# Presentation: 892087

## Modeling and Simulation for National Security Decision Makingor “A Plea for a V&V and UQ Community of Practice”

**Laura A. McNamara, PhD**
**Timothy G. Trucano, PhD**
**Sandia National Laboratories**
**Albuquerque, NM **

**Sandia Review and Approval**
**2005-7679 C**

**Notes:**
Tell story of IC guy and wanting computational model of China. 

Why we should be concerned about computational modeling: in areas like global climate change, it plays a significant role in shaping the political activity around global warming. Edwards speaks of a ‘tight coupling between climate science and policy at the highest levels.”  Are we seeing the same thing in social science and intelligence research?  Could we? What role should model projections play in policy making? 

On page 444, Edwards talks about the problems that global climate modelers have in trying to root large scale variables in small scale processes.  The problem isn’t dissimilar, on its surface, to the problem that social science modelers face.  He notes that modelers invent ‘ad hoc’ schemes to establish the necessary connections between small and large scale variables, so that the problem is computable.  This sounds like social science models to me. He notes as well that these are established as parameters.  

The title of my paper is Human behavior, physics, complexity, and computational modeling – where are the anthropologists?   The title of this paper was generated by a question I hear frequently from modelers, physicists, and decision makers that I work with: how do anthropologists do this kind of modeling? The answer is that, typically, we don’t, and this paper was conceived as a way for me to learn more about the relationship between computational models and anthropology.  

In this talk, I’m going to present some of the issues that this study has raised for me. 

hat’s interesting is that the use of computational models to explore social phenomena has gelled more completely in certain areas of the physics community, particularly in conjunction with our archaeology colleagues, but is far less prevalent in the cultural anthropology community.  

What I’ve become increasingly interested in is the question of how these models should be, or are used, as tools for the production of knowledge. In archaeology, computational models are used in a more academic sense, as research tools. But in policy settings, computational models of social phenomena are a hot topic among analysts and decision makers who are trying to grapple with some very complicated problems. 

This is where I think we can learn a great deal about how to question the epistemological status of computational models from applied physicists at the national laboratories who are grappling with the use of computational physics tools in critical decisions about engineering problems. 

In the next 15 minutes, I’m going to define what I mean by a model, how it differs from a simulation, and demonstrate an example of the kind of model that I’m specifically interested in, a class of models known as agent based models.  I’ll talk a little bit about the intersection between complexity physics and anthropology and the kind of models that intersection has produced, and I’ll explain some of the reasons why I think computational modeling is very difficult for anthropologists.  Finally, I’m going to raise the issue of how these models might be used in decision making... (something here about NW experience). 

## Part One

**Computational Social Simulation, **
**or**
***“******Scaling to China”***

**Notes:**
As an anthropologist who works at Sandia National Laboratories, one of the Department of Energy’s three nuclear weapons design laboratories, I am frequently invited to meetings and seminars that address the social and cultural dimensions of national security issues.  Recently, at one such meeting, I participated in a conversation about the role of computational social science in intelligence analysis of socioeconomic issues.  One of my Sandia colleagues – a computational physicist – had recently been put in charge of several programs that Sandia is pursuing in computational psychology.  This colleague was giving an overview of some of his staff’s work in computation and cognition to a group of visitors from another branch of the US government.  As he explained his researchers’ projects in computational modeling of cognitive styles, one of the visitors interrupted the presentation with a question.  Given that there exist a set of validated personality profiles, he asked, such as those used in criminology or law enforcement, would it be possible to use such profiles to program an agent based model to analyze and predict future trends in another country?  My physicist friend answered with little hesitation. “If we had the right scaling laws,” he said, “I don’t see why not.”   Another researcher in the room, a metallurgist, compared the current state of computational social science to an earlier phase in the evolution of modeling and simulation for materials. “We’re doing things now with computers that I’d never have thought possible two decades ago,” he said, implying that computational social science is on the brink of a similar capability revolution. 

## DECISION MAKING; or,Who cares about  Balinese Water Temples?  

**Lots of computational social science takes place outside of decision context**
**Anasazi Longhouse   **
**Balinese Water Temples (Lansing)**

## Computational Science 

**Why Models are Fraught with Conflict**

## CUD STUDY:Mod-Sim, Forecasting, and Decision Making

**Notes:**
To study this issue, we  and simulation tools are used in a many areas: WEATHER FORECASTING, PUBLIC HEALTH/EPIDEMIOLOGY, CLIMATOLOGY/GEOPHYSICS, ECONOMIC AND BUSINESS FORECASTING, DESIGN AND OPERATION OF NUCLEAR POWER PLANTS, ENVIRONMENTAL IMPACT ANALYSIS, NUCLEAR WEAPONS

Not eliminating judgment – risk informed decision making, not risk BASED decision making. Nobody does the basing on 

