# Presentation: 534886

## Verification and Validation as Applied EpistemologySAND 2007-2628

**Supported by Sandia National Laboratories LDRD Program 08-1158**** **

- Laura A. McNamara, George Backus
- Exploratory Simulation Technologies
- Timothy G. Trucano
- Optimization and Uncertainty Quantification
- Sandia National Laboratories

**Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration ****under contract DE-AC04-94AL85000.**

## Here’s the plot:

- Since 1998, the Department of Energy/NNSA National Laboratories have invested millions in strategies for assessing the credibility of computational science and engineering (CSE) models used in high consequence decision making.
- The answer? *There is no answer.* There’s a process  - and a lot of politics.
- The importance of model evaluation (verification, validation, uncertainty quantification, and assessment) increases in direct proportion to the significance of the model as input to a decision.
- Other fields, including computational social science, can learn from the experience of the national laboratories.
- Some implications for evaluating ‘low cognition agents’

**Notes:**
Similarly, the importance of model evaluation increases as models are used to ‘bust paradigms’ (e.g., challenging classical economic theory)

## Why “Applied Epistemology?”

- Epistemology considers the question, How do we know what we [think we] know?
- Science: What makes Western science special in producing reliable, predictive knowledge about the world?
- V&V takes epistemology out of the realm of thought and puts it into practice.
- What is the role of modeling and simulation in the production of reliable, credible scientific knowledge about the world?
*What steps, investments, practices do I pursue to convince myself that the model I have developed is producing credible knowledge? *
- In large organizations, getting stakeholders to sign up for V&V can be... umm, a challenge.
- Too hard, too slow, too expensive...

## Rom Harre on the pitfalls of models:

- “If we regard theories as descriptions... of reality produced by the human imagination, it is clear that there must be some account of the constraints upon that imagination, for the human imaginative faculty is well-known for its capacity to generate mere fantasy: and yet, it is plain that the conceptions of reality which scientists have drawn upon from time to time are not fantasies, though at the end some have been abandoned as unrealistic.”

*V&V is a methodology of constraint*

## Why care about V&V for computational social science?

- Too much skepticism about models
- “paradigm-busting” role of modeling and simulation in the social sciences
- V&V can strengthen claims by tying model back to real world
- Too little skepticism about models
- Search in intelligence, defense, policymaking communities for predictive technologies
- V&V can give ‘reality check’ about where model is (and isn’t) valid

**Notes:**
Marks notes that economics has been slow to embrace computational simulations of economic phenomena because of a lack of confidence in the results that the models show us.  If nothing else, better validation reduces skepticism.

Ironically, from my vantage point inside the national security community, there’s perhaps too little skepticism about the promise of modeling.  

## La prévision, c’est très chic!

- NSTC: “Network models and analyses ... will allow for the development of more effective predictive strategies.”
- IEEE: “The US intelligence community... is looking for better ways to identify terrorists... and predict where and when they will strike.”
- DNI: “Predictive network-centric intelligence assessment offers the ability to foresee or warn of threats, be these strategic or tactical and conventional or asymmetric.”

**Notes:**
The first quote is from a report entitled ‘Combating Terrorism’ that was released in National Science and Technology Council’s subcommittee on social, behavioral, and economic sciences.  I note that of the five references on modeling, simulation, and prediction in the report, four of them were more that 20 years old.   Despite the emphasis on modeling and simulation for prediction, the problem of verification and validation isn’t addressed in the report.

The last one is a sentence from a paper that wone the 2006 galigleo award, a n intelligence community awards program that honors IC offerces with innovative ideas to shape the future of intelligence.  To his credit, the author 

## Slide 7

## The purpose of computing is to provide “high-performance, full-system, high-fidelity-physics predictive codes to support weapon assessments, renewal process analyses, accident analyses, and certification.” (DOE/DP-99-000010592)

- The purpose of computing is not insight. 

**Notes:**
Modeling is predictive if it represents a major means of providing information to a weapons expert in all stockpile stewardship applications relevant to the simulation and
Is the primary (or only) means in specific stockpile critical situations.

