon the social atom's proximity to
> opium production. The Afghan example is simpler than most countries
> where corruption indices might create usable indicator databases where
> economic activity under leadership control may be more complex.
> However, in Afghanistan, the primary source of discretionary economic
> resources that a leader might use to obtain behavior according to his
> order is directly connected to the leader's ability to control opium
> profits or his ability to control law enforcement actions. A map of
> opium districts is shown in Figure 1-6 (in Chapter 1).
>
> The coercion motive is captured by $\alpha$, the relative magnitude of
> the resources that can be brought by the leader against an agent. We
> have hypothesized that agents who belong to the majority ethnic group
> in the district will have more resources brought against them than
> those in minority ethnic groups. Within each district, each ethnic
> group is ranked in one of three categories, from the most numerous to
> the least. If an agent belongs to the most numerous ethnic group, the
> resources ($\alpha$) that may be brought against the agent are
> quantified by an $\alpha$ value of 0.75. Agents in the second-ranked
> ethnic group are given an $\alpha$ value of 0.5, and agents in the
> least numerous ethnic groups are given a value of 0.25.
>
> We assumed that agents' initial perceptions (λ) of the leader's
> legitimacy follow a normal distribution with a mean of 0.5 and a
> standard deviation of 0.25.
>
> **Mandatory Agent Attributes**
>
> To calculate the utility function in Eq. (3), the following attributes
> must be quantified for each agent: O,$\alpha$, P, E, V, and $\lambda$.
> P and $\lambda$ change over time according to the learning theory
> applied. The rest of the attributes remain constant for the entire
> duration of the simulation.
>
> In summary, each ORMAC ACSES agent is represented by a tuple
> (district_id, cell_id, ethnicity_id, agent_id, x_coordinate,
> y_coordinate, $O$,$\alpha$, $P_{i}(t)$, E, V, $\lambda_{i}(t))$. The
> combination of "district_id + cell_id + ethnicity_id + agent_id"
> uniquely identifies an agent.

***Flow diagram of the ORMAC Simulation***

The flow diagrams in Figures 5-4 and 5-5 illustrate the computational
logic of the ORMAC ACSES simulation.

**Figure 5-4**. Flow diagram of "Social Mobilization Theories +
Cognitive Dissonance Theory"

> ![](media/image255.wmf){width="3.4451388888888888in"
> height="4.391666666666667in"}
>
> **Figure 5-5.** Flow diagram of "Social Mobilization Theories +
> Homophily Theory"
>
> ![](media/image256.wmf){width="3.738888888888889in" height="4.775in"}

**ORMAC Simulation Output Description -- Feasibility Experiment**

We operated the ORMAC simulation to compare the distributions of Afghan
terrorist incidents in 2006 against the detailed results of the ORMAC
simulation system. This experiment was not intended as a demonstration
of predictability but rather as evidence that disaggregated systems can
generate sufficiently detailed results to allow comparison with
real-world data.

After operating the ORMAC simulation, we were able to observe small
district-by-district differences in the rise of pro-Taliban populations.
If we assign district identification numbers from 1 to 329, we can plot
the distribution of these pro-Taliban populations and compare these
predicted pro-Taliban cells with the geographic distribution of Afghan
terror incidents. If data on the district-by-district distribution of
these pro-Taliban populations can be correlated with increased terrorist
incidences, then we might be able to evaluate courses of remedial action
by leadership theory. The plot of these minority (pro-Taliban)
population distributions is shown in Figure 5-6.

**Figure 5-6.** Percentage pro-Taliban minority opinion by district

![](media/image257.jpeg){width="6.0in" height="4.5in"}

A geographic representation of the distributions (Figure 5-7) shows that
the pro-Taliban populations seem to cluster around the boundaries
between the Pashtun-dominated areas and those areas dominated by other
ethnic groups.

**Figure 5-7.** Distribution of pro-Taliban minority opinion

> ![](media/image258.wmf)

Figure 5-8 maps the distribution of terrorist incidents according to the
National Center for Counterterrorism, which maintains the Worldwide
Incident Tracking System (WITS).

**Figure 5-8.** Distribution of Taliban terrorist incidents

![](media/image259.jpeg){width="5.347222222222222in"
height="3.9583333333333335in"}

If we examine a small subregion (Ghazni Province) of the plotted terror
incidents (Figure 5-9) and compare it to the district population of
Taliban supporters (Figure 5-10) calculated in the ORMAC system, the
qualitative correlation is apparent.

**Figure 5-9.** Distribution of Taliban terror incidents with Ghazni
province highlighted

![](media/image260.jpeg){width="5.385416666666667in"
height="3.9895833333333335in"}

**Figure 5-10.** Distribution of pro-Taliban minority opinion under
representative theory

![](media/image261.png){width="5.385416666666667in"
height="3.9902777777777776in"}

If we plot the location of terrorist incidents against the location of
pro-Taliban populations, the resulting histogram (Figure 5-11) resembles
the type of histogram we would predict under a coercive leadership.

**Figure 5-11.** Minority pro-Taliban opinion correlated with 2006
terrorist incidents

![](media/image262.png){width="6.190277777777778in"
height="4.503472222222222in"}

The correlation coefficients for three different leadership theories[^4]
are shown in the table below. Coercion theory produces the strongest
correlation with the data. Thus, manipulation of the input variables for
the coercion model may allow the commander to see the impact of
different courses of action intended to quell terror incidents in
specific locations.

**Table 5-3.** Correlation coefficients between minority opinion and
WITS databases

+-----------------------------------+----------------------------------+
| **Variables**                     | **Pearson Correlation            |
|                                   | Coefficients**                   |
|                                   |                                  |
|                                   | **Reference: 2006 Incidents**    |
+-----------------------------------+----------------------------------+
| Coercion, % Pro-Taliban           | .49564 (\<0.0001)                |
+-----------------------------------+----------------------------------+
| Legitimacy, % Pro-Taliban         | .20776 (\<0.0001)                |
+-----------------------------------+----------------------------------+
| Representative, % Pro-Taliban     | .48307 (\<0.0001)                |
+-----------------------------------+----------------------------------+
| Incidents, 2007                   | .83159 (\<0.0001)                |
+-----------------------------------+----------------------------------+

From this exercise, a few insights into the ORMAC system can be
identified.

1)  Although the coercion and representative theories have similar
    correlations to the actual NCTC data, the ORMAC simulation system
    identified coercion theory as most representative of the ground
    truth observed in Afghanistan.

2)  Resolution of data allows small subpopulations to be identified.
    These small populations were not apparent in the more aggregated
    NetLogo model but were apparent at higher resolution modeling.
    Testing the modeling results against the presence of these
    populations would be a good validation exercise for this modeling
    approach.

3)  Higher numbers of agents reveal features important to
    course-of-action analysis. We will discuss this further in the next
    section.

4)  Using real-world data, it is possible to select among social science
    models and tease out proximate root causes. Because variables
    important in coercion theory produce the best correlation with
    real-world data, courses of action predicted by coercion theory to
    suppress problematic populations may be the most promising.

5)  Model calibration approaches may be developed to illustrate forward
    anticipation as a next step. Calibration of the model with more
    historical data sets might lead to more refined weighting functions
    that would improve the correlations and forecasting skill.

Following this comparison of the ORMAC system to actual 2006 data, a
comparison experiment between the NetLogo and ORMAC systems was
conducted. The objective of this experiment was to observe whether
different results were obtained when the scale of the simulation was
disaggregated from 5000 agents to 31 million agents.

**Comparison of NetLogo and ORMAC Results in Response to the Commander's
Questions**

To compare the two simulation systems, we selected the following
top-level, hypothetical question:

> Taliban terrorist activity has increased in Ghazni Province in 2007
> over the levels of 2006. My objective is to drastically reduce the
> level of violence in 2008. If I have the following courses of action
> at my disposal---

-   surging of troops into Taliban-controlled areas,

-   providing additional economic and social development resources to
    local leaders,

-   replacing local leaders with more supportive officials,

-   reducing the number of Taliban entering the border regions, and

-   implementing social, political, or economic actions to reduce
    general population support for the Taliban

> ---what would the impact of these courses of action be?

A subset of the question above was selected to perform the side-by-side
comparison. The complete NetLogo response for the commander's question
can be found in Chapter 2. Selection of the experimental run to compare
the effects of different levels of aggregation between the NetLogo
system and the ORMAC system is described below.

Although the ORMAC simulation is capable of simulating the movement of
fighter agents and the geographical movement of violence, for the
comparison we selected a special case in which soldier agents were
neither initially present nor "regenerated". This decision was made
because the NetLogo scale for the citizen agents is different from the
soldier agents in a variable and nondeterministic way. Therefore, the
special case of no troops allows direct, side-by-side comparison of the
different simulations. Following are the initializing conditions:

-   No soldier agents were simulated. In the NetLogo simulation, \"Add
    Agent at Setup\", \"Regeneration\", and \"Surge\" were set to zero.

-   \"Influence\" and \"Repression\" were both turned off.

-   Initial O, P, R, E, and V values were determined as described below.
    For this simulation, our goal was to create input variables as
    similar to University of North Carolina at Charlotte\'s (UNCC's)
    input variables as possible.

O - Values of Leader\'s Order are dependent upon ethnicity as follows
(max of 1, min of -1):

> Pashtun (O = 0.0)
>
> Tajik (O = 0)
>
> Hazara (O = 1)
>
> Uzbek (O = 0)
>
> Turkmen (O = -1)
>
> Balouch (O = 1)
>
> Nuristan (O = 1)

P - Values of political ideology are dependent on ethnicity. The mean
value (with a standard deviation of 0.25) for all agents of each
ethnicity is given below (max of 1, min of -1):

> Pashtun (P= -0.1)
>
> Tajik (P = 0.45)
>
> Hazara (P = 0.8)
>
> Uzbek (P = 0)
>
> Turkmen (P = 0.3)
>
> Balouch (P = -0.25)
>
> Nuristan (P = -0.75)

R - Resources leaders may use against agents: 

R = 1 for all agents

E - Economic welfare of agents varied depending on the presence of opium
in a district. In districts where opium was present, 38% of agents had E
= 0.8718; otherwise E=0. The data used to determine the presence or
absence of opium in a district was a patch-level map of opium districts
used by UNCC. By comparing the geographical distribution of patches with
opium with the location of districts in Afghanistan, six districts were
determined to be opium producers (districts 27713, 27721, 27731, 27734,
27744, and 27747).

V- Violence welfare was not updated.

> In the Netlogo formulation, violence is given as vc/vc^-^, where vc is
> defined as "foes"/ ("friends" + "foes"), and vc^-^ is the worst
> possible value of vc. In this simulation, V reflects the perceived
> level of violence that individuals reported in the Gallup World Poll
> survey.
>
> V = 0.69 for all agents.

The Ghazni region as defined in the NetLogo simulation encompasses the
following districts in the ORMAC simulation: 27701, 27713, 27721, 27723,
27726, 27731, 27734, 27736, 27744, 27746, 27747, 27761, 27764, 27767,
27778, 27785.

The learning theories were turned off.

We varied two parameters: number of days per tick (3, 7, 14) and
leadership theory (C, L, R). C represents coercion theory, R corresponds
to representative theory, and L represents legitimacy theory. The runs,
given in the format of days per tick_theory, are

run1: 3L

run2: 3R

run3: 3C

run4: 7L

run5: 7R

run6: 7C

run7: 14L

run8: 14R

run9: 14C

Each leadership theory assigns the following values to the seven weights
of the utility function:

C: 0.1 0.5 0.1 0.1 0.2 0.0 0.0

R: 0.05 0.05 0.3 0.3 0.3 0.0 0.0

L: 0.6 0.1 0.1 0.2 0.0 0.0 0.0

Events were defined from the WITS data as district-level assassination
attempts within Ghazni province. At each event the order of the leader
changed. The number of days per tick determines the total number of
ticks simulated and determines at which tick each event occurs:

**Table 5-4**. Points of event insertion

  ------------- ---------- ---------- ---------- ---------- ---------- -----
                Event - 1  Event - 2  Event - 3  Event - 4  Event - 5  End

  3days/tick    11         26         27         47         58         68

  7days/tick    5          12         12         21         25         35

  14days/tick   3          6          6          13         16         26
  ------------- ---------- ---------- ---------- ---------- ---------- -----

After each event, the O values for Pashtuns decrease by 0.2. The O value
is truncated to be within the range of \[-1.0, 1.0\].

The results of these comparison experiments are plotted in Appendix 10
-- Visual Plots of NetLogo and ORMAC Comparisons. At the completion of
the nine experiments, the percentages of pro-Taliban, neutral, and
pro-government agents were estimated and recorded. Because the
stochastic elements randomized the initial conditions in the NetLogo
model, we constrained the starting point for pro-Taliban and neutral
agents to be time step zero and allowed the pro-government agents to
constitute the residual. Comparisons such as these may help improve
model outputs, in this case to correct bias errors. Simple correlation
coefficients between the NetLogo and ORMAC simulation systems were
calculated for each run, and the population's mindshare as a function of
time was also plotted. Differences in the results of the two simulation
systems could then be ascribed to differences in scale.

One interesting aspect is that the ORMAC outputs appear more responsive
to disruptive events when there is a response, while the Netlogo outputs
exhibit more random fluctuations but less response to the events. The
cause of this observation is not precisely known, but the random
fluctuations may be due to stochastic elements embedded within the
NetLogo code. The lack of responsiveness is a common feature when acute
events are averaged over large populations such as is necessary in
aggregated systems.

In situations where both NetLogo and ORMAC exhibit what appear to be
"legitimate" (i.e., occurring at the expected time steps) responses to
events, the NetLogo responses seem somewhat damped compared to the ORMAC
responses. This indeed may be caused by resolution effects, even though
this empirical "rule" has a few exceptions. The lack of any ORMAC
response (even the changes in NetLogo appear to be no more than random
fluctuations) to the disruptive events when the representativeness (R)
theory is instantiated may be worth noting. The lack of a leader may
have less immediate effect on followers when the predominant behavior is
representative. However such social explanations must be exercised with
care given that the simulation runs appear pretty flat in each case when
this theory is implemented (runs 2, 5, and 8). This may suggest an
artifact of the specific experimental design. Although the causal
explanations offered here are plausible but not proven unless further
simulations are performed, the value of simple metrics (e.g., bias and
first differences) together with visual representations may be apparent
from the discussions here.

Traditional statistical distance measures are shown next. The
correlation coefficients (CC) in the table below further indicate that
the increase in the number of days per tick does not significantly
improve the dependency between systems with respect to the number of
agents in each class.

**Table 5-5**. Correlation coefficients between NetLogo and ORMAC
outputs

  --------- --------------- ------------ ----------------- -------------- --------------
  **Run**   **Days/tick**   **Theory**   **Pro-Taliban**   **Neutral**    **Pro-Govt**

  **1**     **3**           **L**        **0.9750**        **0.8870**     **-0.2867**

  **4**     **7**           **L**        **0.9776**        **0.9463**     **-0.4136**

  **7**     **14**          **L**        **0.9610**        **0.9109**     **-0.6806**

  **2**     **3**           **R**        **-0.2505**       **-0.2614**    **0.1203**

  **5**     **7**           **R**        **-0.3966**       **-0.4101**    **0.1839**

  **8**     **14**          **R**        **-0.5601**       **-0.6048**    **0.2603**

  **3**     **3**           **C**        **0.8479**        **0.1196**     **-0.2215**

  **6**     **7**           **C**        **0.9361**        **0.4923**     **-0.3166**

  **9**     **14**          **C**        **0.9479**        **0.5612**     **-0.4139**
  --------- --------------- ------------ ----------------- -------------- --------------

If the pro-government behavior is ignored (because the ORMAC variations
in that category are limited), the correlations are strongest for
legitimacy (L) theory, followed by coercion (C). Correlations for
representativeness (R) are negative, confirming the intuition that
resolution effects may be very important for that theory. The mean
squared errors (MSE) are rather low (Table 5-6), suggesting that on
average for all the simulation time steps, the errors are not too high.

**Table 5-6**. MSE between NetLogo and ORMAC outputs

  --------- --------------- ------------ ----------------- ------------- --------------
  **Run**   **Days/tick**   **Theory**   **Pro-Taliban**   **Neutral**   **Pro-Govt**

  **1**     **3**           **L**        **0.0013**        **0.1804**    **0.1677**

  **2**     **3**           **R**        **0.0644**        **0.1648**    **0.0244**

  **3**     **3**           **C**        **0.0333**        **0.3132**    **0.1467**

  **4**     **7**           **L**        **0.0018**        **0.1567**    **0.1715**

  **5**     **7**           **R**        **0.0602**        **0.1697**    **0.0296**

  **6**     **7**           **C**        **0.0284**        **0.3089**    **0.1532**

  **7**     **14**          **L**        **0.0033**        **0.1521**    **0.1691**

  **8**     **14**          **R**        **0.0598**        **0.1596**    **0.0263**

  **9**     **14**          **C**        **0.0294**        **0.3159**    **0.1550**
  --------- --------------- ------------ ----------------- ------------- --------------

A pair-wise comparison of the correlations between ORMAC and NetLogo
runs based on instantiations of different leadership theories is shown
in Tables 5-7 and 5-8. The results are interesting not just for
exploring how the outputs from multiple theories relate to each other,
but also for seeing how the relations themselves change between the two
implementations.