Intuitive, complex, heavily intuition with meaning of increasingly constrained on what an analytic approach.   SBSS is the most 
Models are not faster better cheaper, and they don’t end.  This is why designers are still part of the picture.  Designers are NEVER replaced by anything – informations ources richer, more diverse, and better targeted for decision making.  Leads to a reorganization of the community that is making the knowledge.   These are profoundly disruptive technologies

Faster, better, cheaper.  Anther point of disputation is third leg of science: discourse around CS is nothing like discourse around experimental vs. therotical science.   We beliee better, but we don’t believe faster, and we don’t get cheaper at all, eveh though that’s dead wrong.  In some cases you might get faster, 

The focus here is on information analysis and synthesis, though strictly speaking, the two concepts aren’t often delineated clearly in these communities.  For example, intelligence analysts do a great deal more synthesis than analysis.  (This becomes important when we talk about modeling and simulation, where information is integrated – synthesized – though they’re often discussed as analytical tools). 

In considering the role of modeling and simulation in each of these areas, we reviewed literature from within these communities as well as scholars who’ve written about these communities.  We considered the goals of the community: is it pure knowledge production, as in academia, with little emphasis on decision making; or is the community engaged in some kind of decision support? - for example, weather forecasting or economic forecasting. We considered the flow of information to each community: where does information come from, how do 

 In some of this writing, consideration of the epistemological issues of modeling and simulation is quite explicit – for instance, Thomas Stewart is a cognitive psychologist who’s written extensively on how weather forecasters use models to make predictions, and in doing so, he emphasizes the close and often explicit coupling between knowledge production and decision making.  

## Modeling and Simulation in Intelligence Analysis

**A Case Study**

## PART ONE

**Models in Physics**

## Model vs. Simulation

**MODEL represents one or more concepts and the relationships among them.**
**SIMULATION reveals what emerges as the relationship is invoked over time. **

- As A increases, B decreases

- TIME

- INCOME

**Notes:**
Science depends on the existence of models, which enable us to apply reasoning across a number of cases or examples of a phenomenon of interest. A model is an humanly constructed abstraction, a way of representing reality so that we can make sense of it. 

How a model is defined is a very controversial topic and it’s not one that I’m going to dive into here.  For my purposes, let’s say that a model simply expresses a relationship, or a set of relationships, between one or more concepts.  Models begin as purely conceptual entities that are represented in some kind of sensible format: an equation, in narrative, in a 3 dimensional sculpture.   For example, I’ve used English to represent a simple model of an inverse relationship: as A increases, B decreases.   A and B are what we call parameters; they’re sometimes referred to as variables, though technically a variable is a type of parameter.  

A simulation, in contrast, can be thought of as model plus computer.  In a simulation, a computer enables the researcher to  vary the model’s parameters. The resulting simulation invokes the relationship over time, in generic chunks of time that are called ‘time steps,’ and aggregates the results for analysis. Let’s say A is time off and B is income.  What the simulation would show is the level of income over time as a function of time off. Computers enable us to visualize what kind of patterns might emerge from the relationship as we’ve represented it in the model. 

Models are useful abstractions. tools; we develop and use conceptual models of the world all the time.  In anthropology, we have models for understanding kinship, political hierarchy, economic relationships and modes of production, and countless other areas of knowledge that we consider important in our discipline.  As I’ll discuss later, we tend to represent our models in narrative format, which raises special challenges for modeling and simulation. 

There are many different schools of computer modeling that are capable of, or focused on, the exploration and replication of social phenomena.  Social network models are perhaps the most familiar to anthropologists; systems dynamics models are another kind of model.  For this talk, I’m going to focus on one kind of model, because it’s the one that complexity physicists and anthropologists seem to be converging on, and that’s an agent based model.   Agent based models are composed of individual elements, called agents, which are programmed with rules for their behavior.  They sit in a spatial-temporal framework, so they can move through space as the model is run through a series of time steps. As the model runs, the agents interact, and different patterns emerge.  The rules can be very simple.  

## From Physics Knowledge to Computational Model

**PHYSICS **
**KNOWLEDGE**

**Mathematical **
**Representation**
**(e.g., PDE, ODE)**

**‘****Solutions’**

**Notes:**
Note that this is elicited from work with Tim, that he’s a mathematician and computational physicist, mentor for understanding models in knowledge production. According to Tim, verification and validation in the ‘old’ regime were straightforward: mathematical proofs provided verification that the mathematics are correct, while validation took place in the form of experiments.

Now, all this changed in the mid 1940s, when the Manhattan Project invented computers to do extremely complicated calculations for bomb design. Does anyone know where the word computer came from?  They were women, mostly the well educated wives of Los Alamos scientists, working on mechanical adding machines.  It was a handoff process.  I believe they were assembled and their production process designed by Richard Feynman.  Later, in the 1950s, mechanical computers were invented by IBM – ENIAC AND MANIAC were the first two – and later superseded by Cray mainframe machines.  Now we’ve got massively parallel processors, and they’re enabling the development of some very complex, three dimensional computational modeling and simulation tools, or as they’re known in the physics and engineering world of the national laboratories, ‘codes.’