## Drivers for V&V and UQ in CS&E

- Codes have historically played a critical role in certifying high-consequence systems that can’t be repeatedly tested in a fully representative environment
- Nuclear power plant operations
- Nuclear waste storage and containment
- Aerospace applications
- As computational power increases, so expands the demand for high-fidelity, predictive computational simulations
- Nuclear weapons performance, safety, reliability
- Should drive demands for higher credibility

## V&V Definitions

- VERIFICATION:
- The process of determining that a computational software implementation correctly represents a model of a physical process... or
*The process of determining that the equations are solved correctly*
- VALIDATION:
- The process of determining the degree to which a computer model is an accurate representation of the real world from the perspective of the intended model applications...or
*The process of determining that equations are correct*

## V&V is a methodology.

**Code**
**Verification**

**DP**
**Application**

**Planning**

**Experiment**
**Design, Execution**
**& Analysis**

**Metrics**

**Assessment**

**Prediction **
**& Credibility**

**Document**

**Calculation**
**Verification**

**1**

**7**

**6**

**5**

**2**

**4**

**3**

**3**

**8**

- Requirements and planning

**Verification**

**Validation Metrics**

**Credibility**

**Permanence**

**Validation Experiments**

## Let’s simplify and also bring this up to date:

- Let’s simplify and also bring this up to date:

**Application(s)**
**Requirements**

**PLANNING**

**Credibility/BE+U**

**M&S**

**V&V**

**Metrics**
**Assessment**

**Referents**

**V&V results mean ****what?**
**Use within decision****environment?**

**Risk-Informed Decision Analysis (RIDA)**

**PCMM**

## Simulation as a third corner of science

- ‘Reality’

- Conceptual Model

- Experimental design,
- Simulation predictions

- Computer Simulation

- Observation and Analysis;
- hypothesis formulation

- Model development and
- code predictions

- Ang, Trucano, Luginbuhl 1998

***Are***
***data valid?***

## “Too hard, too slow, too expensive...”

- CS&E
- Computational science and engineering is mature, capable, and reliable for the responsibilities being demanded of it.
- I’ve used this code for such a long time that I trust it.  I turn the knobs, I calibrate, and I get the right answer.
- V&V is... (see above)
- CSS
- Models don’t make decisions; they provide insights to decision makers
- The code isn’t ready for V&V.  When it’s more mature, we’ll do V&V.
- V&V for *this* code?  We don’t have the money. Besides, it’s up to the analyst to use the code right.
- If V&V reveals problems with the code we’ve invested so much money, time, energy... (finish the sentence).

**Notes:**
The title of this slide  comes from Tim Trucano’s signature line.  It emphasizes that getting the larger community that comprises computational science and engineering to accept and implement rigorous techniques and methodologies for verification and validation has been a real fight for the researchers that believe V&V and uncertainty analysis are important.
Below are some of the themes that I’ve encountered when speaking to people about V&V in both the national laboratories and, to a lesser extent, with people who do computational social science.  
The first is one taken directly from a report that Sandia recently issued on V&V benchmarks.  

Codes are well documented, widely used, and community understands limits to applicability. If there were problems, we’d know what they are because the code gets exercised all the time.  
The second is the idea that if the code seems to be working, why mess with it.   

## Implications for low cognition agents

## Busting the Rational, Utility Maximizing Decision Maker

- Individuals gather, weigh, process information to maximize utility (rational behavior)
- Problem: this assumes a lot of cognitive power in very high dimensional environments
- Problem: classical economic theory doesn’t predict business cycles (or other cycles) very well
- What if agents are more efficient in processing information, making decisions, than traditionally assumed?

**Notes:**
Does this offer us a more cogent theory of reality?
Is it predictive?  On what scales?
Are the predictions useful? 

## LOW COGNITION AGENTS

- DATA: Aggregate data seem to follow an exponential  or power law distribution
- This implies something about the relationship between agent and structure
- MODELING THEORY: ‘Degree of cognition that needs to be assigned to agents decreases as system matures’
- IMPLICATIONS:
- People have minimal cognitive ability
- OR - People develop heuristics that help them simplify complex information environments
- OR - ‘Structure’ establishes boundaries on quality, quantity of information required for agents to make a ‘good’ decision
- OR - None of the above – ‘low cognition agents’ don’t actually say much about agents themselves.

**Notes:**

Right there are some telling questions for V&V:
CONSIDERING THE CONCEPTUAL MODEL:  There is a posited relationship between “system maturity” and “degree of agent cognition.”   What does it mean to say that a system is mature? At what point does a system comprise a system that constrains the degree of agent behavior?  