**Table 5-7**. Correlation coefficients between runs for NetLogo outputs

  ---------- --------------- -------------- ----------------- ------------- --------------
  **Runs**   **Days/tick**   **Theories**   **Pro-Taliban**   **Neutral**   **Pro-Govt**

  **1,2**    **3**           **L,R**        **0.7942**        **0.4615**    **0.6081**

  **1,3**    **3**           **L,C**        **0.9166**        **0.4490**    **0.9096**

  **2,3**    **3**           **R,C**        **0.5329**        **-0.4681**   **0.7442**

  **4,5**    **7**           **L,R**        **0.7759**        **0.3778**    **0.5842**

  **4,6**    **7**           **L,C**        **0.9149**        **0.5631**    **0.9198**

  **5,6**    **7**           **R,C**        **0.5144**        **-0.4346**   **0.7077**

  **7,8**    **14**          **L,R**        **0.8190**        **0.4502**    **0.6347**

  **7,9**    **14**          **L,C**        **0.9378**        **0.6350**    **0.9006**

  **8,9**    **14**          **R,C**        **0.6023**        **-0.2874**   **0.7761**
  ---------- --------------- -------------- ----------------- ------------- --------------

**Table 5-8**. Correlation coefficients between runs for ORMAC outputs

  ---------- --------------- -------------- ----------------- ------------- --------------
  **Runs**   **Days/tick**   **Theories**   **Pro-Taliban**   **Neutral**   **Pro-Govt**

  **1,2**    **3**           **L,R**        **-0.1879**       **-0.1879**   **-1.0000**

  **1,3**    **3**           **L,C**        **0.9996**        **0.9996**    **1.0000**

  **2,3**    **3**           **R,C**        **-0.1833**       **-0.1834**   **-1.0000**

  **4,5**    **7**           **L,R**        **-0.2551**       **-0.2551**   **-1.0000**

  **4,6**    **7**           **L,C**        **0.9996**        **0.9996**    **1.0000**

  **5,6**    **7**           **R,C**        **-0.2503**       **-0.2505**   **-1.0000**

  **7,8**    **14**          **L,R**        **-0.3626**       **-0.3627**   **-1.0000**

  **7,9**    **14**          **L,C**        **0.9998**        **0.9998**    **1.0000**

  **8,9**    **14**          **R,C**        **-0.3533**       **-0.3534**   **-1.0000**
  ---------- --------------- -------------- ----------------- ------------- --------------

The correlation between the outputs from the legitimacy (L) and coercion
(C) theories, as implemented here, is high and appear to be least
affected by the simulation granularity (i.e., NetLogo versus ORMAC).
However, the relations appear completely altered for any comparison
involving the representativeness (R) theory. Once again, this may be an
artifact of the specific simulation, as all the plots with this theory
appear relatively flat \[Appendix 10, Figures A10-1 (b), (e), and (h)\].
One other observation is that the change in number of days per tick
seems to cause no statistical difference in the dependency between runs
with respect to the number of agents in each class.

A detailed comparison of a relatively "lumped" or low-resolution model
(e.g., the NetLogo-based implementation) with a relatively more
spatially "distributed" or high-resolution model (e.g., the ORMAC-based
implementation) typically entails one of two approaches: either
aggregate the distributed model outputs to the scales of the lumped
model and compare at the aggregate scales or allocate the lumped model
outputs to the scales of the distributed model and compare at the higher
resolutions. A comparison based on aggregation may be fairer because the
allocation process is not necessarily well- defined and may introduce
errors. (In contrast, aggregation processes are typically well-defined;
for example when agent counts are considered, a simple sum would almost
always be considered appropriate.). Indeed, most of the previous
comparisons in this section would fall in that category. However, in
situations where the end users or stakeholders demand higher resolution
outputs, or when the underlying dominant social processes can be best
captured at higher resolutions, a comparison at those resolutions may be
more appropriate. In such cases, allocations become necessary for
comparisons. In the absence of ancillary information, simple methods
like equal or area-weighted allocations are probably the only options.

In our case, the aggregate "patch" level outputs generated from NetLogo
need to be allocated to the finer grids at which data are obtained from
the Geographical Information Systems (GIS) and which are ultimately used
by the ORMAC-based simulations. The simulation results must be compared
at scales that matter to decision-makers (e.g., district levels in
Afghanistan).

Motivated by the points discussed above, and driven by the need to
develop proof-of-concept comparison metrics and methods, we uniformly
allocated the aggregate NetLogo outputs to the resolutions of the ORMAC
simulations. Specifically, the number of agents for each class and each
patch was converted into the corresponding number of agents for each
district by multiplying the uniformly distributed number of agents by
the number of patches that equal the geographical size of each district.
In a sense, this is just an area-weighted allocation strategy. We
focused the comparison on "Run 1" (see Table 5-6) and a few districts
for illustrative purposes.

**Table 5-9**. Linear correlation between ORMAC and NetLogo runs at
district levels

  --------- -------------- ------------------- ------------- ---------------
  **RUN**   **District     **Pro-Taliban**     **Neutral**   **Pro-Govt**
            ID**                                             

  1         27713          0.9744              0.8855        0.0000

            27723          0.9759              0.8890        0.0000

            27726          0.9759              0.8897        0.0000

            27731          0.9744              0.8856        -0.2867

            27747          0.9740              0.8846        0.0000
  --------- -------------- ------------------- ------------- ---------------

Table 5-9 shows high correlations between the ORMAC and NetLogo outputs
for pro-Taliban and neutral agents. The zero or negative correlations
for pro-government agents may be ignored given that the number of these
agents remains relatively constant during the simulation time period.
The high correlation at a district level indicates that a simple
allocation was able to handle the resolution effect in terms of the
correlation measure. In addition, we note that correlation is one
measure that captures linear associations among data fluctuations, but
more nuanced differences may not be obvious by looking at this measure
alone. Thus, the effects of resolution are clearly seen in the
district-level agent distributions over time.

The following summarizes the insight gained from these comparison
experiments:

1.  The aggregated approach yields different results when stochastic
    elements are substituted for ground truth about initial conditions.

2.  This randomness causes the aggregated system to be less responsive
    to acute localized events such as assassinations.

3.  The two systems stabilized at similar, but not identical, final
    results for mindshare at the end of the simulation time.

The ORMAC and NetLogo simulation results differ in additional ways that
would lead to different evaluations about potential courses of action.
Under each of the theories, the NetLogo system produced final results
significantly different from both the ORMAC system and the WITS data
used for validation. To ensure that the level of aggregation does not
adversely impact the usefulness of these simulations, disaggregated
models executed on fast platforms within flexible and robust simulation
environments may be important to capture the sudden, significant
destabilizing events within such a simulation environment.

Until very recently, computational frameworks and widely available
hardware have limited the selection of approaches to highly aggregated
systems (the first two approaches). New architectures can now manage
10^8^ agents and allow simulations with predictive value in
computationally tractable times. These architectures provide to the
social theorist the entire simulation space to select the most efficient
system based solely upon the theory and population under study.

We have learned a number of lessons:

1.  It is possible to manipulate 31 million agents and perform a
    calculation to compare different leadership theories without judging
    whether other theories are more appropriate to the question at hand.

2.  Behaviors observed in disaggregated models differ from those
    observed in aggregated models. The former can be compared to
    real-world events.

3.  Correlation with significant events is highly dependent on the scale
    used for the simulation. Depending on the theory to be examined,
    fast granular models are likely to be needed.

Future work is needed to define

1)  the computational space, architecture, and framework required to
    apply many of the most common social theories;

2)  the trade-off between increased simulation speed, agent aggregation,
    and observance of emerging or actionable results;

3)  the criteria and metrics that might be used to select the
    appropriate computational scheme---desktop, cluster, cloud, or HPC
    applications---for a given region, population, question, and
    sequence of theories to be instantiated.

**Insights and Conclusions**

The objective of Task 5 was to illustrate the feasibility of operating a
complex agent-based simulation system that could generate courses of
action in computational times much less than 24 hours. The approach to
this task included

-   estimating the maximum hypothetical complexity of the agents that
    provides for each dimension of the HSBC, incorporating elements of
    modern social theory;

-   characterizing the maximum complexity of the environment that allows
    for real-time updating of the environment with unfolding events;

-   developing literature examples of analogous systems of comparable
    complexity;

-   comparing employed computational resources to those currently
    available with similar run times for case studies.

Testing the 31-million-agent model of Afghanistan's population
demonstrated the ability to computationally control and analytically
manipulate a system with the large number of agents (i.e., 10^8^)
necessary to model regions at the individual level. Simulations in
computationally tractable times can be performed with computational
costs that range from less than a dollar for simulations with simple
interactions to a few hundred dollars for the largest and most complex
simulations.

If the linear scaling holds, a 31-million-agent simulation would require
approximately 5.2 hours to complete a time step. A 1000--time-step
simulation would exceed the computational time permitted at 5200 hours.
However, multiple processor systems can reduce the computational time to
a practical range at modest cost.

Even at the extreme end of computational intensity, these costs are well
within the costs of similar simulation programs currently under
development. Between these two extremes is a rich three-dimensional
trade space that trades speed of simulation, simulation costs, and
observed emergent behavior.

This effort compared and contrasted two different theory-driven,
agent-based modeling approaches and explored how each could be
implemented and evaluated. A very high-resolution model and a more
aggregated model were implemented using the same synthetic population
and the same base set of social theories to explore a variety of
response actions to an increase in Taliban-inspired terrorist
activities. Simulations with agents representing individual social atoms
were able to predict the development of small populations of pro-Taliban
members that were geographically correlated with the terrorist incidents
as recorded in the Worldwide Incident Tracking System. These
correlations were very different at aggregation scales on the level of
5000 to 1.

The lesson learned from this task is that compromises taken in the past
to aggregate populations because small platforms were employed to
perform social simulations can be eased through HPC applications. These
applications expand the opportunity to leverage human terrain results
within a simulation environment and will offer new options for strategic
planners.

The resulting insight is that HSBC simulations are an HPC problem in the
same sense that weather prediction is an HPC problem; the computations
can be done in hours or days (not months, like some \"big physics\"
problems), but the need to meet an \"actionable\" deadline imposes a
completion time of minutes or hours and, consequently, a need for HPC.
The cost and availability of these resources have now placed these
resources within the reach of the combatant commands.

**References**

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***Appendix 1 -- Research Plan***

**Objectives of the Program**

The goal of this project is to develop a TiVo™-like simulation
capability to monitor, assess, forecast and respond to adverse human
behavioral developments around the world in near real-time. This
seedling proposal identifies some of the key technical challenges in
developing such a system and proposes a research plan for assessing the
feasibility of overcoming them.

The current generation of societal modeling and simulation uses existing
architectures that are based on linking independently developed models
to provide some measure of predictability of HSBC effects resulting from
specific diplomatic, information, military, and economic (DIME)
triggering events. The current generation of modeling and simulation
systems is characterized by the following attributes:

-   These are modular systems, with each module (model) optimizing one
    or two elements of the HSBC domain. Multiple models are then linked
    together to cover the HSBC spectrum. The degree of aggregation in
    the models does not permit sensitivity analysis with respect to the
    causes of surprising changes in human behavioral dynamics without
    prohibitive computational power or analytical time.

-   Current agent-based models allow the agents to interact within their
    environment without constraints. The models generate a large number
    of potential futures but fail to quantify the probability of any
    single stream of future events. They incorporate little or no modern
    social scientific theory or analysis to adjust the rule base to
    mimic real-world observations. To generate realistic behaviors
    consistent with observations, the entire range of HSBC theories must
    be leveraged to inform the simulation rule sets.

-   These systems employ a flexible architecture to combine models from
    a variety of constituencies (such as the NATO application), thereby
    permitting a community (M&S community, alliance community,
    interagency coordination and coalitions) to jointly experiment and
    understand intracommunity interactions. However, current models and
    simulations provide no capability for real-time or near real-time
    data updates as surprising events unfold or rapid changes occur in
    societal dynamics.

-   These systems cannot conduct a sensitivity study of key causal
    effects of an observed change in a region's stability in reasonable
    computing times. They provide only limited capability for analysts
    to select streams of future events important for the question at
    hand. Current systems are difficult for analysts to intuitively
    comprehend and easily control in an agile manner.

Many recent advances permit us to consider the next generation of
modeling and simulation systems that can support the ability to
anticipate adverse human behavioral developments and that can evaluate
alternative courses of action to mitigate the impact of these
developments on U.S. national security interests. They include

-   new approaches to multi-agent systems that allow us to envision
    agents with goals and that allow events that the goal-oriented agent
    could respond to according to the rule sets that span the HSBC
    domain;

-   advances in high-performance computing (HPC) resources that can
    assist computationally intensive cases when large numbers of agents
    are required to adequately describe regional stability and when
    aggregation complicates sensitivity analyses;

-   emergence of architectures that can handle 10^8^ agents, and that
    facilitate disaggregation to the level of individuals behaving
    according to modern social theories within community scales
    important to regional stability.

This research thrust has as its central goal the ability to simulate
human behavior in a way that anticipates adverse future developments and
supports alternative course of action assessments to mitigate them. The
objective of the proposed research is to develop the next generation of
social simulations that integrates advances in social science theory and
computational sciences. This new generation of societal simulation will
be characterized by the following attributes:

-   These systems will be independent of the linkage of separate models
    (i.e., a political model linked to an economic model, linked to an
    infrastructure model, etc.) that are limited by aggregation and by
    unrealistic behavior rule sets.

-   The new systems will be able to conduct sensitivity studies of key
    causal effects of an observed change in a region's stability
    (surprises) in reasonable computing times.

-   These systems will allow analysts to intuitively comprehend and
    control the simulation of future event streams and will facilitate
    analysis of historical precursors.

Key to the next generation of social modeling and simulation is the
ability to (a) accurately simulate observed surprise events that
threaten or impact a region\'s stability, (b) model observed historical
events that led to the event with sufficient transparency to allow
insight into causal factors, and (c) use this insight to evaluate DIME
options for influencing emerging events that will increase regional
stability.

## Research Plan

The first phase addresses the feasibility of the five scientific tasks
that are cornerstones of the new simulation system:

1.  Identify and characterize global data sources and granularity needed
    to synthesize and model populations at the level required to support
    the simulation environment. The metrics for this task are as
    follows:

-   Demonstrate, or document the feasibility of, direct use of observed
    or simulated data to seed the behavioral dynamics governing
    heterogeneous agents representing the entire HSBC domain at each
    scale of aggregation.

-   Demonstrate or document the adequacy of available public domain or
    classified data sources that relate directly to individual human
    behavior as well as overall social or economic phenomena, with the
    objective of guiding the formulation of CAS-based models capable of
    generating actionable predictive insights as well as for modeling
    surprising emergent behavior.

-   Demonstrate or document the adequacy of disparate, multiscale, and
    possibly dynamic data sources that can validate the causal patterns
    and the predictive insights extracted from CAS-based models for
    social or economic systems.

-   Demonstrate the feasibility of utilizing methodologies and tools
    that can employ multiscale, multisource, disparate, dynamic, and
    sparse and/or possibly incomplete observations to continually
    improve the simulation models.

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-   Demonstrate the feasibility of utilizing the observed and simulated
    data in a scalable fashion within a realistic modeling and
    simulation environment. Specifically, demonstrate the feasibility of
    enabling the online data-model feedback loop in an environment with
    more than a 100 million agents, with complex behavior based on
    realistic social or economic theory, and with disparate data feeds
    coming in at real-time or near real-time.

> In summary, for the seedling effort, the overall metric for Task 1 is
> the degree of success in providing convincing evidence that the data
> and related tools exist for enabling the rest of the tasks (2--5). 

2.  Identify, compare, and contrast several different theory-driven,
    agent-based modeling approaches, and explore how each could be
    implemented and evaluated. The task will begin by exploring three
    candidate approaches:

-   static, hard-coded rules;

-   dynamic, fitness function-driven, evolvable rules that are
    initialized from state-of-the-art sociological models of human
    behavior;

-   dynamic, evolvable rules that are initialized from random models of
    human behavior.

> The metric for this task will be the model's response time and
> accuracy. Of those that are sufficiently responsive, only the CAS
> models that achieve 80% predictive accuracy will be considered
> adequate or successful.

3.  Prototype the capabilities needed to update the agent environment
    with real-time data, and train the agents so that their emergent
    behavior will mimic real-world events without prescribing the
    eventual result. Following is the metric for this effort:

-   Demonstrate, or document the existence of, a scheme that maps
    real-time data updates to a semantic model understood by the CAS
    used in these experiments.

-   Demonstrate, or document the existence of, a CAS agent design that
    can incorporate real-time data updates.

4.  Demonstrate the ability to account for the context precipitating a
    surprising or emergent historical event, and characterize causal
    factors followed by the development of future doctrinal actions to
    forecast future events. The metrics for assessing the feasibility of
    this task are as follows:

-   Demonstrate, or document the existence of, a CAS design that is
    sufficiently flexible and true-to-life that it can generate
    unexpected results (surprises).

-   Demonstrate, or document the existence of, a data aggregation system
    (ontologies, data mappings, extraction processes) that facilitates
    the review of simulation history to identify potentially causal
    factors.

-   Demonstrate, or document the existence of, a programmatic way to
    generate a text-based (human) narrative summarizing key simulation
    events.

5.  Demonstrate the ability to computationally control and analytically
    manipulate a system with the large number of agents (i.e., 10^8^)
    necessary to model regions at the individual level. The metrics for
    task 5 are listed below:

 

-   Demonstrate, or document the existence of, frameworks of more than
    100 million agents with ruleset complexities on the order of that
    found in Task 1.

-   Demonstrate, or document the existence of, the ability of
    high-performance or grid computing resources to accommodate these
    frameworks.

-   Demonstrate, or document the existence of, the ability to perform
    scenario simulations in less than one day of computational time.

The individual tasks to accomplish these five objectives are listed
below.

***Task 1. Data Characterization***

-   Identify variables, attributes, etc., of data set needed for initial
    modeling

-   Identify sources. What are available and are they sufficient?