Discuss difference between pencil and paper to computational physics.  Turnley and trucano on pencils....

## CODES IN X DIVISION

**CODES IN X DIVISION**

**Notes:**
For most of the national laboratories’ history, researchers have used computer modeling software, which in this community is referred to as a code, in the design process.  In the nuclear weapons design division, X Division, where I did some of my anthropological fieldwork,  designers had access to multiple codes, each of which was preferred for different kinds of problems.   Codes had a distinct place in the design process (describe).  So codes supported the production of knowledge in tandem with small scale experiments and full scale nuclear tests. 

Codes were also tightly embedded in the community’s design culture.  Codes had lineages and histories – in the manuals that accompany the codes, it’s not uncommon to have a statement of lineage that tells the reader the history of the code, what its predecessors were, who developed each version of the code, if it was developed using pieces of other codes.  Often, these tools were designed by the very people who would later design the small scale experiments and the full-scale nuclear tests.  Codes represented a numerical expression of the community’s collective knowledge, its analytical processes.  They were precipitates of the ongoing flow of knowledge producing activity.  

Designers were quite comfortable interpolating between code results, or between codes, or between codes and experiments – in other words, to predict an unknown point from within a space that existed between two known points - but these codes weren’t built for extrapolation – in other words, for prediction from the known to the unknown.

That changed once testing ended.  This is where I’ve spent most of my intellectual energy, in understanding teht ransition from 

## A Changing Role for Physics and Engineering Codes

**1945-1992: Computational models for “numerical experiments”**
**NW Testing program ends, 1992**
**Science Based Stockpile Stewardship, 1995  **
**Accelerated Strategic Computing Initiative**
**“****Virtual test environment” for nuclear physics and engineering problems**
**Computational models for **
**knowledge integration?**
**Diagnostics**
**prediction**
**certification? **
**The deterrent will be effected/affected by decisions we make with these models**

## From Design and Test toVerify, Validate, and Quantify Uncertainty

**VERIFICATION: is the model solving the equations correctly and accurately?**
**VALIDATION: does the model output compare well with experimental data?**
**UNCERTAINTY QUANTIFICATION: What are the primary sources of uncertainty; are they reducible; and how do these dictate investment in research priorities? **

**Notes:**
What’s been remarkable is the way that this shift in the use of models has driven the national labroatories to think far more carefully about what a model is, how it functions, what its purpose is – and as a result, what they can do with their models, and the level of confidence in them has increased dramatically
Recognition that models are no longer academic tools. An inaccurate or misleading numerical simulation for a research project has little impact when compared to a model being used to support decisions, investments in high-consequence strategies or technologies – like nuclear power plants or nuclear weapons.  The end of the CW forced this realization on the nuclear weapons laboratories, and made them ask the question, why should we trust the models we use? 
The model no longer feeds into the experiment – the model drives the experiment
I’d note that there are two places in the USG where really good work is being done on VV and UQ for computational models, and that’s the Defense Modeling and Simulation Organization and the nuclear weapons laboratories. 

## From Physics Knowledge to Computational Model

**PHYSICS **
**KNOWLEDGE**

**Mathematical **
**Representation**
**(e.g., PDE, ODE)**

**‘****Solutions’**

**Notes:**
Verification and validation in the ‘old’ regime were straightforward: mathematical proofs provided verification that the mathematics are correct, while validation took place in the form of experiments.

Now, all this changed in the mid 1940s, when the Manhattan Project invented computers to do extremely complicated calculations for bomb design. Does anyone know where the word computer came from?  They were women, mostly the well educated wives of Los Alamos scientists, working on mechanical adding machines.  It was a handoff process.  I believe they were assembled and their production process designed by Richard Feynman.  Later, in the 1950s, mechanical computers were invented by IBM – ENIAC AND MANIAC were the first two – and later superseded by Cray mainframe machines.  Now we’ve got massively parallel processors, and they’re enabling the development of some very complex, three dimensional computational modeling and simulation tools, or as they’re known in the physics and engineering world of the national laboratories, ‘codes.’

Using computer tools requires the transformation of the process. 

## PART TWO

**Complexity and Agent Based Modeling**

**Notes:**
I’ve introduced the physics paradigm as a way of providing you with some contrast between 

## Complexity, complex systems, complex adaptive systems...

**“****Something almost entirely random, with practically no regularities, would have effective complexity near zero.  So would something completely regular, such as a but string consisting entirely of zeroes.  Effectively complexity can be high only in a region intermediate between total order and complete disorder.” *******
**Complicated systems aren’t necessarily complex!**
**A complex system: nonlinear, feedback, iterative nondeterministic, resists decomposition, emergent, self organizing....**
- *From Gell-Mann, Murray. 1995. ‘What is Complexity?” in *Complexity. *New York: Wiley and Sons.** **

**Notes:**
First of all, a system can be very complicated without being complex.  For example, an airplane is a very complicated system. 
Formally, a system starts to have complex behaviours (non-predictability and emergence etc.) the moment it consists of parts interacting in a non-linear fashion. It is thus appropriate to differentiate between a complicated system (such as a plane or computer) and a complex system (such as ecological or economic systems). 