Are we imposing order on an unpatterned base? 
DATA VALIDATION: 
Does that power law distribution really exist? Is it the best fit for the data?
EXPLANATION:
Assuming it does exist, what generates this pattern?
HYPOTHESIS:  
Herd behavior means that small perturbations can feed on each other, amplify

## Low cognition agents

- ‘Reality’

- Conceptual Model

- Experimental design,
- Simulation predictions

- Computer Simulation

- Observation and Analysis;
- hypothesis formulation

- Implementation of
- conceptual model in code
- Mathematics works right?

- Ang, Trucano, Luginbuhl 1998

***Are***
***data valid?***

**Notes:**
V&V is about testing all the posited relationships between the simulation and the ‘real world,’ and figuring out ways to strengthen those.  Ideally, you do this by trying your darndest to break the model.  
Question number one: validity of patterns that different data sets exhibit.  Do the power laws exist?
This leads us to some hypothesis formulation about a model – that simple behaviors can generate wild fluctuations.  It also leads to a hypothesis about formulating a model that can replicate patterns – namely a low cognition model that generates a simulation. 
This, in turn, generates some kind of a conceptual model 
Assumption that the data reflect something that is ‘real’ – which in itself is problematic in the social sciences. 

AGENT is represented as rules for decisions
E.g., firm output decisions reflects the ’sentiment’ (“irrational exuberance?”) of the collective, an agent’s weighting of that sentiment, plus a random variable to reflect uncertainty
STRUCTURE is represented in model as topology; and protocols governing agent interactions

## REFERENCES

- Ang, J., Trucano, T. 1998. Confidence in ASCI Scientific Simulations. Albuquerque, NM: Sandia National Laboratories.  SAND 98-1525c.
- Axelrod, R. 2003. Advancing the Art of Simulation in the Social Sciences. *Japanese Journal for Information Management Systems.*12(3)
- Goldstein, H. 2006.Modeling terrorists: New simulators could help intelligence analysts think like the enemy. *IEEE Spectrum* September: 34-43.
- Harre, H.R. 2003. Modeling: Gateway to the Unknown.  Amsterdam, NL: Elsevier Press.
- Marks, Robert E. 2003. ‘Coffee, Segregation, Energy and the Law: Validating Simulation Models.’ GET FULL CITATION
- Trucano, T., Garasi, C., Mehlhorn, T. 2005. ALEGRA-HEDP Validation Strategy. Albuquerque, NM: Sandia National Laboratories (SAND 2005-6890).
- Oberkampf, W.L., Trucano, T. 2007. Verification and Validation Benchmarks. Albuquerque, New Mexico: Sandia National Laboratories (SAND 2007-0853).
- Pilch, M., Trucano, T., Moya, J., Groehlich, G., Hodges, A., Peercy, D. 2000.  “Guidelines for Sandia ASCI Verification and Validation Plans – Content and Format: Version 2.0.”  Albuquerque, NM: Sandia National Laboratories, SAND 2000-3101.
- Smith, T. J.2007.  “Predictive Network Centric Intelligence: Toward a Total Systems Transformation of Analysis and Assessment.” Washington, DC: Director of National Intelligence.

## Auxiliary Material

## Agency and structure

- Where does society reside, if not in the individual?
- How is the social reproduced across generations?
- Is the social world comprised by parts whose dynamics are structured in terms of an externally imposed cause (structuralism)?
- To what extent are individual actions constrained by society?
- To what extent do the actions of individuals change society?
- How much of a rebel am I, really?
- Nature or nurture? What is free will?
- Do low cognition agents have anything to say about these questions?

**Notes:**
Harre differentiates between atomism and structuralism.  Is the world a ‘flux of independent, non-overlapping, unconnected and passive elementary parts... so that the dynamics of such a world [are] structured in terms of an [efficient, externally-imposed] cause?   Or does the world comprise “the operation of formal causes – that is, the imposition of structure or pattern on an unstructured, unpatterned base. Such imposition is conceived by structurists as the evolving of an antecedently given structure, under the constraint of certain invariants in the structure or pattern...” (33).  Structuralism is about the imposition of pattern on an unpatterned base.  This is where grounded dispositions fits in, ‘since the pattern of experience... is derived from the structural properties of that object [that are the] grounds of the disposition.”

## CSS vs CSE

- High-consensus ‘laws,’ rules, theories exist
- Implemented mathematically with (relative) ease
- Theory is explanatory and predictive

- Multiple theories explain the same set of phenomena
- Theories expressed in narrative form
- Theories are explanatory and descriptive

**Notes:**
Because we’re such a young field, we might turn to fields in which computational modeling and simulation are more established.  I work in one of those fields - namely, the nuclear weapons laboratories, and in a mathematics organization in a computational science and engineering center at Sandia National Laboratories. 