-   Determine needed versus available attributes and dimensions

-   Characterize data (reduce, extract, etc.) and populate spreadsheet

***Task 2. Comparison of Three Approaches***

-   Identify and generate the three agent-representation candidates
    (existing social theory, bootstrapped with social theory, emergent
    from primitives)

-   Simulate the above

-   Evaluate and write up

***Task 3. Real-Time Updating***

-   Identify existing ways of online text analysis, real-time news
    feeds, etc., that can be mapped to Goldstein categories

-   Document approaches for turning categorical data into a state vector
    delta

***Task 4. Accounting for Context and Surprise***

-   Train the agents to produce the event, then retrospectively
    determine causal relationships

-   Perform a partial-order temporal analysis of state space to
    determine the evolution of the society

-   Search the literature and generate a narrative from one of the above

-   Perform visual analytics

***Task 5. Scale Test***

-   Survey literature of 10^8^ agent scalability

-   Demonstrate or analyze a similarly scaled, agent-based simulation.

-   Compare the demonstrated environment to the anticipated ABS
    environment.

***Appendix 2 -- Data Sources for Independent Variables***

**Political**

National government

Legislative bodies, characteristics, role and power relative to
executive

Executive roles (president, vice-president, ministers, etc.) and how

> selected, degree to which positions are filled by members of
>
> particular social groups
>
> Representatives (of executive) throughout country: distribution,
> resources, degree to which national government controls outlying
> territory

Descriptors (numbers, resources) for:

> Administrative bodies (agencies for tax collection, welfare,
> regulation, licensing, etc.)

Domestic intelligence agencies including secret police

Voting system for selecting representatives and executive, rate of
turnover

> of individuals in elective office

-   Corruption data from World Resources Institute (WRI): environmental
    governance and institutions:
    <http://earthtrends.wri.org/searchable_db/index.php?theme=10>

-   Politics and Freedom data from WRI:
    <http://earthtrends.wri.org/searchable_db/index.php?theme=10>

-   Data from CIA World Factbook:
    <https://www.cia.gov/library/publications/the-world-factbook/geos/af.html>

-   Economist Intelligence Unit Country Reports

-   <http://en.wikipedia.org/wiki/List_of_legislatures_by_country>

-   Lijphart Elections Archive (<http://dodgson.ucsd.edu/lij/>)

-   International Institute for Democracy and Electoral Assistance
    worldwide electoral systems data
    (<http://www.idea.int/esd/world.cfm>)

-   Trinity College's Electoral systems Web site with its least squares
    (election result disproportionality) index
    (http://www.tcd.ie/Political_Science/Staff/Michael.Gallagher/ElSystems/index.php)

Judicial system

Distribution and characteristics of courts

Method by which judges are chosen

Distribution and characteristics of police

Level of government that controls police

Penal system: distribution of jails; number, characteristics, and
distribution of incarcerated; control over jails

-   Jurist Legal Intelligence World Law Web site
    (<http://jurist.law.pitt.edu/world/>)

-   International Centre for Prison Studies' World Prison Population
    List and Efficiency of Courts measure
    (http://www.prisonstudies.org/)

-   Library of Congress's legal information for the nations of the world
    (http://www.loc.gov/law/help/guide/nations.html)

Subnational governments

Type, distribution, depth (how many levels), resources, degree of formal

> and informal independence from higher levels of government

Rules for suffrage and conducting elections, vote participation,
freeness and

> fairness of elections

-   Lijphart Elections Archive (<http://dodgson.ucsd.edu/lij/>)

-   International Institute for Democracy and Electoral Assistance voter
    turnout data (http://www.idea.int/vt/index.cfm)

Political rights and civil liberties, press freedom, degree to which a
political

> opposition is tolerated and can organize

-   Freedom House's political rights, civil liberties, and press freedom
    indices
    (http://[www.freedomhouse.org/](http://www.freedomhouse.org/))

-   Reporters Without Borders Worldwide Press Freedom Index
    (http://www.rsf.org/article.php3?id_article=24025)

Degree of security and stability

-   CIDCM's Peace and Conflict Instability Ledger
    ([http://www.cidcm.umd.edu/pc/chapter02//](http://www.openbudgetindex.org/))

-   Economist Intelligence Unit/Vision of Humanity's Global Peace Index
    (<http://www.visionofhumanity.com/index.php>)

-   George Mason University's Political Instability Task Force Internal
    Wars and Failures of Governance datasets
    (<http://globalpolicy.gmu.edu/pitf/>)

-   George Mason University's State Fragility Index (in book Global
    Report on Conflict, Governance and State Fragility 2007)

-   Foreign Policy-Fund for Peace's Failed States Index
    (http://www.fundforpeace.org/web/index.php?option=com_content&task=view&id=229&Itemid=366)

Level of corruption with respect to government and the distribution of
where that

> corruption occurs, transparency of government activities

-   Transparency International's Corruption Perceptions Index
    (http://www.transparency.org/policy_research/surveys_indices/cpi)

-   International Budget Project's Open Budget Index
    (http://www.openbudgetindex.org/)

-   International Budget Project's Latin American Index of Budget
    Transparency
    (<http://www.fundar.org.mx/fundar/indicelat/pg/2007/docregionales.php>)

-   Global Integrity's Integrity Scorecard
    (http://report.globalintegrity.org/)

**Military**

Government:

> Different institutions and their functions

-   Peacekeeping operational organizations: United Nations Peacekeeping
    Operations: <http://www.un.org/Depts/dpko/dpko/bnote.htm>

-   Stockholm International Peace Research Institute's FIRST database
    (http://first.sipri.org/index.php/)

Numbers, distributions, bases and their distribution, ethnic composition

-   Monthly summary data of contributors of military and civilian police
    personnel from 1995--2007: United Nations Peacekeeping Operations:
    <http://www.un.org/Depts/dpko/dpko/contributors/>

-   Monthly data of gender statistics in peacekeeping military from
    2006--2007: United Nations Peacekeeping Operations:
    <http://www.un.org/Depts/dpko/dpko/contributors/gender/dec07.pdf>

-   Monthly financing data of United Nations Peacekeeping Operations:
    <http://www.un.org/Depts/dpko/dpko/contributors/financing.html>

-   Yearly fatalities statistics from 1948--2008: United Nations
    Peacekeeping Operations: http://www.un.org/Depts/dpko/fatalities/

> Activities: protect against foreign attack or domestic threats, pursue
> terrorists, economic

-   Peacekeeping activities including military, police, logistics
    support, mine action service, medical support, norms of conduct, and
    contingent owned equipment: United Nations Peacekeeping Operations
    Agency: <http://www.un.org/Depts/dpko/dpko/>

-   Peace agreements (descriptive data only): UN Peacemaker
    <http://peacemaker.unlb.org/advancedsearch.php>

> Characteristics of members: why joined, pay, distribution of important
> membership groups (e.g., ethnic groups), experience, loyalties

Domestic, nongovernment (militias, paramilitary groups, terrorist
organizations):

Size, location, composition in terms of social groups

Allegiances

> Activities: types (protection, bombing, kidnapping, fighting),
> frequencies, locations
>
> Ties to other organizations:
>
> Organizational, informational, or economic ties
>
> To domestic or international, military or political organizations

-   Global Security.org's para-military groups database
    (http://www.globalsecurity.org/military/world/para/index.html/)

Foreign:

> Size, locations, loyalties, activities, ties to other countries, ties
> to other military groups

Conflicts: size, frequency, location, seasonal concentration,
casualties, combatants

-   Data from UN Environment programme GEO Data Portal:
    <http://www.grid.unep.ch/data/index.php>

-   Uppsala Conflict Data Project
    (<http://www.pcr.uu.se/research/UCDP/UCDP_toplevel.htm/>)

-   George Mason University's Armed Conflict and Intervention project
    data (http://www.systemicpeace.org/aci.htm)

**Economic**

Occupational distribution of population

-   Data from international labor organization:
    <http://www.ilo.org/public/english/employment/strat/kilm/>

Sector distribution of economic activity

-   County statistical information database: World Bank datacenter:
    <http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20541648~menuPK:64133152~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html>

-   IMF development and statistics data

-   Industrial statistics from economic statistics of UN statistics
    division: <http://unstats.un.org/unsd/economic_main.htm>

-   Agricultural and food data from WRI:
    <http://earthtrends.wri.org/searchable_db/index.php?theme=8>

Distribution of employment in terms of nature of place of employment

> (government, government-controlled firm, large firm, small business,
> self-
>
> employed)

Rate of change of those three distributions

Job growth in comparison to population growth

-   International Labor Organization's labor statistics database
    (http://laborsta.ilo.org/)

> Principal products (agricultural, manufactured): absolute and
> proportional value, concentration of and location of production,
> location of markets, profit, growth

-   Afghanistan data (and other Asian countries) of population,
    employment, GDP by industrial origin, structure of output, structure
    of demand, at constant 2002 factor cost, money and banking,
    government finance, export and trade, direction of trade, balance of
    payments, international reserves, external indebtedness: Asian
    Development Bank:
    <http://www.adb.org/Documents/Books/Key_Indicators/2007/pdf/afg.pdf>

-   National Account data from UN Economic Statistics:
    http://unstats.un.org/unsd/economic_main.htm

> Largest companies: size, locations, domestic or multinational, ties to
> other companies, ties to government

-   Doing business in Afghanistan 2008 from Doing Business project:
    <http://www.doingbusiness.org/ExploreEconomies/Default.aspx?economyid=2>

-   Trade statistics from UN Economics Statistics:
    http://unstats.un.org/unsd/economic_main.htm

> Financial system: characteristics of central bank (is there one? is it
> autonomous?), links of banks to international firms, distribution
> throughout country, access of population to loans, capitalization

-   Yearly data of external debt, GDP, GNI, life expectancy, population,
    population growth, school, surface area in 2005 and 2006: World Bank
    data center:
    <http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20535285~menuPK:1192694~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html>

-   FDI flows 2004: from UN conference on trade and development:
    <http://www.unctad.org/sections/dite_dir/docs/wir04_fs_af_en.pdf>

-   International debts and governmental expenditure data from WRI:
    <http://earthtrends.wri.org/searchable_db/index.php?theme=10>

> Degree to which economy is integrated with and competitive in global
> economy

-   KOF's Index of Globalization (http://globalization.kof.ethz.ch/)

-   ATKearney-Foreign Policy's Globalization Index
    (http://www.atkearney.com/main.taf?p=5,4,1,127)

-   IMD's World Competitiveness Yearbook
    (http://www.imd.ch/research/publications/wcy/index.cfm)

-   World Economic Forum's Global Competitiveness Index
    (http://www.weforum.org/en/initiatives/gcp/Global%20Competitiveness%20Report/index.htm)

> Degree of economic freedom

-   Heritage Foundation/Wall Street Journal's Index of Economic Freedom
    (http://www.heritage.org/research/features/index/index.cfm)

-   Fraser Institute's Economic Freedom of the World Index
    (http://www.freetheworld.com/reports.html)

> Degree of income and wealth inequality (Gini)

-   World Income Inequality Database
    (http://62.237.131.23/wiid/wiid.htm)

> Extent of business corruption

-   Transparency International's Corruption Perceptions Index
    (<http://www.transparency.org/policy_research/surveys_indices/cpi>)

-   World Bank's Worldwide Governance Indicators
    (http://info.worldbank.org/governance/wgi2007/)

> Extent and concentration of poverty and hunger

-   United Nation's Human Development Report Human Development Index and
    Human Poverty Index (<http://hdr.undp.org/en/statistics/indices/>)

-   CIESIN's Global Poverty Data, Food Security Catalog, Small Area
    Estimates of Poverty and Inequality, and Unsatisfied Basic Needs
    datasets (http://sedac.ciesin.columbia.edu/povmap/)

**Social**

> Demographic (perhaps by region): Age distribution, gender
> distribution, birth rate, infant mortality rate, life expectancy,
> immigration and emigration rates

-   Population, health, nutrition, poverty, gender, World Bank data
    center:
    <http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20535285~menuPK:1192694~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html>

-   Population data from UN statistics division of Demographic and
    Social Concerns: http://unstats.un.org/unsd/demographic/default.htm

> Education: years of education (by region, gender?), type of school
> (religious, vocational, etc.), proportions with professional and
> technical (engineering) training

-   Yearly data of education in 2006: World Bank data center:
    <http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20535285~menuPK:1192694~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html>

-   Education data from UN statistics division:
    http://unstats.un.org/unsd/demographic/default.htm

> Health care: doctors/other health care people/facilities per 1000
> population, nature of health care system (government, private, mixed,
> etc.), geographic distribution of health care facilities/personnel

-   World Bank's Millennium Development Goals dataset
    (http://ddp-ext.worldbank.org/ext/GMIS/gdmis.do?siteId=2&menuId=LNAV01HOME3)

> Family: distribution of family types (nuclear, polygynous,
> single-parent, etc.), distribution of marital statuses (by age)

-   Marriage and divorce data from UN statistics division:
    http://unstats.un.org/unsd/demographic/default.htm

> Minority groups: major identity-defining characteristics (e.g., social
> class, ethnic group, caste, clan, region, language, religion,
> native-born vs. immigrant), relative size of groups, whether minority
> groups are concentrated geographically or dispersed, relative
> circumstances of groups, degree to which minority groups are at risk
> of harm or disappearance

-   University of Maryland's Minorities at Risk dataset
    (http://www.cidcm.umd.edu/mar/)

> Principal social cleavages: dimension(s) of cleavage (as above plus
> age and sex), relative treatment of groups by government, severity of
> discrimination based on identity, intensity of cleavage(s)

-   UN statistics and indicators on women and men:
    http://unstats.un.org/unsd/demographic/default.htm

-   University of Maryland's Minorities at Risk dataset
    (http://www.cidcm.umd.edu/mar/)

> NGOs (e.g., Red Cross): Characteristics such as size, locations,
> resources, foreign links, political involvement and leanings

-   Yearly data of UN houses from 1996 to 2005: United Nations
    Development Group: <http://www.undg.org/index.cfm?P=214>

-   Yearly data of UN country development teams from 2003 to 2005:
    United Nations Development Group:
    <http://www.undg.org/unct.cfm?module=CountryTeams&page=Region&RegionID=RAS>

-   USAID's NGO Sustainability Index for Central and Eastern Europe and
    Eurasia
    (<http://www.usaid.gov/locations/europe_eurasia/dem_gov/ngoindex/>)

-   Duke University's resources Web page regarding NGOs
    (http://library.duke.edu/research/subject/guides/ngo_guide/index.html)

> Religious organizations (e.g, churches, cults): Characteristics such
> as size, locations, resources, foreign links, political involvement
> and leanings, influence over population

-   Adherency.com's religious statistics
    ([http://www.adherents.com](http://www.adherents.com/))

-   Wikipedia Web page on religions by country
    (http://en.wikipedia.org/wiki/Religions_by_country)

> Degree of religious freedom

-   Hudson Institute's Religious Freedom Index (book publication
    imminent)

-   U.S. Dept. of State Annual Report on International Religious Freedom
    (http://www.state.gov/g/drl/rls/irf/)

> Other social movements (e.g., land reform, temperance):
> characteristics such as size, locations, resources, foreign links,
> political involvement and leanings

-   Data sources as below

> Social Safety Net: presence or absence, who is covered, magnitude of
> support

-   U.S. Social Security Administration's Social Security Programs
    Throughout the World database
    (http://www.ssa.gov/policy/docs/progdesc/ssptw/)

> Mechanisms for empowerment and creating opportunities

-   World Economic Forum's Global Gender Gap Index
    (http://www.weforum.org/en/initiatives/gcp/Gender%20Gap/index.htm)

-   Avon Products, Inc.'s Worldwide Women's Poll and Empowerment Index
    (http://shop.avon.com/shop/landing/global_summit/summit_press4.html)

-   United Nation's Human Development Report Gender Related Development
    Index and Gender Empowerment Index
    (<http://hdr.undp.org/en/statistics/indices/>)

> Crime: type; distribution by region, social category, etc.;
> probability of personally experiencing a violent crime

-   Yearly data of human development indices, including income,
    inequality, poverty, education, gender, family, demography, health,
    water, sanitation, nutrition, survival, political participation,
    technology, economic performance, trade, international aid, public
    spending, energy, pollution, environment, refugee, crime, gender
    empowerment, human rights from 1975--2008: United Nations Human
    Development Program: <http://hdrstats.undp.org/countries/>

-   Data UNC JIN statistics and research sources:
    <http://www.uncjin.org/Statistics/statistics.html>

**Infrastructure**

Transportation:

> Roads: network (location), capacity, condition, suitability for
> different types of transport (foot, animal, car, truck, military)
>
> Vehicle ownership: type, concentration in population by location,
> class, etc.
>
> Other transportation: type (animal, foot); patterns in population by
> location, income, etc.
>
> Water transport: network (location), capacity, condition, suitability
> for different types of transport
>
> Rail transport: network (location), capacity, condition
>
> Ports: locations, capacity, seasonal limitations
>
> Air: number, location, and condition of airports; number and type of
> airplanes, control over airports and airplanes

-   Same data sources as below

Communication:

> Telephone networks: distribution of network, distribution of
> telephones
>
> Television: channels/networks, access to channels, distribution of
> TVs, control over content
>
> Internet: distribution of access, government (or other) control over
> content
>
> Print (newspapers, pamphlets): distribution, control over content

-   Same data sources as below

-   Internet, telecommunications, telephone, digital access index, main
    telephone lines:
    <http://earthtrends.wri.org/searchable_db/index.php?theme=10>

> Utilities:
>
> Electrical, water: sources, distribution, capacity, reliability and
> vulnerability, access of population

-   Water resources and freshwater ecosystems data from WRI:
    <http://earthtrends.wri.org/searchable_db/index.php?theme=2>

-   Electricity data from WRI: same as above

> Fuel: type (gasoline, coal, etc.), sources, distribution, capacity,
> reliability and vulnerability, access of population

-   Afghanistan data from World Bank Group from 1990 to 2006:
    <http://ppi.worldbank.org/explore/ppi_exploreCountry.aspx?countryID=149>

-   UN Energy statistics from UN Environment and Energy statistics:
    <http://unstats.un.org/unsd/environment_main.htm>

-   Fuel data from WRI:
    <http://earthtrends.wri.org/searchable_db/index.php?theme=6>

-   Mineral Resources of Afghanistan from UN Economics and Social
    Commission for Asia and the Pacific:
    <http://unescap.org/publications/detail.asp?id=62>

    Note: Asia Society provides complete data for Afghanistan:
    http://www.asiasource.org/profiles/ap_mp_04.cfm

    ***Appendix 3 -- LandScan Methodology***

**Methodology for Dasymetric Modeling for Building Synthetic
Populations**

**Summary**

The LandScan™ Global Population Database describes the 24-hour average
(i.e., ambient) population distribution for every 30 arc-second
(approximately 1 km × 1 km) grid cell covering the world. This database
is produced through an innovative, flexible, and dynamically adaptable
spatial model that refines census data utilizing remote-sensing--derived
earth observation data and geographic information systems (GIS)
technology. The LandScan algorithm incorporates spatial data and imagery
analysis technologies and a multivariable dasymetric modeling approach
to disaggregate census counts within an administrative boundary. Since
no single population-distribution model can account for the differences
in spatial data availability, quality, scale, and accuracy as well as
the differences in cultural settlement practices, LandScan population
distribution models are tailored to match the data conditions and
geographical nature of each individual country and region. Improvements
to the population distribution data using new spatial data, imagery,
census information, and algorithm enhancements are made annually.
High-resolution imagery is used to refine population distributions and
correct spatial data errors.