Nondeterministic – even if you knew in advance what all the parts were doing, you couldn’t predict the precise outcome. Even if every element’s action is predictable... the behavior of the system is not.

Resist decomposition – the levels in a complex system are intertwined with each other and their environment in such a way that it’s impossible to neatly tease one level out from the others. 
They’re nonlocalized.  This means that the system exhibits behaviors that can’t be precisely traced to a single area, layer, or entity in the system. 
They exhibit emergence.  This means that coherent, patterned properties of the system emerge in nonpredictable ways from the interactions and movement of parts at a lower level of organization.   

Now, as I said before, models are useful abstractions, insofar as they help us think through problems.  The real difficulty with complex systems is that they are irreducible: namely, a true model of the system would have to include all its parts and interactions, in which case it would be isomorphic with the system and, hence, useless as a guide to the system.

Complex systems theory is being applied to a lot of entities – we see it in economics – think market behaviors – in politics, in ecology, in medicine, in mathematics.  

## Consider the Boid

- Maintain separation

- Maintain alignment

- Maintain cohesion

**Notes:**

The stereotypical example of a distributed and complex system is a flock of birds. Without any central co-ordination (if we suppose that nobody in a social insect colony have such organisational power), the flock nonetheless behaves in a very coherent way. They exhibit structural flexibility, fast reaction to external environment changes, and they’re robust, meaning that they endure. 

Agent based models, which You can see some of this in the boids video, when the flock splits and re-forms whenever it encounters an obstacle. Craig Reynolds. 

## BOIDS Film

**Notes:**

The boids film isn’t one obviously that focuses on human behavior, but it’s of interest because it demonstrates that behaviors that exist at an individual level can generate phenomena, like flocking, that exist at a higher order than the individual, but that depend on the action of individuals for its existence.   
One place in which agent based models have emerged as a very important methodology is in the interdisciplinary field of complexity.  Complexity represents an intersection of physics, mathematics, and computer science that grew out of systems research – the study of entities that are composed of many interacting parts.   A complex system is defined as any dynamical system that’s composed of many similar parts that interact in a nonlinear fashion.  Nonlinearity describes a state in which a change in variable A can produce quite unexpected effects in variable B. Feedback is a characteristic of these systems as well: As variable B changes, variable A can itself change in a nonlinear fashion.   A Complex Adaptive System is one in which the entire system, composed of parts interacting in this nonlinear fashion, responds to and evolves in conjunction with the environment in which it is embedded.  This is referred to as emergent behavior; it’s the idea that phenomena at a higher order of existence are generated by activity at a lower level of organization.  

As a rule, social scientists view simulation with suspicion.  For being soft sciences, we’re a highly empirical lot of researchers.  As I’ll discuss in a minute, this is particularly true for anthropology, which is certainly makes the least use of computational tools of any of the social sciences. 

Agent based models have been used to explore human behavior since the early 1970s – really, since computer power enabled the development of simulations. The fields in which agent based models tend to be used most frequently are economics and political science; those of you who are familiar with the work of political scientist Robert Axelrod know that he has used agent based models for many years to explore the conditions under which cooperative behavior becomes a norm among states.   One of the earliest, and most famous, models is Thomas Schelling’s segregation model, which dates back to the early 1970s.     In this model, Schelling had two kinds of agents, green and red, on a grid that looks like a checkerboard.  Each agent, sitting at any point in the grid, has eight possible neighbors. He gave them a few simple rules: each agent seeks to maximize its happiness, and it’s happiest when it has a minimum of 2 of its neighbors that look just like it.  What emerges in the successive model runs is – segregation.  The lesson that Schelling drew from this model is that seemingly benign decisions we make for ourselves – I’d like to have a couple of neighbors who have a similar educational background to mine – can lead to the emergence of patterns that are detrimental for society as a whole and, ultimately, for individuals.  Conscious antipathy isn’t a precondition for social problems like segregation. 

What Schelling described sounds an awful lot like a complex system and indeed complexity theorists recognized about twenty years ago that social things, for lack of a better term, represented a topic of study to which theories and tools of complexity might be fruitfully applied. Indeed, one of the things that sparked my interest in this topic was learning that one of the lead computational physicists in the national laboratory system, Stephen Younger, who ran the nuclear weapons programs at Los Alamos, has now dedicated his career to developing agent based models of precontact Pacific island societies.  He’s actually an adjunct faculty member at the University of Hawaii now, and publishes extensively in the 

Certainly not al One of the areas in which a great deal of this work is taking place is right in my own backyard, at a private nonprofit known as the Santa Fe Institute.  This is a site for the interdisciplinary study of complexity, and since the mid 1980s they’ve been sponsoring interdisciplinary workshops on complexity and the social sciences.  An edited volume of the resulting work was released in 2000.  