A few years ago, a colleague David Hakken writing a review of literature on anthropology and computation noted that the anthropology of computers and computational anthropology hadn’t yet found a way to speak to each other.   I believe that Verification and validation provides one of the mechanisms to begin that conversation.  More specifically, I am an anthropologist who works among the mathematicians, statisticians, physicists and engineers who worry about verification, validation, and the quantification of uncertainty in computational modeling and simulation applications. One of my co-authors, Timothy Trucano, is a mathematician who played a key role in convincing the Department of Energy to invest in a verification and validation program.  My other co-author, George Backus, has been developing agent-based models of industrial-economic systems for 20 years now.  This paper is the result of a conversation among the three of us and, more recently, Rich Colbaugh, who we’re quickly dragging into the philosophical and methodological morass that comprises our discussions.  

There is certianly increased interest in V&V among social scientists who are interested in using computational models for knowledge production. 

To my knowledge, researchers engaged in computational social science seem only weakly acquainted with the extensive work that our colleagues in the computational science and engineering community have done on V&V.   In (X) references on V&V for CSS, rarely did I come across references on V&V from the communities have really invested heavily in the problem - engineering, physics. 

NOT IN AXELROD
NOT IN CAFFE NERO PAPER
BECKER, HIEHAVES AND KLOSE MENTION TWO REFERENCES FOR INDUSTRIAL MOD0SIM
NOTHING IN MARKS

My argument here is that we’ve got a lot to learn from these communities, even though their problem sets are so different from ours. . 

## So where is the computational social science community?

- Axelrod: Does the program correctly implement the model? (internal validity)
- Carley: processes and techniques for addressing comparability between simulated world and ‘real’ world ... (external validation)
- Marks: How successfully the model’s output exhibits the historical behaviors of the real world target system (‘output validation’, cf Manson 2002)

**Notes:**
Axelrod notes that achieving what he calls ‘internal validity’ is 

## Q: Why do we create models?

-  Kinds of models
- To highlight features of a phenomenon we have observed (to describe, explain, predict)
- As our observations mature, so can our conceptual models
- The Ptolemaic universe
- The Copernican universe
- To represent a conception of a phenomenon we have not yet observed
- Superstrings in cosmological physics
- Roles that models play
- Models fix a mental representation, collective or otherwise, of a phenomenon occurring in the world around us
- Models are frameworks for organizing inquiry
- Models enable knowledge to evolve

- A: Because we can’t do science without them.

**Notes:**
Verification and validation are, at heart, a problem of epistemological practice. Pursuing verification and validation asks us to consider how we know what we know, or how we convince ourselves that we are producing reliable knowledge about the world – then to apply these principles to a method, tool, theory, process, or practice we employ in the produciton of knowledge.   

One of my favorite philosophers is Horace Romano Harre, a philosopher of science who has written extensively and specifically on the role of models – not simulations per se, but modes – in scientific knowledge production. 

## Uses of agent-based simulations

- Explain a phenomenon, explore a phenomenon, understand interactions that produce a phenomenon (Marks 2003)
- Insight into system control, make predictions, derive general principles (Haefner)
- Prediction, performance, training (flight simulators), entertainment, education (SimCity), existence proofs, discovery, *gedankenexperiment* (Axelrod 2003)

**Notes:**
Robert Axelrod recently (2003) wrote a nice paper in which he describes computational social simulation as a young field with greater promise than, perhaps, its current accomplishments support. 

We don’t yet constitute a community - we’re dispersed across dozens of journals, disciplines, organizations, funding streams - not to mention applied vs. academic applications of the technologies we’re developing. 

Axelrod lists seven uses for models, including economic prediction, performance of tasks - e.g. in robotics or expert systems; training for human operators of complicated equipment, entertainment - the same flight simulator as a game in an arcade; education, such as Sim city, existence proofs, such as the principle that simple behaviors can
Axelrod is interested in simulation as a third leg of science, as a technology for gedankenepxeriments

What Axelrod doesn’t address here is the difference between simulation as a methodology for doing research - itself an epistemologically fraught enterprise - and the use of simulation in policy making or high-consequence decision making. 

NONE IN AXELROD

## Terminology

- CSE: Computational Science and Engineering
- CSS: Computational Social Science (to include agent-based models)
- V&V: Verification and Validation
- UQ: Uncertainty Quantification