**Introduction**

The LandScan global population distribution model is a multilayered,
dasymetric, spatial modeling approach that is also referred to as a
"smart interpolation" technique. Dasymetric modeling is analogous to
areal interpolation \[a technique by which data from one set of polygons
are redistricted onto a set of overlapping, but nonhierarchical target
polygons (Gregory 2002)\] except that ancillary spatial data aids
interpolation. Typically, in dasymetric mapping, source layers in the
region are first transformed into a surface lattice. Rather than using
purely mathematical algorithms to interpolate lattice cell values from
known data associated with the original source zones, an ancillary data
layer is added to the lattice and a weighting scheme is applied to cell
counts according to known or derived density levels associated with
values in the ancillary data.

In LandScan, the typical dasymetric modeling is improved by integrating
and employing multiple ancillary or indicator data layers. The LandScan
modeling process involves the collection of the sub-national level
census counts for each country and primary geospatial input or ancillary
datasets, namely land cover, roads, slope, and urban areas and village
locations and high resolution imagery analysis; all of which are key
indicators of population distribution. Relationships between any of
these datasets and population distribution are not globally uniform. For
example, roads play a critical role in human settlements, independent of
other forms of transport. However, residential population density in
proximity to major roads varies significantly across the world. Such as,
residential population is much higher near primary roads in south Asia
countries, where the opposite is true in the United States. Similarly,
the tendency to reside on steeper slopes rather than on gentler (or
flat) terrain is generally a function of the abundance of available
areas with gentler slope as well as local climatic regimes. Based on
this variability in cultural and settlement geography, each country is
considered to have a unique set of settlement characteristics.

Within each country, the population distribution model calculates a
"likelihood" coefficient for each LandScan cell and applies the
coefficients to the census counts, which are employed as control totals
for appropriate areas. For example, in the United States, census blocks
serve as the polygonal unit or control population but generally census
data are aggregated to and are associated with an administrative
boundary. These administrative boundaries are divided into finer grid
cells (1 km), and each cell is evaluated for the likelihood of being
populated based on its geospatial characteristics. Based upon the land
cover category (such as agricultural or commercial) and upon the
socioeconomic and cultural understanding of an area, cells are
preferentially weighted to accommodate the likelihood of the presence of
workers. This represents a 24-hour average population rather than just a
residential population. The total population for that area is then
allocated to each cell proportionally to the calculated population
coefficient, which represents the likelihood of any cell being
populated. The resultant population count is an ambient or average
day/night, population count.

**Ambient Versus Residential Population**

Commonly available census population data are severely constrained both
in space and time and do not capture the population dynamics as
functions of space and time. Indeed, from a spatial perspective, census
data are limited by census accounting units, such as blocks, where often
there is great uncertainty about the spatial distribution of residents
within those accounting units. This is particularly true in suburban and
rural areas, where the population is dispersed to a greater degree than
in urban areas. From a temporal perspective, census counts represent
"residential" or "nighttime" population, and their use in a daytime
event is highly inaccurate. Because of this uncertainty, there is
significant potential to misclassify people with respect to their
location in relation to hazards, for example, pollution sources.
Consequently, it becomes extremely challenging to determine, with the
required accuracy, whether certain subpopulations are more likely than
others to get differential environmental exposure. To a large degree,
these limitations can be overcome by developing population data with a
finer resolution in both space and time at sub-census levels. ORNL has
developed a very high resolution (90 meter) diurnal (nighttime
residential and daytime) population distribution database for the U.S.
(LandScan USA). However the goal of LandScan is to produce a
distribution of an "unwarned" or 24-hour average population.

The LandScan data represents an average or ambient population that
integrates diurnal movements and collective travel habits into a single
measure. This is desirable for purposes of emergency response. Consider,
for example, the hypothetical case of a cell with a major, multilane
highway passing through a desert. If a nuclear, biological, or chemical
(NBC) release contaminates the cell, the potential exists for many lives
to be at risk even though no one lives there. Most official census
counts, if available at such fine resolution, would show zero population
because most national censuses are concerned with residential population
based primarily on where people sleep rather than where they work or
travel. In the LandScan procedure, population will be apportioned to the
cell based on the presence of ambient highway traffic. Consider another
cell containing a large agricultural field and no houses. Most censuses
would place farm workers in their village residences and record zero
populations for their fields. Yet, a few lives are at risk in the
fields, depending on when the NBC release occurs. Hence, our procedure
will show population in the agricultural cell (based on the region's
agricultural practices) and a slightly reduced population in the village
to suggest, albeit imprecisely, the collective time that some villagers
are in their fields rather than their homes. For certain nations, even
arid grassland cells will have sparse populations assigned to simulate
the movements of nomads and other herders. The same can be said of
factories, airports, and other places of work and travel.

**Census Counts**

All population counts, even the most sophisticated high-resolution
official censuses of advanced nations like the United States, are
stochastic estimates. Accuracy and precision are limited by the census
takers\' access to homes and even to whole neighborhoods; by the census
takers\' understandings of personal work and travel habits; and by the
frequency with which censuses can be undertaken. These limits are
exacerbated in many nations by lack of resources and, all too often,
outright manipulation of census figures to meet political objectives. In
addition, many nations are reluctant to release detailed census counts.
The variable quality of census figures from country to country presents
a major challenge to global population distribution efforts such as
LandScan. The International Program Center (IPC) at the U.S. Bureau of
Census provides ORNL updated census data at the sub-national
administrative level. Individual nations conduct their census at
different intervals, thus "new" census information is acquired by the
IPC each year. For inter-census years, the IPC is tasked for producing
the midyear population projection for each subnational administrative
area. These projections become the "official" U.S. estimate of
international populations and are supplied to ORNL as a spreadsheet.
Certain national census estimates provided by other organizations (e.g.,
United Nations) may differ from those produced by IPC. However, all
census counts incorporated into the models used by LandScan are produced
by the IPC.

**Input Data** **and Data Management**

In addition to census data, large volumes of high-resolution
satellite-derived spatial data as well as traditional mapping data are
used during the LandScan modeling process and during the verification
and validation (V&V) of the model results. Datasets include

-   1 m and 5 m satellite imagery (CIB01 and CIB05),

-   High Resolution Commercial Satellite Imagery (Ikonos and Quickbird),

-   Derived land cover \[MODIS\]

-   Shuttle Radar Topography Mission \[SRTM\] formats 1 and 3,

-   Digital Terrain Elevation Data \[DTED\] levels 1 and 2,

-   Vector Smart Map \[VMAP\] levels 1 and 2,

-   Urban Vector Smart Map \[UVMAP\] data,

-   GeoCover Land Cover data,

-   Coastal Change Analysis Program \[CCAP\] Land Cover Data,

-   National Land Cover Database \[NLCD\] data, and

-   Compressed ARC Digitized Raster Graphics \[CARDG\] data.

**Spatial Data Input Variables**

***Vector Data***

-   Transportation networks (i.e., roads, railroads, airports, and
    navigable waterways) are primary indicators of population. As a
    single indicator, roads are preferred because of their vital role in
    human settlements with or without other forms of transport.

-   Populated places include polygonal outlines of urban areas as well
    as point locations of smaller towns and villages. Attributes for
    populated places data may provide the name but not the population
    associated with each location.

***Raster Data***

-   Elevation and slope are important variables in calculating the
    LandScan population probability coefficient because most human
    settlements occur on flat to gently sloping terrain. Even in regions
    noted for hillside settlement, relative measures of slope may
    correspond (inversely) with population density. Rather than
    calculating an average slope value for each 30 second cell which may
    mask many small relatively flat areas, a slope "index" is produced
    by processing higher resolution elevation data into discrete slope
    categories. This methodology captures the percentage of land
    preferable for habitation within each 30 arc-second cell.

-   Land cover is perhaps the best single indicator of population
    density. In most regions population would range from extremely low
    densities in desert, wetlands, ice, or tundra land cover to high
    densities in developed land cover associated with urban land use.
    Arid grasslands, forests, and cultivated lands would range in
    between. LandScan analysts assign relative weights to each land
    cover type based upon the region's cultural settlement patterns and
    agricultural intensity practices, and employ these weights in
    calculating the probability coefficient for each cell. Most land
    cover data used in LandScan was processed using Landsat Thematic
    Mapper (TM) at a resolution of 30 m^2^. For certain areas where no
    TM-derived data is available, land cover information is derived from
    the Moderate Resolution Imaging Spectroradiometer (MODIS) at a
    nominal 1 km^2^ resolution. Even at 1 km^2^ resolution, land cover
    can be a good indicator of relative population density, and its
    efficacy improves as resolution approaches the typical size of
    individual home sites.

***High-Resolution Imagery***

-   High-resolution imagery is incorporated throughout the LandScan
    population distribution modeling process and facilitates the
    assignment of relative weights to land-cover and slope categories
    for each particular region. Imagery is used to update or correct
    existing land-cover information and includes capturing the spatial
    extents of growing urban areas as well as correcting spatial data
    anomalies such as town and village precision errors or omissions.

***Exclusion Areas***

-   Some areas, such as cells whose land cover are composed of 100%
    water as well as islands with no permanent inhabitants, are excluded
    from the ambient population distribution although there may be
    people in these locations for a short period of time.

***Administrative Boundaries and Coastlines***

-   Considerable effort is required to reconcile the positional accuracy
    of diverse global databases, and mismatches among databases are most
    conspicuous on administrative boundaries and coastlines. Accurate
    administrative boundaries (especially at the 2^nd^ order---e.g.,
    subprovince) that are associated with the census data as control
    totals remain problematic for much of the world. Many digital
    versions of sub-national boundary information exist, but none are
    authoritative. In fact, when many of these boundaries are overlain
    on geo-rectified imagery it can be seen that spatial precision
    and/or attribute correctness is lacking. ORNL continually searches
    for, and in some cases creates, more precise 2^nd^ order or higher
    administrative boundaries. The same precept hold true for
    coastlines. Typically, a coastline differs somewhat from the related
    line representing the seaward boundary of administrative units. For
    this reason, all coastal administrative boundaries used in LandScan
    are extended seaward to avoid omitting any coastal land.

**Verification and Validation (V&V)**

Verification of spatially explicit global population databases is
inherently limited by the difficulty of establishing a suitable
reference database for purposes of comparison. The ideal reference
database would be actual census counts for sample areas at the same
resolution or finer resolution than the database being evaluated, in
this case 30\"$$`<!-- -->`{=html}30\" cells. A traditional validation or
ground-truthing of the LandScan data is implausible due to the
tremendous resources necessary for such a process as well as the
logistical issues of replicating a "census" for a foreign state. Such a
comparison may be feasible for certain countries that collect and
disseminate high resolution (e.g., block or tract level) census data,
but this applies only to urbanized areas of the United States, certain
other advanced countries, and a few less developed countries.
Unfortunately, suitable reference data are least likely to be available
for unstable hostile regions where improved population distributions are
most needed for government planning, i.e., to assess risks to military
and diplomatic personnel and civilian populations or to provide
humanitarian relief.

However, there are some measures by which qualitative assessments of the
LandScan data may be made. By examining the characteristics
(scale/resolution, date, etc.) of the various LandScan model input data
layers, relative comparisons are possible that enable a general
"confidence level" to be considered for assorted processing tasks. V&V
activities include tests of (a) consistency with ancillary data and/or
(b) surrogate analysis of similar areas. The accuracy of spatial models
such as LandScan is tied to the resolution and accuracy of the data
layers used in the model. New digital spatial data and imagery are
created on a daily basis and the LandScan model attempts to incorporate
as much new spatial data as time and budget constraints allow. However,
these data are often not consistent in scale, resolution, spatial
precision, or attribute accuracy on a global basis, and in fact, may
vary considerably within a country. For this reason, the "confidence
level" of the LandScan population distribution varies from country to
country.

LandScan receives all population and demographic information from the
International Program Center, Bureau of the Census (IPC). The IPC
acquires foreign census information and further validates and
extrapolates the population information to produce a midyear estimate at
the province or sub-province level. LandScan uses a normalization
process that guarantees that the population counts distributed
throughout an administrative boundary will reflect exactly the number
reported by IPC for that coinciding area. However, individual countries
conduct their census at diverse intervals and to various level of
detail. The mid-year estimates produced by IPC may not reflect certain
population movements \[e.g., migrations, internally displaced persons
(IDPs), refugees, etc.\] since the last official census conducted by a
country. In addition, the administrative boundary level by which the
census data is distributed varies considerably in size and spatial
precision from country to country. Generally, the LandScan model will
reflect population distributions more accurately for those areas with
smaller and spatially precise administrative boundaries.

Most digital spatial data and imagery used in the LandScan model are
produced by or for the NGA and supplied as input to the LandScan model.
These data (e.g., land cover, elevation/slope, roads, urban areas,
village locations, etc.) vary considerably in resolution and accuracy
throughout the world. For example, derived land cover may vary in type
and scale from the MODIS at a nominal 1 km^2^ resolution to that derived
from the Landsat Thematic Mapper (TM) at a resolution of 30 m^2^ to that
derived from submeter resolution imagery analysis. However, producing
accurate land-cover data at a high resolution is a difficult and
time-consuming process, and for many areas of the world the land-cover
data reflect imagery that is now many years old. Thus many rapidly
growing urban areas are not adequately captured in the land-cover data
and must be interpreted manually using high-resolution imagery. Vector
information included in datasets such as the VMAP1 series include data
such as roads, urban boundaries, and village locations and are digital
representations of a paper map series (i.e., 1:250,000 scale). Thus the
accuracy and currency of the VMAP data not only depends upon the raster
and vector processes used to produce the VMAP data, but also upon the
accuracy of the original compilation of the paper map. Therefore, data
precision and completeness varies by the individual maps and can be seen
both between and within various VMAP data tiles. Generally, the LandScan
model will reflect population distributions more accurately for those
areas with recent and higher spatial resolution.

**Results**

As access to increasingly better input data from the NGA (and its
predecessors) has improved, so has the modeling and model results
validation. Indeed, the use of quality input datasets results in a
superior population distribution. ORNL has included the use of very
high-resolution imagery during the model results validation process, and
each year, several entire countries are validated by using 1 m and 5 m
imagery products and by conducting photographic interpretation of the
countries. As part of the validation effort, all countries are provided
with photographic interpretation of the data. This effort ensures that
the model has appropriately assigned population to cells. It also
locates previously undocumented village locations across entire
countries and helps to locate the true urban zones. Other validation
datasets are used in a similar fashion. ORNL uses local land-cover
datasets developed by organizations in-country to aid in the validation
of the LandScan modeling results.

***Appendix 4 -- Narrative Description of Key Data Sources***

### Granular Placement of Social Atoms---The National Geospatial-Intelligence Agency 

### The National Geospatial-Intelligence Agency (NGA) is responsible for the USG programs in GEOINT ( GEOspatial INTelligence). GEOINT is an [**intelligence discipline**](http://en.wikipedia.org/wiki/List_of_intelligence_gathering_disciplines) comprising the exploitation and analysis of [**satellite**](http://en.wikipedia.org/wiki/Satellite) information to describe, assess, and visually depict physical features and geographically referenced activities on the Earth. As a beginning, we will describe the ability to place social atoms geospatially within OCONUS regions of U.S. interest. 

The LandScan^TM^ dataset comprises a worldwide population database
compiled on a 30\"×30\" latitude/longitude grid. Outside the United
States, the census region corresponds to grids about one kilometer on a
side. Census counts (at subnational level) were apportioned to each grid
cell based on likelihood coefficients, which are based on proximity to
roads, slope, land cover, nighttime lights, and other all-source
information. LandScan has been developed as part of the [Oak Ridge
National Laboratory (ORNL)](http://www.ornl.gov/) Global Population
Project for estimating ambient populations at risk.