## PART THREE

**Agent Based Modeling**
**And Anthropology**

**Notes:**
A good deal of the modelers that I know who are exploring the use of agent based models in cultural studies are, believe it or not, computational physicists.  I think they’re drawn to modeling social phenomena for a few reasons: one, it allows them to explore culture as an example of complexity, to apply complexity theory to one of the most complicated phenomena in our world.    

## PRIMATOLOGY: troop behaviors
ARCHAEOLOGY: hunter-gatherer foraging; settlement patterns in SW Anasazi society; the emergence of states from chiefdoms or tribal societies
CULTURAL ANTHROPOLOGY: Marriage and stratification in Tonga; Balinese water temples as an emergent system for efficient agriculture
MORE GENERAL PROBLEMS: Why cooperation or competition emerge as behavioral preferences in societies; one-on-one or spontaneous violence vs. organized warfare as a function of environmental characteristics, population size, other variables

**PRIMATOLOGY: troop behaviors**
**ARCHAEOLOGY: hunter-gatherer foraging; **_**settlement patterns in SW Anasazi society**_**; the emergence of states from chiefdoms or tribal societies**
**CULTURAL ANTHROPOLOGY: Marriage and stratification in Tonga; Balinese water temples as an emergent system for efficient agriculture**
**MORE GENERAL PROBLEMS: Why cooperation or competition emerge as behavioral preferences in societies; one-on-one or spontaneous violence vs. organized warfare as a function of environmental characteristics, population size, other variables**

**Notes:**
There is some history of anthropologists working with complexity theorists to develop tools that push anthropological research in new boundaries.  computational modeling of complex social systems.  Computer science and the mathematics of complexity could provide anthropologists with a set of tools and theories for describing how social behavior emerges and shifts over time.  The barriers, however, are formidable, and include epistemological, methodological and ethical issues. 

It’s hard to overemphasize the importance of the Santa Fe institute as a center for the study of complexity in many areas, including the social.  Begins with the interested of Mrurray Gell-Mann, a physicist whose name you’ve probably heard, on prehistoric southwest, specifically the question: ca we apply complex systems theory to explain things like settlement patterns.  Well, this kicked off a long collaboration between a small group of anthropologists and the folks at SFI, and it’s fair to say that complexity theory has indeed expanded archaeology’s tools for exploring the past. 
So what kind of work has been done in anthropology in conjunction with the Santa Fe Institute?

So what are these models good for?   One of the comp physicists that I interviewed for this paper described computational modeling as “the discovery of the blindingly obvious with computers.”  The ‘blindingly obvious” tends to exist in the relationship between the micro and macro levels of society. For example, this particular physicist that I interviewed  had developed a theory about levels of interpersonal violence in pre-contact Pacific Island societies.  

## The Anasazi Longhouse Simulation:Why was the valley abandoned? 

**Households harvest, consume their nutritional requirements of grain.**
**Households store any remaining grain for later consumption.**
**Households evaluate availability of grain for following year, based on current harvest and grain stores. **
**If the amount is insufficient, the household moves to a new viable location to farm**

**Dean, Gumerman, Epstein and Axtell, 1998. ‘Understanding Anasazi culture change through agent-based modeling’. Santa Fe Institute Working Papers ref: 98-10-094.**

**Notes:**
The Anasazi simulation is based on the Sugarscape model developed by J. Epstein and R. Axtell. The project was created to provide an empirical ‘real world’ evaluation of the principles and procedures embodied in the Sugarscape model.
The simulation aims at modelling the settlement and behaviour of the Anasazi culture in Long House Valley in northeastern Arizona between 1000 BC and 1300 AD. The study area is well suited for investigation for four reasons:
	• 	It is a topologically bounded, self-contained landscape.
	• 	There is a rich paleoenvironmental record (based on alluvial geomorphology, palynology, and dendroclimatolgy, giving an accurate quantitative reconstruction of annual fluctuations in potential agricultural production (the populace were subsistence-level agricultural farmers growing maize).
	• 	Detailed regional ethnographies are available which provide an empirical basis for deriving plausible behavioral rules of the agents on the landscape.
	• 	A large database on human behaviour during the last 2000 years has been constructed from intensive archaeological research.

The Anasazi could have stayed, but chose not to.  The reasons were sociocultural, not environmental.  We still don’t know what the reasons were. 

The model generates output that is indeed a good ‘fit’ with the archaeological data – never goes to zero, but gets close.   But is this validation, or calibration? Calibration is fitting the model to the results, and doesn’t necessarily entail a strong understanding of why the model is giving you the results that it doe.s 

## “The most humanistic of the sciences, the most scientific of the humanities...”

**“****The most humanistic of the sciences, the most scientific of the humanities...”**

**ETHNOGRAPHY**
***Verstehen***
**Experiential**
**Idiographic**
**“****Thick Description”**
**Narrative**
**Qualitative**
**Postmodern**
**Holistic****	**
**Meaning**

**ABM**
**Logical positivism**
**Distanced**
**Nomothetic**
**Abstraction**
**Simplification**
**Rules, equations**
**Modern **
**Extractive**
**Observables**

**Notes:**
 In physics, says Younger, modelers can represent physical phenomena in equations.  His models are all decisions written in if-then rules.  “If leader says X, then you do Y.”  “If hungry, you get food.”  He’s got altruism parameters, aggression parameters, drive parameters. 

Many of us who do fieldwork might look at computational modeling, especially that done by non-social scientists, as nineteenth century armchair anthropology resurrected in a computational body. ABMs are rooted in logical positivism; idea that there’s a reality ‘out there’ whose dynamics can be replicated in a model. Indeed, the discussions I’ve had with modelers reveal, to my mind at least, a remarkable naivete about ethnographic data.  For example, the physicist I mentioned earlier took as ‘truth’ an informant’s statement that he’d read in an ethnography: the informants told the anthropologist that they remembered no murders ever taking place on the island they inhabited.  For the physicist, this was evidence that interpersonal violence was a rare, if nonexistent phenomenon, in that society.  But any of us who’ve done fieldwork realize that informants have a million reasons, many difficult to tease out, for saying something to an anthropologist.    As Margaret Mead warned, "What people say, what people do, and what they say they do are entirely different things." 
One of the biggest tings that I notice when I’m talking to the physicists I know who do this work is, quite simply, that they don’t get wrapped around any epistemological axes about the validity of developing higher-level theories of universal human behavior.  Raised in an era of postmodernism, this initially struck me as highly naïve. 

Then there is the question of levels of analysis.  Ethnography, as we all know, is intensely focused on the microsocial.  One of the physicists that I’ve interviewed about his work in agent based modeling finds this somewhat  frustrating.  There are different situations that lead to violence, he said, but at a higher level, they’ve all got the effect of eliminating part of the population.  For example, his interest is population size and coordinated violence in pre-contact Pacific Island societies, and through computational models, he’s developed a theory that says, essentially, that islands with populations of fewer than 500 are too small to develop warfare-like behaviors, while islands with populations over 10000 tend to be unified hierarchically.  The danger zone is populations of a few thousand individuals.

Certainly there are some deep epistemological differences – I’ve listed some keywords on this slide – but I do believe that we should consider how computational modeling methodology might drive us to think differently about the work that we do. 
There are places where good anthropological use of computational models exists.  Michael Agar, for example... archaeological work.

But it tends to be very academic, focused on developing knowledge, as opposed to the application of this knowledge to real world problems.  And this brings me to my next topic – what happens when computational models and simulations are used in decision making? 

## Why Computational Social Modeling is Really Hard (1)

- “If X occurs,
- then Y is likely.”

- P(y,x) > .5

**Notes:**
Now, the 

Computational social science is hard for anthropologists, I think, because it involves such a dramatic transformation of knowledge.  We’ve struggled with the epistemology and ethics of translating our field experience into narrative – and I think here of Clifford and Marcus’ critique of written culture - and that was a fairly painful experience for anthropologists.  To move to another level of abstraction, to the development of rules that can be written in a computable format – as I’ve done here, using a statement of conditional probability -  is difficult.  We are reluctant to lose that description that we’ve woven s thickly.  Plus there’s the question of faith to the informants who have worked so closely with us, presumably, to provide the information that underwrites that narrative. 

## Part Four

**Agent Based Models in **
**National Security Decision Making**

## EPISIMS: Day 2

**Notes:**
The simulation I’m showing now was generated by a model called EpiSims, which was developed at Los Alamos National Laboratory to simulate the impact of a smallpox bioweapon attack.  It simulates the movement of smallpox through an virtual American city and in doing so, it tests the impact of quarantine measures on the spread of the disease.  

The images compare two scenarios: on the right, the spread of the virus under conditions of no quarantine and no vaccination, and on the right, the spread of the virus under a scenario in which the government inoculates the population and enforces a quarantine.   You can see as I go through the slides that both scenarios begin with an infeciton of 1281 individuals.  

## EPISIMS: Day 24

**Notes:**
By day 28, the scenario on the left shows an infection of 6416, with 104 deaths; on the right, under conditions of quarantine and vaccination we’ve got 2181 infected and 62 dead...

## EPISIMS: Day 34

**EPISIMS: Day 34**

**Notes:**

And on day 34, the scenario on the left shows an infection of 21467 and 501 deaths; on the right, under conditions of quarantine and vaccination we’ve got 2564 infected and 303 dead...