The LandScan dataset files are available via the internet in
[ESRI](http://www.esri.com/) grid format by continent and for the world,
and in ESRI raster binary format for the world. Figure A4-1 provides a
country-by-country assessment of the country coverage.

**Figure** **A4-1**. The LandScan quality map represents data quality on
a nation by nation basis.

![](media/image263.wmf){width="6.746527777777778in"
height="3.310416666666667in"}

LandScan datasets are released annually. In the most recent (fourth)
version, NASA\'s earth observing system (EOS) moderate resolution
imaging spectroradiometer (MODIS) land-cover database was used for input
data. The MODIS land-cover data replaced an earlier AVHRR-derived global
land-cover database. The land-cover data was refined substantially using
National Imagery and Mapping Agency\'s (NIMA) VMAP-1, CIB, and/or
scanned maps, CADRG/ADRG. Accurate administrative boundaries are an
integral part of the LandScan population distribution modeling process.
ORNL received or developed an additional 700 international second-order
administrative boundaries that were used for population distribution.
For certain countries, the precision of the administrative boundaries
were improved. These boundaries coincide with new census counts provided
by the International Programs Center (IPC) of the United States Bureau
of the Census and represent the most recent census for all countries.
New and revised National Imagery and Mapping Agency\'s (NIMA) VMAP-1
tiles were incorporated, continuing the improvement of road networks,
populated places, and waterbodies. Also, new and revised NIMA\'s DTED-1
tiles were added, thus enabling us to calculate how much land within
each cell is suitable for settlement. ORNL made extensive use of NIMA\'s
Controlled Image Base (CIB) imagery for verification and validation and
for refining urban built-up areas. Additional algorithm refinements were
developed and implemented for the population models.

***The Afghan Example***

An example of the LandScan results for our Afghan case study is shown in
Figure A4-2. Even though the data quality for Afghanistan is described
as below average, census data is currently being collected in
Afghanistan, illustrating that placement of social atoms within one
kilometer grids is feasible even in areas that are data sparse.

**Figure** **A4-2**. 2002 Afghan Population Density

![](media/image264.wmf){width="6.246527777777778in"
height="4.394444444444445in"}

**Data Description of Nongovernmental Agencies**

To address the location, efforts, and nature of nongovernmental agencies
(NGOs), informal groups, and indicators of actions and intentions, we
will describe long-term data collection programs that show the
feasibility of obtaining the required data and then show selected
examples for the Afghanistan case study.

***Database of Archives of Non-Governmental Organizations (DANGO)***

The database of archives of NGOs since 1945, DANGO is a UK on-line, free
resource, enabling researchers to identify NGOs and then access both
existing and new information about the content, location, and
accessibility of archival holdings relating to those bodies. DANGO is an
Arts and Humanities Research Council (AHRC)-funded project that will be
maintained until at least 2012. Information can be accessed through the
DANGO Web site where the prototype database is updated on a daily basis.

Database users can search for NGOs in various ways, such as by name and
areas of interest. Links to on-line catalogues are provided, as well as
summaries of information held elsewhere, like the NRA's catalogue
collection, with the aim of bringing together everything that is
currently in the public domain. All the essential information for
researchers such as location, extent, and access conditions is also
provided.

***Office of the United Nations High Commissioner for Refugees***

Because regional stability issues often revolve around human migration
(Darfur, Iraq, Afghanistan, Bosnia, Kosovo, Israel-Palestine, Lebanon),
NGO activities in these areas are important additions to direct
humanitarian activities. The following provides an overview of the
activities of Global Migration Group (GMG) members in the fields of
migration- and development-related policy research, data-collection and
publications.

The GMG is an inter-agency group bringing together heads of agencies
that seeks to promote the wider application of all relevant
international and regional instruments and norms relating to migration,
and to encourage the adoption of more coherent, comprehensive, and
better coordinated approaches to the issue of international migration.
The GMG consists of 10 organizations that are actively involved in
international migration and related issues. The following four
organizations are the most important for the purposes of the ASCES and
are described in the sections below:

International Labor Organization (ILO)

United Nations Department of Economic and Social Affairs (UNDESA)

United Nations Population Fund (UNFPA)

World Bank

> **International Labor Organization (ILO)**
>
> Collection of data and statistics on the international migration of
> workers forms part of a comprehensive body of labor and employment
> information compiled by the office. This makes it possible to relate
> migration information to broader employment and labor market issues
> facing countries. ILO's work is divided into several areas:
>
> • Contribution to methodology and guidelines for developing migration
> statistics: The ILO has developed comprehensive manuals and guidelines
> for collecting migration statistics. The ILO serves on interagency
> expert committees on improving migration and related statistics. It
> has developed a migration module to be attached to existing household
> surveys on the labor force, and is testing it in certain countries.
> Particular attention is paid to the measurement of migrant
> remittances. The goal is to create questions that effectively measure
> various dimensions of labor migration in both source countries and
> destination countries and can be periodically added to household
> surveys. The module is currently being tested in both Armenia and
> Thailand.
>
> • Global estimates on labor migration and trafficking: ILO made an
> estimate of the number of migrant workers globally and regionally
> based on UN estimates of migrant stocks for the year 2000. These
> estimates are currently being updated. The ILO (Program for Promotion
> of the Declaration) has made a minimum estimate of the incidence of
> forced labor at the global and regional levels, including those
> trafficked.
>
> • Databases on labor migration information and statistics: The online
> International Labor Migration Database, which has been operational
> since 1999, consists of statistics on labor migration for 86 member
> states, compiled in cooperation with EUROSTAT for Europe and through a
> questionnaire sent to other countries.
>
> The information collected and research outputs are disseminated
> through special publications and reports, working papers (mainly the
> series on International Migration Papers and Perspectives in Labor
> Migration), presentations at various conferences/workshops,
> newsletters, and the ILO Web. Most studies and publications can be
> accessed and freely downloaded from the MIGRANT Web site.
>
> **United Nations Department of Economic and Social Affairs (UNDESA)**
>
> The Population Division of UNDESA monitors global international
> migration trends and policies. It carries out research and publishes
> reports on international migration and development and maintains
> various databases. In the context of preparing the estimates and
> projections of the world population, it produces estimates of net
> migration flows. The following UNDESA compilations are currently
> available:
>
> • International migrant stock: This database contains estimates of the
> number of international migrants by sex in each country or area of the
> world as of 1 July 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995,
> 2000, and 2005. The estimates are derived mostly from data on the
> number of foreign-born persons enumerated by censuses. Estimates of
> the number of female migrants and refugees are also included.
>
> • Net migration estimates: Estimates and projections of net
> international migration for each country of the world and for each
> five-year period from 1950 to 2050 are available as part of the set of
> population estimates and projections released under biennial revisions
> of World Population Prospects. The most recent is the 2004 Revision.
>
> The Population Division publishes the International Migration Report
> on a periodic basis. The report presents relevant data on
> international migration trends and policies for each country of the
> world. It also issues a biennial report on World Population Policies
> providing an overview of governments' views and policies on
> population, including those relative to international migration. The
> 2005 edition is the most recent, covering the period 1976 to 2005.
>
> The Population Division carries out research on the various aspects of
> international migration, including its demographic implications. The
> report entitled Replacement Migration, for instance, explores the
> potential impact of international migration in countering population
> ageing.
>
> The UNDESA Statistics Division collects and disseminates statistics on
> stocks and flows of international migrants. It uses the Population
> Census Questionnaire to compile census data on the number and
> characteristics of foreign-born and foreign persons enumerated by
> censuses. The data collected are disseminated via the Demographic
> Yearbook. As of early October 2006, 71 countries had reported data on
> the native and foreign-born population by age, sex and urban/rural
> residence for censuses taking place from 1985 to 1994, and 62
> countries had done so for censuses carried out from 1995 to 2004. The
> population by country of citizenship and sex had been reported by 71
> countries for censuses occurring from 1985 to 1994 and by 53 countries
> for censuses with dates ranging from 1995 to 2004. The foreign-born
> population by country of birth, age, and sex had been reported by 61
> countries with censuses taking place between 1985 and 1994 and by 47
> countries with censuses occurring between 1995 and 2004. The
> foreign-born population enumerated in a country who had lived abroad
> five years before the census, classified by country of birth, age, and
> sex, was reported by 17 countries each for the periods 1985--1994 and
> 1995--2004. The economically active foreign-born population by
> occupation, age, sex, and urban/rural residence was reported by 29
> countries with censuses dating from 1985 to 1994 and by 24 censuses
> dating from 1995 to 2004.
>
> **United Nations Population Fund (UNFPA)**
>
> UNFPA supports collection, analysis, and dissemination of good-quality
> and timely age- and sex-disaggregated migration data for
> evidence-based decision-making and policy formulation. UNFPA supports
> operational research that fosters increased understanding of the
> migration process and addresses the needs of migrants, especially
> female migrants.
>
> **World Bank**
>
> The World Bank is developing its research and statistical activities
> with a focus on enhancing the quality of the policy debate on
> migration and development. These efforts include activities on data
> collection, processing and reporting on migration and remittances. The
> analytical work is exploring in detail issues related to gender,
> subregional migration, poverty, social security, and trade, among
> other topics.
>
> As part of its research program, the Bank is piloting different
> instruments for collection of information on migrants and their
> behavior. These include

-   separate migration modules in household surveys, such as those
    already implemented in Morocco, Guatemala, and Ghana; and

-   alternative sampling designs for the migrants' survey to better
    capture the migrant population.

> The database was recently expanded to include information on the age
> of migrants and the location of their education. A specific focus on
> the health sector includes data on the bilateral migration of medical
> personnel between developing countries and OECD countries.

## International Programs Center Demographic and Socioeconomic Research Services 

## The International Programs Center (IPC) of the U.S. Bureau of the Census conducts studies covering a wide range of demographic, socioeconomic, and health issues. These include 

-   population projections and trends for all countries of the world and
    selected subnational areas,

-   tracking and projecting HIV/AIDS prevalence and assessing the
    demographic consequences,

-   economic and social status of populations in transition to market
    economies, and

-   the role and status of women.

**Afghanistan Case Study**

The development of the data for the Afghanistan case study draws on the
above generally available global databases. However, the United Nations
Office for Coordination of Humanitarian Affairs (UNOCHA) has undertaken
a program to synthesize available data for Afghanistan, and we will draw
from their synthesis here.

The UNOCHA effort had its origin in the Project Management Information
System (ProMIS) that was started in Islamabad, Pakistan under direct
supervision of the U.N. Food and Agriculture Organization (FAO) in 1997.
The purpose was to enhance the capacity of UN Agencies, NGOs, and the
donor community to undertake analysis and planning for operational
activities as well as common programming for Afghan rehabilitation and
development activities. The ProMIS data center in Islamabad produced,
maintained, and distributed data and information related to Afghanistan.

After 11 September 2001, UNOCHA proposed to utilize the information
developed by ProMIS in conjunction with a new Humanitarian Information
Center (HIC). An agreement between the U.N. Development Program (UNDP),
FAO, and UNOCHA allowed the three agencies to combine resources for
delivering information management services to the humanitarian
community. This new project was named HICFA (Humanitarian Information
Center for Afghanistan).

Subsequently the Afghanistan Information Management Service (AIMS) was
established in late 2001 as a joint venture between UNDP and UNOCHA
through the merging of two projects--- the Humanitarian Information
Center for Afghanistan (HICFA) and ProMIS. At the same time AIMS
established regional offices in collaboration with the U.N. Assistance
Mission in Afghanistan (UNAMA) in five Afghan cities.

Throughout 2002 and into 2003, AIMS continued to provide a variety of
GIS, database, and information management services to the government and
humanitarian community. In 2003 AIMS became a Directly Executed (DEX)
project of UNDP, administered and managed by UNDP and reporting to the
Resident Representative with a new mandate of building information
management capacity in the government of Afghanistan.

***Example Data***

The AIMS data profiled each of the 388 Afghan districts, creating a
series of district profiles. Each profile lists the province and
district; representative villages; ethnic composition; returnee
population; leadership ethnicity and party; government departments;
leadership; NGOs including UN, national, and international groupings;
employment; housing stock; and access to basic social services. An
abbreviated example of a district profile is reproduced below.

**Table A4-1**. Example district profile
![](media/image265.wmf){width="6.561805555555556in"
height="8.379166666666666in"}

These district profiles can be coalesced into general maps of NGO
identification, location, and activities necessary for the ACSES
modeling effort. A 2003 example from the Afghan Information Management
Services , (<http://www.aims.org.af/>) ,is reproduced in Figure A4-3.

**Figure A4-3**. Local NGO sectoral activities from
<http://www.aims.org.af/>

![](media/image266.wmf){width="7.208333333333333in"
height="5.031944444444444in"}

Considering the data programs gathering NGO and leadership data on a
global scale, it appears feasible to identify, enumerate, and distribute
people, organizations, and governmental institutions through the
simulation system according to the communities and mixing groups
necessary to develop causal factors and actionable future courses of
action. However, once the agents are distributed among the communities
and mixing groups, we must rely on global data-collection efforts to
populate the agents with beliefs.

**Worldwide Surveys of Community and Mixing-Group Beliefs**

The programs available to develop and maintain databases of beliefs,
attitudes, and intentions for the assumed communities and mixing groups
must be

-   worldwide in scope,

-   collected with common and consistent methodology, and

-   maintained with known currency.

    To illustrate what is available, we will examine an example of a
    worldwide sampling program.

    ***Gallup World Poll***

    The Gallup World Poll maintains the capability to survey attitudes
    and beliefs in the countries listed below. The Gallup Organization
    is entirely responsible for the management, design, control, and
    funding of the poll. Any individual, institution, or governmental
    agency may access the database, regardless of nationality.

    **Table A4-2**. Gallup World Poll coverage from www.Gallup.com

    ![](media/image267.wmf){width="5.620138888888889in"
    height="4.772916666666666in"}

The Gallup World Poll uses two primary methodological designs:

• A Random-Digit-Dial (RDD) telephone survey design is used in countries
where 80% or more of the population has landline phones. This situation
is typical in the United States, Canada, Western Europe, Japan,
Australia, etc.

• In the developing world, including much of Latin America, the former
Soviet Union countries, nearly all of Asia, the Middle East, and Africa,
an area frame design is used for face-to-face interviewing.

The following are key aspects of the overall Gallup World Poll survey
philosophy:

• The sample represents all parts of each country, including all rural
areas. Countries are reviewed on a case-by-case basis when part of a
country cannot be included in the sample design. The review determines
whether the survey should be carried out.

• The target population includes all individuals aged 15 and older.

• Face-to-face interviews are approximately one hour in length.
Telephone interviews are considerably shorter, about 30 minutes in
length.

• A standard set of questions is used worldwide.

• In the parts of the world where face-to-face surveys are conducted,
the questionnaire includes questions tailored to each region. For
example, the questions used in heavily indebted poor countries are
tailored toward providing information about progress on the

Millennium Development Goals.

• The questionnaire is translated into the major languages of each
country.

• Interviewing supervisors and interviewers are trained, not only on the
questionnaire, but also on the execution of field procedures. This
interviewing training usually takes place in a central location.

• Quality control procedures are used to validate that correct samples
are selected and that the correct person is randomly selected in each
household.

Random respondent selection uses either the latest birthday method or
the Kish Grid. The typical World Poll survey in a country consists of
1000 completed questionnaires. However, in some countries, oversamples
may be collected in major cities. For example, we collected an
additional 500 interviews in Moscow.

In countries where face-to-face surveys are conducted, census listings
of Primary Sampling Units (PSU), consisting of clusters of households,
are the main way of selecting the sample. Typically, the PSU are
stratified this way:

> I. Cities with population = 1,000,000 or more
>
> II\. Cities with population = 500,000 to 999,999
>
> III\. Cities with population = 100,000 to 499,999
>
> IV\. Cities with population = 50,000 to 99,999
>
> V. Towns with population = 10,000 to 49,999
>
> VI\. Towns/Rural villages with populations under 10,000

PSU are proportionally allocated to the population in each stratum and
typically 125 PSU are sampled with an average of eight interviews, one
interview per sampled household, per PSU. If maps of the PSU are
available, then they are used; otherwise, the selected PSU must be
mapped. Random route procedures are used to select sampled households.

**Available Indices Routinely Generated**

In addition to the worldwide gathering of specific questions needed for
instantiation of a specific region, indices of beliefs, intentions, and
attitudes and perceptions are routinely generated. A full listing is
presented below. This broad database could support a wide range of HSBC
models and would allow flexibility in our choice of model.