I’m not interested in critiquing the EpiSims model, since it’s not one that I worked on.  However, it’s a nice example of how computational models can, and are, playing a role in shaping government policy in an age of terrorism.  And I think it’s important for anthropologists to be aware of these models. EpiSims in particular relies on assumptions about patterns of social interaction that lead to human-to-human contact as a disease vector.  Ethnographic data was not used in this model, though. But I think we all agree that the kind of data that anthropologists gather all the time – micro-level data about how people lead their daily lives – can play a critically important role in developing computational models like the EpiSims model.

In fact, this model was highlighted in a recent Scientific American article about the potential of an avian flu epidemic – so it’s already being used in policy analysis and public health circles as an analytical tool and something of a forecasting tool.  

This brings me to the issue of the role of computational models in decision making.  Increasingly, the national security community – which includes a lot of federal agencies, though we tend to equate national security with the intelligence and defense communities – is interested in developing computational models as part of their analytical and decision making process.   But the knowledge production process in applied areas is far different from that in academic or ‘pure’ research; each places different demands on the model and its products.  This is where I think we can learn something from the experience of the nuclear weapons programs at the national laboratories.  

## Part Five

**The Pen and the Sword**

**Notes:**
Anthropological knowledge and involvement is critical if we’re to carry forward social science models.  But getting anthropologists to engage with these proples, particularly in an applied sense, will be challenging.  
This is perhaps the most difficult part of this talk, because I’m afraid that some of what I say will seem ridiculous.  Buf if you or your colleagues are going to engage, or attempt to engage, with anthropologists for applied national security resaons, you’ve got to be aware of this history. 

## Anthropology and National Security:An “allergic response?”

**The sordid history of anthropology**
**Applied vs. Pure Research**
**Applied research is always done with an end in mind, not with an open mind**
**First Amendment Rights**
**McCarthyism: anthropologists being watched, monitored for leftist behavior**
**Hoover’s FBI: academics in general, and anthropologists in particular, monitored in their classrooms**
**Moral epistemology of anthropology: work to preserve cultural integrity, diversity**
**Protection of human subjects**

**Notes:**
This is an area of research that we anthropologists tend to leave to the political scientists, maybe the sociologists and economists.  
So tehre are a number of issues here: the first is the fact ythat anthropology that’s performed for a specific purpose, with an end in mind – that is, a practical form of anthropology – runs into the same criticism that any form of applied research does: you’re not producing pure knowledge.  

## Cold War Anthropology:The Time of Troubles

**WWII: ‘National Character’ studies – Ruth Benedict, *****The Chrysanthemum and the Sword***
**1963-1965: Project Camelot**
**SORO/American University project**
**Develop models of insurgency/ counter insurgency**
**Chilean fiasco**
**Was the work any ‘good’?**
**National Student Mobilization Committee**
**Photocopies of anthropologists’ notes from meetings with intelligence, military officials**
**Ethics investigation**
**AAA statement of ethics in anthropological research, 1973**

**Notes:**
Many anthropologists are reluctant to engage with the national security community, in any form, because the memory of these events is clear and true within this community. 
Project camelot raised a lot of questions about applied social science for national security purposes. 
The national studient mobilizatin committee revelations were a ‘painful fiasco for the discipline. 

## Part Six

**Drawing it all Together**

## The Strong and the Weak

**STRONG Artificial Intelligence:  “I can create a machine that can reason as a human mind.”**
**WEAK Artificial Intelligence: “I can create a machine that can mimic human techniques for aggregating information.”**
**STRONG Computational Social Science: “I can create a virtual society, in which interacting agents produce cultural forms that in turn shape their behaviors.”**
**WEAK Computational Social Science: “Within a bounded problem space, I can program agents with simple rules and see what emerges; I can use evolutionary algorithms so my agents can adapt to the emergent phenomenon.”**

**Notes:**
The differentiation between the two organisations lies in the ability to reason. Specifically, ‘people have the ability to recognise, reason about and react to human institutions, that is, to emergent features. Behaviour which takes into account such emergent features might be called second-order emergence’ (as opposed to first-order emergent behaviour). This has implications for modelling human social situations since we might need to model what *effect* the macro-level properties have on agents actions.
Computational social simulation is not a point solution for understanding complicated social processes. 
Computational social simulation is only a weak decision support tool
Computational social simulation is a research method

## Modeling and Simulation in Applied Physics and Decision Making

**What’s the purpose of the model?**
**Who owns the model? Who runs it? Who interprets its results?**
**How transparent is the model? **
**How is the model verified? Validated?**
**How does the model account for uncertainty in inputs and outputs?**
**What is the social location of the model in the knowledge production process? In decision making?**
**Is there a formal basis for TRUST in a given model/simulation? **

**Notes:**
These are questions that I’ve pulled from work done at Sandia, specifically that of Tim Trucano and Bill Oberkampf, two researchers who have really pushed the NW programs to think more carefully about models, uncertainty, and decision making.  These are questions that the US nuclear weapons programs have been grappling with for about a decade, since the end of the US nuclear testing program removed their central experimental program – nuclear tests at the Nevada Test Site.   IN 1995, the DOE instituted the Accelerated Strategic Computing Initiative as a program to develop the hardware and software for a massive modeling and simulation capability that was sold, initially, as a replacement for nuclear testing.  Ten years later, the ASCI program no longer exists – it’s been restructured as the Advanced Scientific Computing program – and its goals are far more modest.  