**Table A4-3**. Performance indices of the World Path from
www.Gallup.com.

![](media/image268.wmf){width="6.834027777777778in"
height="7.871527777777778in"}

![](media/image269.wmf){width="6.749305555555556in"
height="5.134027777777778in"}

***Appendix 5 --*** Dimension Data Quality by Country

  --------------- ------------- ------------ ------------ ---------- ----------------- ------------------
  *Country*       *Political*   *Military*   *Economic*   *Social*   *Informational*   *Infrastructure*

  Andorra         3             0            3            3          3                 3

  United Arab     3             3            3            2          3                 3
  Emirates                                                                             

  Afghanistan     2             3            1            2          2                 3

  Antigua &       3             3            3            3          3                 3
  Barbuda                                                                              

  Anguilla        3             3            3            3          3                 3

  Albania         2             3            2            2          3                 2

  Armenia         2             3            2            2          2                 2

  Netherlands     3             3            3            3          3                 3
  Antilles                                                                             

  Angola          1             1            1            1          1                 1

  Antarctica      0             0            0            0          0                 0

  Argentina       3             3            2            2          2                 2

  American Samoa  3             3            3            3          3                 3

  Austria         3             3            3            3          3                 3

  Australia       3             3            3            3          3                 3

  Aruba           3             0            3            3          3                 3

  Azerbaijan      1             2            2            1          2                 1

  Bosnia and      2             2            2            2          2                 1
  Herzegovina                                                                          

  Barbados        3             3            3            3          3                 3

  Bangladesh      3             2            1            2          2                 1

  Belgium         3             3            3            3          3                 3

  Burkina Faso    1             1            1            1          1                 1

  Bulgaria        2             2            2            2          2                 2

  Bahrain         2             2            3            2          3                 3

  Burundi         1             1            1            1          1                 1

  Benin           1             1            1            1          1                 1

  Bermuda         3             3            3            3          3                 3

  Brunei          3             3            3            3          3                 3
  Darussalam                                                                           

  Bolivia         2             2            2            2          2                 2

  Brazil          2             3            3            2          2                 2

  Bahama          3             3            3            3          3                 3

  Bhutan          2             1            1            1          1                 1

  Burma (no                                                                            
  longer exists)                                                                       

  Bouvet Island   1             1            1            1          1                 1

  Botswana        3             3            3            3          3                 3

  Belarus         3             3            3            3          3                 3

  Belize          1             1            1            1          1                 1

  Canada          2             2            2            2          2                 2

  Cocos (Keeling) 1             1            1            1          1                 1
  Islands                                                                              

  Central African 2             1            1            1          1                 1
  Republic                                                                             
  --------------- ------------- ------------ ------------ ---------- ----------------- ------------------

  ---------------- ------------- ------------ ------------ ---------- ----------------- ------------------
  *Country*        *Political*   *Military*   *Economic*   *Social*   *Informational*   *Infrastructure*

  Congo            1             1            1            1          1                 1

  Cook Islands     3             3            3            3          3                 3

  Chile            3             3            3            3          3                 3

  Cameroon         2             2            2            2          2                 2

  China            3             2            2            2          3                 3

  Colombia         3             2            3            3          2                 3

  Costa Rica       3             0            3            3          3                 3

  Czechoslovakia                                                                        
  (no longer                                                                            
  exists)                                                                               

  Cuba             1             1            1            1          1                 1

  Cape Verde       3             3            3            3          3                 3

  Christmas Island 0             0            0            0          0                 0

  Cyprus           2             2            2            2          2                 2

  Czech Republic   3             3            3            3          3                 3

  German                                                                                
  Democratic                                                                            
  Republic (no                                                                          
  longer exists)                                                                        

  Germany          3             3            3            3          3                 3

  Djibouti         2             2            2            2          2                 2

  Denmark          3             3            3            3          3                 3

  Dominica         3             3            3            3          3                 3

  Dominican        3             2            2            3          3                 3
  Republic                                                                              

  Algeria          3             2            2            2          2                 2

  Ecuador          3             2            3            2          2                 2

  Estonia          3             2            2            2          2                 2

  Egypt            2             2            2            2          2                 2

  Western Sahara   1             1            1            1          1                 1

  Eritrea          1             1            1            1          1                 1

  Spain            3             3            3            3          3                 3

  Ethiopia         1             3            1            1          1                 1

  Finland          3             3            3            3          3                 3

  Fiji             2             2            2            2          2                 2

  Falkland Islands 2             3            3            3          3                 3
  (Malvinas)                                                                            

  Micronesia       2             2            2            2          2                 2

  Faroe Islands    2             2            2            2          2                 2

  France           3             3            3            3          3                 3

  France,          3             3            3            3          3                 3
  Metropolitan                                                                          

  Gabon            1             1            1            1          1                 1
  ---------------- ------------- ------------ ------------ ---------- ----------------- ------------------

  --------------- ------------- ------------ ------- ------------ --------- ---------- ---------- ----------------- ----------------- ----------- ------------------
  *Country*       *Political*   *Military*           *Economic*                        *Social*   *Informational*                                 *Infrastructure*

  United Kingdom  3             3                    3                                 3          3                                               3
  (Great Britain)                                                                                                                                 

  Grenada         2             2                    2                                 2          2                                               2

  Georgia         2             2                    2                                 2          2                                               2

  French Guiana   3             3                    3                                 3          3                                               3

  Ghana           1             1                    1                                 1          1                                               1

  Gibraltar       3             3                    3                                 3          3                                               3

  Greenland       3             3                    3                                 3          3                                               3

  Gambia          1             1                    1                                 1          1                                               1

  Guinea          1             1                    1                                 1          1                                               1

  Guadeloupe      2             2                    2                                 2          2                                               2

  Equatorial      1             1                    1                                 1          1                                               1
  Guinea                                                                                                                                          

  Greece          3             3                    3                                 3          3                                               3

  South Georgia   2             2                    2                                 2          2                                               2
  and the South                                                                                                                                   
  Sandwich                                                                                                                                        
  Islands                                                                                                                                         

  Guatemala       1             1                    1                                 1          1                                               1

  Guam            3             3                    3                                 3          3                                               3

  Guinea-Bissau   2             2                    2                                 2          2                                               2

  Guyana          1             1                    1                                 1          1                                               1

  Hong Kong       3             3                    3                                 3          3                                               3

  Heard &         1             1                    1                                 1          1                                               1
  McDonald                                                                                                                                        
  Islands                                                                                                                                         

  Honduras        1             2                    1                                 1          1                                               1

  Croatia         1             3                    2                                 2          2                                               2

  Haiti           1             3                    2                                 2          2                                               2

  Hungary         3             3                    3                                 3          3                                               3

  Indonesia       3             3                    3                                 3          3                                               3

  Ireland         3             3                    3                                 3          3                                               3

  Israel          3             3                    3                                 3          3                                               3

  India           3             3                    3                                 3          3                                               3

  British Indian  2             2                    2                                 2          2                                               2
  Ocean Territory                                                                                                                                 

  Iraq            3             2                    2                                 2          2                                               2

  Islamic         3             2                    2                                 2          2                                               2
  Republic of                                                                                                                                     
  Iran                                                                                                                                            

  Iceland         3             3                    3                                 3          3                                               3

  Italy           3             3                    3                                 3          3                                               3

  Jamaica         3             3                    3                                 3          3                                               3

  Jordan          2             2                    2                                 2          2                                               2

  Japan           3             3                    3                                 3          3                                               3

  Kenya           2             2                    1                                 1          1                                               1

  Kyrgyzstan      2             1                    1                                 1          1                                               1

  Cambodia        2             2                    2                                 1          1                                               1

  Kiribati        3             3                    3                                 3          3                                               3

  *Country*       *Political*   *Military*           *Economic*                        *Social*   *Informational*                                 *Infrastructure*

  Comoros         2             3                    3                                 3          3                                               3

  St. Kitts and   2             1                    3                                 3          3                                               3
  Nevis                                                                                                                                           

  Korea,          3             3                    3                                 3          3                                               3
  Democratic                                                                                                                                      
  People\'s                                                                                                                                       
  Republic of                                                                                                                                     

  Korea, Republic 3             3                    3                                 3          3                                               3
  of                                                                                                                                              

  Kuwait          2             3                    3                                 3          3                                               3

  Cayman Islands  2             2                    2                                 2          2                                               2

  Kazakhstan      2             2                    2                                 2          2                                               2

  Lao People\'s   2             2                    1                                 1          1                                               1
  Democratic                                                                                                                                      
  Republic                                                                                                                                        

  Lebanon         3             2                    1                                 1          1                                               1

  Saint Lucia     3             3                    3                                 3          3                                               3

  Liechtenstein   3             3                    3                                 3          3                                               3

  Sri Lanka       2             2                    2                                 2          2                                               2

  Liberia         2             1                    1                                 1          1                                               1

  Lesotho         3             3                    3                                 3          3                                               3

  Lithuania       3             3                    3                                 3          3                                               3

  Luxembourg      3             3                    3                                 3          3                                               3

  Latvia          3             3                    3                                 3          3                                               3

  Libyan Arab     2             2                    2                                 2          2                                               2
  Jamahiriya                                                                                                                                      

  Morocco         2             2                    1                                 1          1                                               1

  Monaco          3             3                    3                                 3          3                                               3

  Moldova,        2             2                    2                                 2          2                                               2
  Republic of                                                                                                                                     

  Madagascar      2             2                    2                                 2          2                                               2

  Marshall        2             2                    2                                 2          2                                               2
  Islands                                                                                                                                         

  Mali            1             1                    1                                 1          1                                               1

  Mongolia        3             2                    1                                 1          1                                               1

  Myanmar         3             3                    2                                 2          2                                               2

  Macau           3             3                    3                                 3          3                                               3

  Northern        3             3                    3                                 3          3                                               3
  Mariana Islands                                                                                                                                 

  Martinique      3             3                    3                                 3          3                                               3

  Mauritania      2             2                    2                                 2          2                                               2

  Monserrat       2             2                    2                                 2          2                                               2

  Malta           2             2                    2                                 2          2                                               2

  Mauritius       1             1                    1                                 1          1                                               1

  Maldives        1             1                    1                                 1          1                                               1

  Malawi          1             1                    1                                 1          1                                               1

  Mexico          3             3                    3                                 3          3                                               3

  Malaysia        2             2                    2                                 2          2                                               2

  Mozambique      1             1                    1                                 1          1                                               1

  Namibia         2             2                    2                                 2          2                                               2

  New Caledonia   2             2                    2                                 2          2                                               2

  Niger           1             1                    1                                 1          1                                               1

  *Country*       *Political*   *Military*           *Economic*                        *Social*   *Informational*                                 *Infrastructure*

  Norfolk Island  3             3                    3                                 3          3                                               3

  Nigeria         2             2                    1                                 1          1                                               1

  Nicaragua       2             3                    2                                 2          2                                               2

  Norway          3             3                    3                                 3          3                                               3

  Nauru           3             3                    3                                 3          3                                               3

  Neutral Zone                                                                                                                                    
  (no longer                                                                                                                                      
  exists)                                                                                                                                         

  Niue            1             1                    1                                 1          1                                               1

  New Zealand     3             3                    3                                 3          3                                               3

  Oman            2             2                    2                                 2          2                                               2

  Panama          3             3                    3                                 3          3                                               3

  Peru            2             2                    2                                 2          2                                               2

  French          3             3                    3                                 3          3                                               3
  Polynesia                                                                                                                                       

  Papua New       2             2                    2                                 2          2                                               2
  Guinea                                                                                                                                          

  Philippines     3             3                    3                                 3          3                                               3

  Pakistan        2             2                    2                                 2          2                                               2

  Poland          2             2                    2                                 2          2                                               2

  St. Pierre &    2             2                    2                                 2          2                                               2
  Miquelon                                                                                                                                        

  Pitcairn        1             1                    1                                 1          1                                               1

  Puerto Rico     3             3                    3                                 3          3                                               3

  Portugal        3             3                    3                                 3          3                                               3

  Palau           3             3                    3                                 3          3                                               3

  Paraguay        2             2                    2                                 2          2                                               2

  Qatar           2             2                    2                                 2          2                                               2

  Réunion         1             1                    1                                 1          1                                               1

  Romania         3             3                    3                                 3          3                                               3

  Russian         2             2                    2                                 2          2                                               2
  Federation                                                                                                                                      

  Rwanda          1             1                    1                                 1          1                                               1

  Saudi Arabia    2             2                    2                                 2          2                                               2

  Solomon Islands 2             2                    2                                 2          2                                               2

  Seychelles      2             2                    2                                 2          2                                               2

  Sudan           2             2                    2                                 2          2                                               2

  Sweden          3             3                    3                                 3          3                                               3

  Singapore       3             3                    3                                 3          3                                               3

  St. Helena      3             1                    2                                 2          2                                               2

  Slovenia        3             3                    3                      3                     3                                               *3*

  Svalbard & Jan  2             2                    2                      2                                       2                             *2*
  Mayen Islands                                                                                                                                   

  Slovakia        3             3                    3                      3                                       3                             *3*

  Sierra Leone    1             1                    1                      1                                       1                             *1*

  San Marino      2             2                    2                      2                                       2                             *2*

  Senegal         1             1                    1                      1                                       1                             *1*

  Somalia         1             1                    1                      1                                       1                             *1*

  *Country*       *Political*   *Military*           *Economic*             *Social*                                *Informational*               Infrastructure

  Suriname        1             2                    1                      1                                       1                             *1*

  Sao Tome &      2             2                    2                      2                                       2                             *2*
  Principe                                                                                                                                        

  Union of Soviet 0             0                    0                      0                                       0                             *0*
  Socialist                                                                                                                                       
  Republics (no                                                                                                                                   
  longer exists)                                                                                                                                  

  El Salvador     2             2                    2                      2                                       2                             *2*

  Syrian Arab     2             2                    2                      2                                       2                             *2*
  Republic                                                                                                                                        

  Swaziland       3             3                    3                      3                                       2                             *2*

  Turks & Caicos  3             3                    3                      3                                       3                             *3*
  Islands                                                                                                                                         

  Chad            1             1                    1                      1                                       1                             *1*

  French Southern 1             1                    1                      1                                       1                             *1*
  Territories                                                                                                                                     

  Togo            1             1                    1                      1                                       1                             *1*

  Thailand        3             3                    3                      3                                       3                             *3*

  Tajikistan      2             2                    2                      2                                       2                             *2*

  Tokelau         1             1                    1                      1                                       1                             *1*

  Turkmenistan    1             1                    1                      1                                       1                             *1*

  Tunisia         2             2                    2                      2                                       2                             *2*

  Tonga           1             1                    1                      1                                       1                             *1*

  East Timor      1             1                    1                      1                                       1                             *1*

  Turkey          3             3                    3                      3                                       3                             *3*

  Trinidad &      1             1                    1                      1                                       1                             *1*
  Tobago                                                                                                                                          

  Tuvalu          1             1                    1                      1                                       1                             *1*

  Taiwan Province 3             3                    3                      3                                       3                             *3*
  of China                                                                                                                                        

  Tanzania United 1                          1                    1         1                                                         1           *1*
  Republic of                                                                                                                                     

  Ukraine         2             2                    2                      2                                       2                             *2*

  Uganda          1             1                    1                      1                                       1                             *1*

  United States   3             3                    3                      3                                       3                             *3*
  Minor Outlying                                                                                                                                  
  Islands                                                                                                                                         

  United States   3             3                    3                      3                                       3                             *3*
  of America                                                                                                                                      

  Uruguay         2             2                    2                      2                                       2                             *2*

  Uzbekistan      2             2                    1                      1                                       1                             *1*

  Vatican City    2             2                    2                      2                                       2                             *2*
  State (Holy                                                                                                                                     
  See)                                                                                                                                            

  *Country*       *Political*   *Military*           *Economic*             *Social*                                *Informational*               Infrastructure

  St. Vincent &   2             2                    2                      2                                       2                             *2*
  the Grenadines                                                                                                                                  

  Venezuela       3             3                    2                      2                                       2                             *2*

  British Virgin  3             3                    3                      3                                       3                             *3*
  Islands                                                                                                                                         

  United States   3             3                    3                      3                                       3                             *3*
  Virgin Islands                                                                                                                                  

  Viet Nam        2             2                    1                      1                                       1                             *1*

  Vanuatu         1             1                    1                      1                                       1                             *1*

  Wallis & Futuna 1             1                    1                      1                                       1                             *1*
  Islands                                                                                                                                         

  Samoa           3             3                    3                      3                                       3                             *3*

  Democratic      0             0                    0                      0                                       0                             *0*
  Yemen (no                                                                                                                                       
  longer exists)                                                                                                                                  

  Yemen           2             2                    2                      1                                       1                             *1*

  Yugoslavia      3             3                    3                      3                                       3                             *3*

  South Africa    3             3                    3                      3                                       3                             *3*

  Zambia          1             1                    1                      1                                       1                             *1*

  Zaire           1             1                    1                      1                                       1                             *1*

  *Zimbabwe*      *1*           *1*                  *1*                    *1*                                     *1*                           *1*
  --------------- ------------- ------------ ------- ------------ --------- ---------- ---------- ----------------- ----------------- ----------- ------------------

***Appendix 6 -- Real-World Correlation***

The correlation measures the agreement between two data series: the
mindshare that is reported by the simulation at each time step (tick),
and the dates on which terrorist events occurred (from the WITS
database). For a single simulation, the two series might map to a
timeline as shown in Figure A6-1, below, in which the red triangles
above the timeline are simulated time steps with corresponding mindshare
values, and the gray triangles beneath the timeline are WITS incidents.

  -----------------------------------------------------------------------
  **Figure A6-1.** Raw mindshare and WITS incidents

  ![](media/image270.png){width="5.990972222222222in"
  height="1.2472222222222222in"}
  -----------------------------------------------------------------------

The main challenge to building a correlation is in identifying how best
to divide the seven-month (215-day) timeline into regular periods so
that it can be sampled consistently across all runs. The data series are
both discrete, but the mindshare data represent a sample drawn from a
continuously changing quantity, whereas the WITS data are specific
incidents that occurred on specific days. Each of these data series is
examined separately.

# Standardizing Mindshare Data

Not all of the simulations have the same number of mindshare data
points, because the time scale was one of the parameters varied across
runs: at 3 days per tick, the seven months elapse in the simulation
after only 70 ticks, whereas at 14 days per tick, the same seven-month
period includes only 25 ticks.

The simplest way to augment these data points is to rely on linear
interpolation, as depicted in Figure A6-2, allowing us to estimate the
mindshare values for any day within the 215-day simulation window. The
mindshare data are presumed to be nonlinear, as that is one of the key
advantages to a CAS simulation, but as each discrete sample represents
the smallest increment that the simulation considered, a linear
approximation is probably no less reasonable than any other spline;
Occam's Razor favors the most direct solution.

  -----------------------------------------------------------------------
  **Figure A6-2**. Interpolating mindshare

  ![](media/image271.png){width="2.029166666666667in"
  height="0.8909722222222223in"}
  -----------------------------------------------------------------------

For each of the simulations, linear interpolation will be used to
estimate 215 mindshare values, one per day.

# Standardizing WITS Data

The WITS data are gathered from daily observations of terrorist events
and inherently include data for all 215 days. Unfortunately, only 60
separate WITS events occurred within this time frame (not all on
separate days), so the counts per day are 0, 1, 2, or 3. Even
aggregation does not address this problem adequately, as the zeros
dominate the data set.

Interpolation and splines are inappropriate, as they presume a
continuous variable underlies the data, but the WITS data are discrete.
Our approach, as illustrated in Figure A6-3 is to transform each WITS
incident from a single-day event into a 5-day event, distributing its
1.0 unit occurrence in a manner that approximates a Gaussian
distribution.

  -----------------------------------------------------------------------
  **Figure A6-3**. Distributing a single discrete WITS incident over 5
  days

  ![](media/image272.png){width="3.05in" height="1.2083333333333333in"}
  -----------------------------------------------------------------------

The justification for doing so is that, while the physical act of
violence itself has a hard onset, the underlying emotional violence
within the populace does not likely ignite instantly but instead waxes
and wanes with some period greater than a day.[^5] When these
distributions overlap each other, they are summed, as shown in Figure
A6-4 below.

  -----------------------------------------------------------------------
  **Figure A6-4**. Distributing a series of 7 discrete WITS incidents
  over 14 days

  ![](media/image273.png){width="5.964583333333334in"
  height="2.3513888888888888in"}
  -----------------------------------------------------------------------

This scheme allows us to smooth the WITS incidents crudely over the
period, preserving the trend in the number of incidents per any given
subinterval. The total number of WITS incidents across the 215-day
period remains 60, but they are distributed more smoothly across the
days. The following chart (Figure A6-5) shows the real data from
3/15/2007 to 10/15/2007, both as given (discrete events) and estimated
(distributed events).

**Figure A6-5.** Visualization of distributed WITS data

![](media/image274.png){width="4.388888888888889in"
height="3.5395833333333333in"}

# Constructing the Correlation

Applying the methods discussed in the preceding two sections, we can
divide the seven-month simulation timeline into as many periods
(samples) as we wish to use for constructing the correlation. The
question remains: How many periods should we use in the analysis?

We conducted a study across the range of possible periods, from 3 to
100, and observed what happened to the minimum, mean, and maximum
correlations between mindshare and WITS incidents across all 180 of the
simulations, assuming a 30-day lag between the two data series. The
results are reported in Figure A6-6.

**Figure A6-6**. Correlations with assumed delay

![](media/image275.wmf){width="5.145833333333333in"
height="4.134722222222222in"}

As the 215-day period is divided into more and more periods, the range
in correlations asymptotically approaches a constant.

# *Appendix 7 -- Real-Time Update Code Description*

**TimerExample.nlogo**

WHAT IS IT?

\-\-\-\-\-\-\-\-\-\--

Wall-clock is intended as an easy way to pass dates to and from an
extension, and to synchronize a simulation with some notion of real
time.

This is the \"hello world\" example for the wall-clock extension. There
are four commands and two reporters. The commands are:

set-scale-factor : this command takes the number of milliseconds in a
single tick of the simulation. This example uses 86400000 as its scale
factor by default, the number of milliseconds in a day.

set-format : this command takes the format you want to use to
communicate dates to and from the extension. This is intended to be the
easiest possible format, from the simulation programmer\'s perspective,
that java\'s SimpleDateFormat will accept. For a complete specification
of this format, see Java\'s API documentation on
java.text.SimpleDateFormat.

set-start-time : this command takes the start date of your simulation
(the value for tick 0), in the format specified above. It\'s important
to set this before the simulation starts running (i.e., at tick 0)!

set-display-format : this command takes the format you want to use to
display dates, typically for \"nicely\" formatted reporters. This should
be a format that is easily readable.

The reporters are:

display-time : reports the current simulation time, in \"nice\" format.

time : reports the current simulation time, in the default format.

real-time : reports the wall-clock time, using the java.util.Calendar
class.

SETUP

\-\-\-\--

Make sure the wall-clock jar file from the seedling/jar/ folder of the
project is in the \<netlogo install dir\>/extensions/wall-clock/ folder.

HOW IT WORKS

\-\-\-\-\-\-\-\-\-\-\--

There isn\'t really a model in this example, and that isn\'t really the
purpose of this extension! The extension makes use of java.util.Date and
java.text.SimpleDateFormatter to provide a simple means for dealing with
dates and wall-clock time. The constructor for Date takes the number of
milliseconds past Jan. 01, 1970. The extension takes the number of ticks
in the simulation, multiplies it by the scale factor, and adds the
milliseconds after 01-01-70 of the start date. The result is returned in
the format specified by the simulation.

The start time is actually recalculated each time you change the scale
factor. See the things to try section for details.

HOW TO USE IT

\-\-\-\-\-\-\-\-\-\-\-\--

Initialization:

If you remove the default settings from the procedures, you\'ll notice
the formatters, start date, and scale factor all have default values
within the extension. They are:

scale factor = 1000.0 (one model tick per second)

start date = Jan 01, 1970 00:00:00.0 GMT, the natural start date for
java Dates

format = \"MMddyyyy\"

display-format = \"MMM dd, yyyy hh:mm:ss zzz\"

No initialization necessary, you can use the reporters at any time to
get wall-clock

values for your simulation.

THINGS TO NOTICE

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

The monitors show off the reporters doing what they do best, and the
inputs allow you to set all the parameters of the extension.

The smallest allowed granularity is milliseconds. This is a restriction
of the java Date class and the date format. However, this also means
that even if you specify reporting format that\'s only accurate to the
minute, the extension will always keep track of it to the millisecond
precision.

THINGS TO TRY

\-\-\-\-\-\-\-\-\-\-\-\--

Click go to watch it step through a day at a time, or click go-forever
and experiment with the scale-factor slider.

Run the experiment for a long time with a large scale factor, then slow
time way down to watch how the extension calculates the new time. Notice
the extension behaves as you would expect, time slows down. The
extension doesn\'t continue to calculate time based on the offset from
the simulation\'s start time, which would cause a jump backwards in time
if the scale factor was set to something much smaller mid-simulation.

EXTENDING THE MODEL

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

This could be extended to any simulation that needs a real-time
component.

**GeocodeExample.nlogo**

WHAT IS IT?

\-\-\-\-\-\-\-\-\-\--

This example demonstrates the geocode functionality as a stand-alone
netlogo extension. In the seedling/jar folder is another jar,
fullsimframe.jar, that has all the geocoding, wall-clock, and simframe
functionality bundled in one extension.

ìTalibanî is a general model that allows users to explore interactions
and consequences among the diverse forces currently in Afghanistan. This
example added the geocode functionality to the Taliban model, to
demonstrate its functionality on a pertinent map. Two additional buttons
have been added to the original model: \"Kabul\" and \"Kandahar.\" Each
button highlights the patch corresponding to each city.

SETUP

\-\-\-\--

Make sure the geocode.jar from seedling/jar is in the \<netlogo
install\>/extensions/geocode/ folder.

HOW IT WORKS

\-\-\-\-\-\-\-\-\-\-\--

The geocode extension uses the Google Map API to request lat-long
coordinates. The extension caches these results, reporting them to the
model as either patch coordinates (based on the bounds of the world), or
lat-long coordinates, reported in decimal degrees.

HOW TO USE IT

\-\-\-\-\-\-\-\-\-\-\-\--

Open a browser to maps.google.com. Google maps accepts all forms of
unstructured queries, and any query that returns a valid result on the
website will also return a geocode with the extension. For the two
examples provided here, \"Kabul\" was a valid query. However, for
Kandahar, the query \"Kandahar, Afghanistan\" was required to receive a
result.

In order to use the google maps api, you must apply for a key at
http://code.google.com/apis/maps/signup.html. The key provided in the
example SHOULD NOT be reused.

This key, along with the bounds of the map, are passed in to the
extension\'s initialization procedure. The map bounds in terms of patch
coordinates are retrieved automatically when the initialization
procedure is called. Currently, the geocode extension only supports box
worlds.

For this example, the following initialization was used:

geocode:init 29.40611 60.50417 38.47198 74.91574
\"ABQIAAAAmd7V71yw9ZddA0s8Z3wSKBQOY93X9JJIv4oRUIhVDwundpXiNhTCZmojXGlyeRZ7KKGgGRE0GgKFpQ\"

The four numbers are the latitude and longitude of the top and bottom
corners. These values were retrieved from the \"Afghanistan\" article on
Wikipedia. The string is the Google key.

The extension comes with four reporters, xpcor, ypcor, lat, and lon. All
these reporters take in a string query, and return a decimal vaue. Xpcor
and Ypcor provide the patch coordinates that can be passed directly to
ask patch, as in the show-city procedure in this example. Lat and lon
return decimal degree values.

THINGS TO NOTICE

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

Google\'s geocoding isn\'t perfect. Kandahar is actually an example of
the inaccuracies that it can occasionally produce. If you navigate to
maps.google.com and zoom in on

Afghanistan, you\'ll see Kandahar labeled on the map. If you then
actually \*search\* for

Kandahar, the search result points to an area about 50 miles Southwest
of where the map says Kandahar is. It\'s the \*search result location,\*
not the actual labeled location, that would be returned by this
geocoding extension.

**RealtimeTest.nlogo**

WHAT IS IT?

\-\-\-\-\-\-\-\-\-\--

This model shows how to use the simframe extension, bundled with the
wall-clock functionality, to deliver events to a running simulation.

This example only shows invocation of simulation functions. The
simframe2 model shows how to use the reaction grammar to add extensions
to simframe in the form of additional jar files.

SETUP

\-\-\-\--

Make sure that \"events.txt\" and \"test.txt\" are in the same folder as
this model. Also, make sure seedling/jar/simframe.jar is in \<netlogo
install\>/extensions/simframe/.

HOW IT WORKS

\-\-\-\-\-\-\-\-\-\-\--

Simframe uses two initialization files, a reaction file (test.txt) and
an events file (events.txt). The reaction file specifies all the
information necessary to take events as they arrive and use them to
update the running simulation at the appropriate time.

Let\'s examine the reaction file line-by-line:

event-attributes specifies the names of the different attributes an
event will have. They can be referred to as parameters passed to
functions, and also to select which event handler to use for a given
event. The line must begin with \"event-attributes\" followed by a
white-space-delimited list of attribute names.

The next line specifies the type of each attribute in the same order.
Possible values are numeric, string, and time.

The next section of the reaction file specifies transforms. In netlogo,
these correspond to reporters, or in general, functions that return a
value. These values can then be passed as parameters to other
transforms. If a transform returns a set of agents or patches, other
functions can be called \*on\* these returned values. For instance, the
line \"sim.die() on sim.blue-turtles()\" would call the die function on
the agents that the blue-turtles transform returns. A transform
declaration should be of the form:

transform return-type name ( param-types )

Return-type can be numeric, string, time, agent, agents, location, or
locations. The name should start with \"sim.\" or \"realtime.\" denoting
whether it is a simulation function or an extension function written in
java that simframe should invoke directly. The param-types can be
numeric, string, or time, and should appear in a comma-delimited list.

The next section of the file includes event handlers. They are declared
as follows:

event name(selectors, \...) {

commands

}

The name must be unique. Selectors are the values of the event
attributes that trigger this particular event handler. They can either
be specific values, or \"\*\" for any value. For instance, in the sample
text file, the event handler red_run will be triggered for every event
with id = 1 and any headline.

Commands are the procedures which are called when this event is
triggered. The line starts with a function to call, either beginning
with \"sim.\" or \"realtime.\" Parameters can be literal values, or
event attributes. For example, if the event id were to be passed to a
particular command, it would be referenced as \"event.id\":

sim.left(event.id) on sim.red-turtles()

Optionally, a transform that specifies a particular agent or location
set can be added to the end of the command, using the keyword \"on.\"
For sim commands, use the function name exactly as it would appear in
netlogo (left, forward, separate, red-turtles, and blue-turtles, are all
functions defined in the nlogo file). For realtime commands, use the
fully-qualified class name, followed by the method name (for an example
of this see the simframe2 model).

The events file is simply a text file full of events. The first token is
the date, specified in the format given in the nlogo file (using the
wall-clock functionality). The attributes of the event itself are
speicied inside the parenthesis, as a comma-delimited list of literals
(strings, numerics, or times).

HOW TO USE IT

\-\-\-\-\-\-\-\-\-\-\-\--

Click setup, then go-forever, and watch as the turtles react to the
events as they arrive at different times.

The \"test\" button shows a simple example of how simframe works: during
each tick, the simulation receives a string of code from simframe to
interpret and run. Test-code simply returns the string \"separate\" from
simframe, and then netlogo runs it.

**RealtimeExample2.nlogo**

WHAT IS IT?

\-\-\-\-\-\-\-\-\-\--

This model shows how to use the simframe extension, bundled with the
wall-clock functionality, to deliver events to a running simulation.

This example shows invocation of simulation and realtime functions. The
simframe2 model shows how to use the reaction grammar to add extensions
to simframe in the form of additional jar files.

SETUP

\-\-\-\--

Make sure that \"events.txt\" and \"test.txt\" are in the same folder as
this model. Also, make sure seedling/jar/simframe.jar and
seedling/jar/mapmgr.jar are in \<netlogo install\>/extensions/simframe/.

HOW IT WORKS

\-\-\-\-\-\-\-\-\-\-\--

Simframe uses two initialization files, a reaction file (test.txt) and
an events file (events.txt). The reaction file specifies all the
information necessary to take events as they arrive and use them to
update the running simulation at the appropriate time.

Let\'s examine the reaction file line-by-line:

event-attributes specifies the names of the different attributes an
event will have. They can be referred to as parameters passed to
functions, and also to select which event handler to use for a given
event. The line must begin with \"event-attributes\" followed by a
white-space-delimited list of attribute names.

The next line specifies the type of each attribute in the same order.
Possible values are numeric, string, and time.

The next section of the reaction file specifies transforms. In netlogo,
these correspond to reporters, or in general, functions that return a
value. These values can then be passed as parameters to other
transforms. If a transform returns a set of agents or patches, other
functions can be called \*on\* these returned values. For instance, the
line \"sim.die() on sim.blue-turtles()\" would call the die function on
the agents that the blue-turtles transform returns. A transform
declaration should be of the form:

transform return-type name ( param-types )

Return-type can be numeric, string, time, agent, agents, location, or
locations. The name should start with \"sim.\" or \"realtime.\" denoting
whether it is a simulation function or an extension function written in
java that simframe should invoke directly. The param-types can be
numeric, string, or time, and should appear in a comma-delimited list.

The next section of the file includes event handlers. They are declared
as follows:

event name(selectors, \...) {

commands

}

The name must be unique. Selectors are the values of the event
attributes that trigger this particular event handler. They can either
be specific values, or \"\*\" for any value. For instance, in the sample
text file, the event handler red_run will be triggered for every every
with id = 1 and any headline.

Commands are the procedures which are called when this event is
triggered. The line starts with a function to call, either beginning
with \"sim.\" or \"realtime.\" Parameters can be literal values, or
event attributes. For example, if the event location were to be passed
to a particular command, it would be referenced as \"event.location\":

realtime.geocode.MapManager.geoCodeX ( event.location )

Optionally, a transform that specifies a particular agent or location
set can be added to the end of the command, using the keyword \"on.\"
For sim commands, use the function name exactly as it would appear in
netlogo (left, forward, separate, red-turtles, and blue-turtles, are all
functions defined in the nlogo file). For realtime commands, use the
fully-qualified class name, followed by the method name:

realtime.realtimeext.MapManagerExt.init( 0.0, 0.0, 40.0, 40.0, 29.40611,
60.50417, 38.47198, 74.91574, \"googlekey\" )

The events file is simply a text file full of events. The first token is
the date, specified in the format given in the nlogo file (using the
wall-clock functionality). The attributes of the event itself are
speicied inside the parenthesis, as a comma-delimited list of literals
(strings, numerics, or times).

EXTENSIONS TO SIMFRAME

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

This example shows a method for adding extensions to simframe in the
form of external jar files. This particular example exposes the geocode
functionality as an extension to simframe rather than as its own
extensions to netlogo. Realistically it makes more sense for geocoder to
be a netlogo extension, it was merely used in this way for demonstrative
purposes. Ideally, simframe should be used as a bundle with both the
wall-clock and geocode funtionality. To do this, delete the simframe and
mapmgr jars from the \<netlogo install\>/extensions/simframe/, rename
fullsimframe.jar to simframe.jar, and copy it to \<netlogo
install\>/extensions/simframe/.

HOW TO USE IT

\-\-\-\-\-\-\-\-\-\-\-\--

Click load, and then the step button \"\>\" twice, and watch as the two
cities passed in from the events are highlighted.

**RealtimeExample3.nlogo**

WHAT IS IT?

\-\-\-\-\-\-\-\-\-\--

This model shows how to use the simframe extension, bundled with the
wall-clock functionality, to deliver events to a running simulation.

This example shows invocation of simulation and realtime functions. The
simframe3 model extends simframe2 to show how to receive events from
distributed services, using SOAP requests.