This raises the question: how can we study and characterize the relationship  between the model and the user of the model?   One of the physicists I’d interviewed when doing my doctoral research, for example, described computational models as seductive.  “They’re like women – you wake up the next morning and you realize they’ve been lying to you the whole time.” 

I ask him to show me some of his inputs and outputs – they’re basically tables of numbers.  He uses his imagination a lot.  I know, he says, that underneath this I’m just moving numbers in arrays.  But the agents attain a reality and I’d find myself sad when one died. 

## Questions to consider when developing, using, considering a model

**Why was the model developed? What does it claim?**
**Which theory?  What is theory doing (description, explanation, prediction?)**
**What aspect of reality is the model targeting?**
**What does it mean for data to ‘represent’ some targeted aspect of social/cultural reality?**
**What theoretical framework underlies the conceptual model? **
**Is the conceptual model being driven by the computational technique being used to develop the model? **
**How do we measure the ‘fit’ between the (rule based) computational format and the (narrative) conceptual model? **
**What will the model be used for?  ‘Pure’ knowledge production or decision making? **

- [from Turnley 2004]

**Notes:**
Turnley offers a model of the computational social science modeling process, in which she argues that the modeling process can be divided into five parts: the selection of data, the development of a conceptual model, the formalization of that model into a computationally tractable representation, the production of computer code, and the generation of output.  

As Turnley points out, the two cultures problem that CP Snow described in the early 1950s  - humanism versus scientific logical positivism – 
is quite salient in the problem of developing computational social models.  The ethos of modeling is that of logical positivism and empiricism; the ethos of anthropology is interpretive and subjective. This difference plays out in very significant ways in the interface between anthropology and the modeling community. 

Take data as an example: In the computational physics world, it is quite easy to select a class of data that’s indicative of the phenomenon of interest.  That’s a lot harder in anthropology, since data and meaning don’t map to each other clearly or easily. Many structures of meaning, all valid, can be imputed to any particular set of social data. 

The second area of difficulty is the problem of developing a conceptual model. How does one choose a particular aspect of reality to represent and study in a model?  What gets included in the conceptual model, and what’s excluded, will be driven by the theory that underlies the model. 

The problem is further complicated by the fact that the modeler must select a computational technique – for example, a social network, agent based, or systems dynamics model – that forces the modeler to downselect among all possible classes of data for those that will make sense in the model.  For example, it’s possible to use both social network and agent based models to study the phenomenon of segregation.  However, the framework and data that are valuable for an agent based model – for example, data that informs a model’s rules for how an individual agent selects a neighborhood – are quite different than those used to inform a social network model that explores the same problem. 

One of the really important questions that this raises for anthropologists and modelers, I think, concerns the role of theory in the production of knowledge.  Anthropological theories are not provable, in the way a mathematical proof can be demonstrated.  We can have multiple, equally valid explanations for the same observed phenomena, and we can invoke different explanations at different times without serious cognitive dissonance.  Likewise, one class of phenomena can have quite different uses, depending on the theoretical framework the researcher invokes. Turnley uses the example of church attendance as a surrogate for faith in god.  But church attendance might be used to explore the role of religious activity in maintaining social status, or as an indicator of religious extremism, or as an one among may indices used to measure coherence in a community’s social network.  In engineering and physics, there are finite interpretations for data. The same is not true in the social sciences. 

The last question – what the model will be used for – is particularly difficult, because it raises the question of what the role of theory is in anthropology.  For computational physics and engineering in the nuclear weapons programs, models are quite explicitly meant to support predictive analysis, and the theories that underlie these models are capable of predicting many – though not all – physical phenomena. 

At what level of analysis is the model being used?  What kind of decisions is the model intended to support, and at what level of impact?  For example, a social network model that’s being used to understand why women in one community perform better in drug rehabilitation programs than women in another might be used to make a decision about developing social programs that increase the tightness of social networks in the latter community.   But a social network model that’s being used to explore what roles are key roles in a terrorist network might be used in an applied setting, for example a law enforcement setting, to select individuals for detention or to disrupt the network.   At that point, the role of theory in the social sciences as a descriptive or explanatory framework changes, as the theory itself is being used, implicitly, to make a decision that rests on an implicit prediction about the importance of a particular individual.  

The way in which anthropological knowledge is represented shifts dramatically in the process of developing a computational social model, from the narrative that we are so comfortable with, to more abstract sets of rules, to computer code in a particular software language.