SETUP

\-\-\-\--

Make sure that \"events.txt\" and \"test.txt\" are in the same folder as
this model. Also, make sure seedling/jar/simframe.jar and
seedling/jar/mapmgr.jar are in \<netlogo install\>/extensions/simframe/.
Also, 4 libs from the seedling/lib folder need to be in the \<netlogo
install\>/extensions/simframe/ folder:

-xerces.jar

-activation.jar

-mail.jar

-soap.jar

To set up the SOAP service, the remote machine needs to have some
servlet container (I used Apache Tomcat). Copy the soap.war file from
seedling/jar to the webapps folder of your servlet container. Also, copy
the events.txt file from the simframe2 example to the bin/ folder of the
servlet container (this is the default root path, and the remote service
will read this file in and provide those events to the model).

To deploy the service, the easiest way is from the command line:

java org.apache.soap.server.ServiceManagerClient

http://localhost:8080/soap/servlet/rpcrouter deploy soap.xml

The classpath will need to point to the four jars listed above for this
java command to work properly.

HOW IT WORKS

\-\-\-\-\-\-\-\-\-\-\--

Simframe uses two initialization files, a reaction file (test.txt) and a
\*local\* events file (events.txt), which can be empty in this case
since we\'re pulling events from a remote source. The reaction file
specifies all the information necessary to take events as they arrive
and use them to update the running simulation at the appropriate time.

Let\'s examine the reaction file line-by-line:

event-attributes specifies the names of the different attributes an
event will have. They can be referred to as parameters passed to
functions, and also to select which event handler to use for a given
event. The line must begin with \"event-attributes\" followed by a
white-space-delimited list of attribute names.

The next line specifies the type of each attribute in the same order.
Possible values are numeric, string, and time.

This reaction file has a new declaration, a CHANNEL. Channels take a url
and an id. In the example provided, the remote service is actually being
hosted locally, so for a true remote test this url would need to point
elsewhere.

The next section of the reaction file specifies transforms. In netlogo,
these correspond to reporters, or in general, functions that return a
value. These values can then be passed as parameters to other
transforms. If a transform returns a set of agents or patches, other
functions can be called \*on\* these returned values. For instance, the
line \"sim.die() on sim.blue-turtles()\" would call the die function on
the agents that the blue-turtles transform returns. A transform
declaration should be of the form:

transform return-type name ( param-types )

Return-type can be numeric, string, time, agent, agents, location, or
locations. The name should start with \"sim.\" or \"realtime.\" denoting
whether it is a simulation function or an extension function written in
java that simframe should invoke directly. The param-types can be
numeric, string, or time, and should appear in a comma-delimited list.

The next section of the file includes event handlers. They are declared
as follows:

event name(selectors, \...) {

commands

}

The name must be unique. Selectors are the values of the event
attributes that trigger this particular event handler. They can either
be specific values, or \"\*\" for any value. For instance, in the sample
text file, the event handler red_run will be triggered for every every
with id = 1 and any headline.

Commands are the procedures which are called when this event is
triggered. The line starts with a function to call, either beginning
with \"sim.\" or \"realtime.\" Parameters can be literal values, or
event attributes. For example, if the event location were to be passed
to a particular command, it would be referenced as \"event.location\":

realtime.geocode.MapManager.geoCodeX ( event.location )

Optionally, a transform that specifies a particular agent or location
set can be added to the end of the command, using the keyword \"on.\"
For sim commands, use the function name exactly as it would appear in
netlogo (left, forward, separate, red-turtles, and blue-turtles, are all
functions defined in the nlogo file). For realtime commands, use the
fully-qualified class name, followed by the method name:

realtime.realtimeext.MapManagerExt.init( 0.0, 0.0, 40.0, 40.0, 29.40611,
60.50417, 38.47198, 74.91574, \"googlekey\" )

The events file is simply a text file full of events. The first token is
the date, specified in the format given in the nlogo file (using the
wall-clock functionality). The attributes of the event itself are
speicied inside the parenthesis, as a comma-delimited list of literals
(strings, numerics, or times).

EXTENSIONS TO SIMFRAME

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

This example shows a method for adding extensions to simframe in the
form of external jar files. This particular example exposes the geocode
functionality as an extension to simframe rather than as its own
extensions to netlogo. Realistically it makes more sense for geocoder to
be a netlogo extension, it was merely used in this way for demonstrative
purposes. Ideally, simframe should be used as a bundle with both the
wall-clock and geocode funtionality. To do this, delete the simframe and
mapmgr jars from the \<netlogo install\>/extensions/simframe/, rename
fullsimframe.jar to simframe.jar, and copy it to \<netlogo
install\>/extensions/simframe/.

HOW TO USE IT

\-\-\-\-\-\-\-\-\-\-\-\--

Click load, and then the step button \"\>\" several times, and watch as
the three cities passed in from the events are highlighted, two remotely
and one locally.

***Appendix 8 -- Generating Narrative***

The relatively simple simulations that have been generated as part of
this seedling effort still generate rather large volumes of data.
Approximately 10 data values could be reported for each of the few
thousand agents (and for each of the \~800 patches) per time step, with
50 or more time steps in short runs. We have been tracking only cursory
data elements from these runs, and storing them in a common database to
facilitate the data entry, but with well over 2000 runs in the
collection, the total amount of data has been manageable only through
SQL queries and human analyst intervention.

The challenge clearly is how best to communicate *what has happened* and
*what is important* to casual users of the system. Commanders in the
field, for example, cannot be expected to troll through millions of rows
of data describing a simulation, but instead wish to know what
interventions are most likely to have the effects they seek, and to be
informed of the most-often encountered side effects. Satisfying these
requirements is an exceptionally difficult task (Kittredge 2000) and
will require dedicated study. This document attempts to summarize some
of the issues that are required to address the problem.

Fundamentally, this is the problem of reducing voluminous quantitative
data to parsimonious qualitative data. There appear to be three general
themes:

1.  framing the task

2.  the mechanics of solution

3.  the art of solution

Each of these will be reviewed in a subsequent section. We also connect
each of these three themes to our Afghanistan model using a subset of
our experimental runs as an example. This subset concerns the evaluation
of multiple COAs, as taken from Task 4: Section III -- Evaluating
Command Options for Ghazni in 2006.

# Framing the Task

Qualitative data are important in their own right (Van Maanen 1979). And
yet, multiple layers are implicit in the construction of any narrative
(Pentland 1999), with each layer having an independent context that
matters (Miles 1979). Qualitative data represent a significant form of
data reduction, eliding the details that do not pertain to the core
message, and highlighting the most important elements of the idea being
expressed. And yet without the appropriate context, these compressed
ideas become increasingly misleading.

In this seedling, we have tried to demonstrate how a generic,
workbench-type framework can be developed to support multiple social
theories and multiple engagement types, but the aggregation and
reporting of simulation results to a field commander require an implicit
understanding of the analysis task to be performed. We have to know what
questions are being asked, so that we know how best to sieve through the
simulation details to summarize what has occurred (Kukich 1983).

In one of our experimental sets of simulation runs, we evaluated what
would happen in Ghazni during 2006 given six different combinations of
economic incentives and troop surge levels. This is an example of the
type of information a field commander would find useful when evaluating
various options. It also highlights some of the pitfalls that could
occur if the simulation results are not properly framed within the
context of key assumptions made by the model or the experimental design.

For example, we consider three different surge levels, but without
defining what form such a surge would take. Does the surge represent a
single large base of operations or multiple forward-operating bases
(FOBs)? Are the troops patrolling as a well-defended force or in smaller
groups that have more direct interaction with the local population?
Presumably these details are known in the calibration and validation of
the Afghanistan model, but such issues must be properly communicated
when presenting the results of the experiments so the commander will
have a greater degree of confidence in the model outputs and a framework
for understanding their significance.

# The Mechanics of Solution

The specific mechanics of solution vary across proposals. Some systems
use prestructured grammars to map event data patterns to plain-text
expressions (Pentland 1995). Others use human-defined templates to
aggregate event-level data (Kumar 2007). All of these solutions concern
how best to reduce the quantity and specificity of the data to the
smaller domain of the human problem being summarized in natural language
(Yu 2006, Maybury 1995). The key problems are that while replication may
boost confidence in the results (Eisenhardt 1989, Jick 1979), even
simple aggregation methods such as distance functions for binning can be
woefully inconsistent (Sabherwal 1993).

The results of the experiment referred to above would require any such
templates or grammars to be very domain---and even model---specific. The
visual-analytic tools that are described in Appendix 9 go a long way in
addressing some of the issues with aggregating the outputs of various
experiments in real-time, as well as allowing more direct and intuitive
control of simulation runs as they are progressing. Nevertheless,
additional tools and templates would be needed to, for example, perform
statistical analysis across multiple, repeated experiments. Currently in
our experiments, these types of aggregation and analysis are mainly
performed using external databases, spreadsheets, and statistical tools,
which are then summarized by expert operators of the model.

# The Art of Solution

As would be expected of any system producing output for non-expert
users, the form of the presentation matters. This means that simple
issues (for a human writer) such as word choice can have substantial
influence on the quality of the outcome (Reiter 2005). Though we think
of the machines and software as cold and antiseptic, the rhetoric we use
in framing the map from data to natural language also can have a
substantial impact on the message the end-user takes away (Golden-Biddle
1993).

Such a presentation would necessarily include a translation or mapping
from the simulation data to real-world situational parameters. For
example, it is not enough to say that 400 CF agents and pro-government
economic incentives are needed to achieve favorable mindshare for the
Ghazni region. The definition of a synthetic agent in terms of how many
soldiers (and what type of soldiers) it represents must be incorporated
to articulate a meaningful COA. Also, a description of the costs, the
assumptions, and the detailed characteristics of these COAs would have
to be included so that the commander can fully understand the issues and
tradeoffs involved in choosing among multiple options suggested by the
simulation experiments. Presumably, these mappings would be an inherent
aspect of a rigorously designed and validated model. Still, integrating
these parameters into a useful presentation of experimental results is a
necessary component of generating a natural language narrative for the
nonexpert end user.

***Appendix 9 -- Visual Analytics***

Our agent-based simulation allows users to visualize the results of
experimenting with different social theories and scenarios in
Afghanistan. Here we see the original NetLogo interface (A9-1). Notice
the large portion of screen-space dedicated to sliders and other control
elements.

**Figure A9-1**. Original NetLogo home page

![](media/image276.png){width="5.125in"
height="3.2291666666666665in"}![](media/image277.png){width="5.126388888888889in"
height="3.025in"}

NetLogo is limited to a single perspective that is tied to the viewable
map area. Similarly, the additional infovis views in the agent-based
tool, such as the bar chart and the time-series view, are also tied to
this single perspective.

The main purpose of this agent-based tool is to allow the user to
manipulate variables within the simulation and visualize the effects of
the changes. Some of these variables are global in that they affect the
simulation of the entire country, while others are tied to fixed single
locations or to specific regions. It is clear that without proper
organization, an exponential number of controls is needed to capture all
combinations of all the variables. In fact, Figure A9-1 shows many of
the \~200 sliders that are needed to operate the original NetLogo model.

To facilitate control and analysis of the Afghanistan model, we apply
the "probe" concept to the existing visualization of the NetLogo
agent-based system. We define a probe as a pair consisting of a
user-defined region-of-interest and a pane containing any variety of
information visualizations coordinated to depict and interact with the
data within that region-of-interest. The region-of-interest and the
visualization pane are linked either directly (e.g., by a line) or
indirectly (e.g., the region-of-interest and the pane's background are
shaded the same color).

To create a probe, the user selects a region-of-interest (e.g., a
central focal point and extent radius, or a manually selected
irregularly shaped region) and then chooses a location for the
visualization pane to be overlaid directly within the main geospatial
visualization. Once created, a probe visually presents a focused, local
view into the dataset/model along with an intuitive visual linkage back
to the overall dataset/model.

The introduction of probes almost completely transformed the NetLogo
tool (Figure A9-2).

Figure A9-2. An example workspace in our new, probe-based interface.
Notice that the user can add any number of different overview maps.
Probes can then be inserted into these maps, spawning linked coordinated
visualization/interaction panes. This extends observation and
interaction across all levels, from global to individual cells.

![](media/image278.png){width="5.4in"
height="2.0388888888888888in"}![](media/image279.wmf){width="5.989583333333333in"
height="2.254861111111111in"}

In addition to the issue of overcrowding from excessive sliders (Figure
A9-1), the agent-based tool suffers another equally severe interaction
issue, in that the sliders offer no spatial context in terms of their
relationships to the corresponding geographical regions. Users and
observers of this agent-based simulation are often left wondering which
slider to operate to affect a specific region-of-interest. This
incongruity between the visualization and its controls greatly
diminishes the effectiveness of the simulation as an experimental
platform for testing social theories.

By applying our probe concept, we can greatly increase the effectiveness
of the interface. As can be seen in Figure A9-2, multiple maps are now
allowed, with each map colored based on a particular dimension in the
data (e.g., ethnic group, loyalty, etc.). Most importantly, the \~200
sliders can now be replaced by an "on-demand" tabbed control panel of
sliders directly associated with each probe (Figure A9-3). This
combination of sliders with geolocated probes makes the effect of each
slider intuitive and obvious, in that interaction with a slider now
affects only the region tied to its corresponding probe.

The implication of a visualization that has capabilities for both
passive inspection and active manipulation is striking. As shown in
Figure A9-3, the user has selected two nested regions to test the impact
of an increased economy in a small selected region and its effect in the
surrounding areas. With the probe interfaces, the user can directly
modify the economy of the small selected region and observe its effects
in the probe associated with the surrounding areas.

**Figure A9-3**. The use of localized control capabilities is shown in
this scenario. Here, the user places a probe (the smaller circle) over
the city of Kabul and expands the control section of that probe's
interface and manipulates local variables to test out a new social
theory within the city limits. A second probe (the larger circle) has
been added to encompass the surrounding region, which has some pockets
of Taliban loyalists (brown cells). This probe is set up to graph the
relative populations of various factions over time. The user can easily
see that after the new social theory is enacted within Kabul, the number
of Taliban agents (white line) in the surrounding areas decreases.

![](media/image280.wmf){width="5.7340277777777775in" height="5.83125in"}

Figure A9-4. Shown here, the user has created two probes, one over a
Taliban-controlled area (magenta circle/brown cells) and one over a
coalition-controlled area (yellow circle/green cells). Each is set to
display the relative populations of each type of agent using parallel
coordinates. Then by choosing to create a direct comparison (bottom
right pane) of the two probes, the user can see the values from each
region-of-interest together in a single visualization.

![](media/image281.wmf){width="5.995138888888889in"
height="4.980555555555555in"}

A common task when testing social theories is to directly compare two
regions-of-interest. With the probe interface, this task is again
trivial and obvious. As shown in Figure A9-4, a comparison pane can be
created between two existing probes to visualize the relationships
between the two regions. Numerous possible operations are possible
within this framework. In this example, the user has selected a "union"
operation, combining the two selected regions into a single view,
immediately revealing the differences in population characteristics.

The most obvious benefit gained by adding probes to this application is
the ability to inspect multiple local regions at once. Whereas the
original fixed visualizations once reflected the global model's values,
we now have any number of dynamically created visualizations, each able
to reflect the values in regions-of-interest of every size and shape
imaginable. We can now provide superior comparison capabilities by
directly presenting regions-of-interest together or separately in their
own visualizations.

The probe interface also allows geospatial-based manipulation of the
simulation and visualization. The user gains the freedom to choose
regions-of-interest of any size and shape and interact with their
properties directly, facilitating experimentation of complex social
theories and immediate visualization of their effects. Finally, by
replacing the static interface with an unrestrained workspace, enabling
and encouraging the user to add and remove "on-demand" interface
elements as needed, we not only remove the original clutter and wasted
screen space, but also extend the single-user application into one that
has potential to support multiple users.

***Appendix 10 -- Visual Plots of NetLogo and ORMAC Comparisons***

**Figure A10-1**. Time-series plots of each run, outputs from both
NetLogo and ORMAC simulations

\(a\) 3 days/tick, legitimacy theory

![](media/image282.png){width="5.290277777777778in"
height="3.9631944444444445in"}

\(b\) 3 days/tick, representative theory

![](media/image283.png){width="4.852777777777778in" height="3.64375in"}

\(c\) 3 days/tick, coercion theory

![](media/image284.png){width="5.168055555555555in"
height="3.8715277777777777in"}

\(d\) 7 days/tick, legitimacy theory

![](media/image285.png){width="5.045833333333333in"
height="3.779861111111111in"}

\(e\) 7 days/tick, representative theory

![](media/image286.png){width="5.080555555555556in"
height="3.814583333333333in"}

\(f\) 7 days/tick, coercion theory

![](media/image287.png){width="4.9in" height="3.670138888888889in"}

\(g\) 14 days/tick, legitimacy theory

![](media/image288.png){width="5.168055555555555in"
height="3.8756944444444446in"}

\(h\) 14 days/tick, representative theory

![](media/image289.png){width="5.15625in" height="3.8756944444444446in"}

\(i\) 14 days/tick, coercion theory

![](media/image290.png){width="5.1506944444444445in"
height="3.8583333333333334in"}

[^1]: These descriptions are copied verbatim from the WITS database.

[^2]: We use this assumption for two main reasons: (1) it has some
    intuitive appeal and (2) the project has no better data available
    for benchmarking the per-agent state-of-mind.

[^3]: We used a two-tailed t-test that assumes unequal variance.

[^4]: The three leadership theories are defined as follows: (1)
    coercion: people of the local population are motivated primarily by
    coercion from their leader, which depends on his resources, (2)
    legitimacy: people of the local population are motivated primarily
    by their high loyalty to their leader, and (3) representative:
    people of the local population are motivated primarily by their
    economic situation, their dislike of violence, and their ideology.

[^5]: The CAS can exhibit such flash-point behaviors, but none of our
    simulations operate on a time scale of as little as one day, so a
    5-day cycle may be justified.