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Verification, Validation and Accreditation of Agent-Verification, Validation and Accreditation of Agent-Based Simulations Based Simulations

Deborah Duong

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PurposePurpose

• To introduce Agent-Based Simulation• To propose measures of effectiveness for Agent-Based

Simulation

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What is an Agent-Based Simulation?What is an Agent-Based Simulation?

• “Agent-Based Simulation” (ABS) is broadly defined– An ABS is a simulation in which entities have “agency”– Agents can perceive and behave in their environment based on

goals

• Agent-Based Simulation is used for modeling living systems – Biological and social systems– Non-living systems are mindless, and therefore don’t have

“agency”

• The concept of “emergence” is important– Agents behave according to one set of rules– New patterns “emerge” from individual behaviors– Emergence is micro-macro integration

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How does Agent-Based Simulation Compare ?How does Agent-Based Simulation Compare ?

• Other methods that don’t involve agency or minds are also used to describe living systems

– Discrete Event Simulation• Events of a process are scheduled to occur at discrete points

– System Dynamics Simulation• Looks at the flow of “fluid” levels over time• Time delays are important

– Social Networks• Patterns in the arrangement of entities to each other are important

• These methods are at their best when modeling “non-mental” phenomena

– Ecology• Predator-Prey cycles

– The Economy• Cycles not based on “beliefs” (like the stock market is)

– Any time entities act similarly• Everybody eats!

• Non-agent simulation methods model flows and arrangements of “averaged” entities

– Their “State” does not change, because entities are not modeled explicitly– They are not “networked”– They are viewed from an external, “etic” standpoint

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Why some Computational Social Scientists prefer ABSWhy some Computational Social Scientists prefer ABS

– Their preference depends on their feelings on the importance of “agency” and minds

– They may believe that other tools are not as rich• Other tools tend to make “heroic assumptions” • They often can not model the crux of the problem• They are more descriptive than causal

– North and Macal: • “We believe that in the future virtually all computer simulations will

be agent-based because of the naturalness of the agent representation and the close similarity of agent models to the predominant computational paradigm of object-oriented

programming.”

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Cognitive vs. Reactive AgentsCognitive vs. Reactive Agents

Agent Types

Cognitive Reactive

Meaning changes Meaning is Hard Coded

Interpretations come from Autonomous Perception

Interpretations come from Copying other Agents

Learn based on Experiences React the same way every time

Coevolves: behavior changes social structure while social structure changes behavior

New starting conditions form different patterns but rules of behavior do not change during the simulation

Heavy Computation Light Computation

Typically uses Machine Learning Techniques

May use static rules

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Data-Based vs. Theory-Based ABSData-Based vs. Theory-Based ABS

Agent Based Simulation Types

Data-Based Theory-Based

Concerned with modeling a single instance of what actually happened and will happen

Concerned with modeling what is possible, based on theoretical principles

Is initialized with the detailed data of a scenario

Starts with a random “primordial soup” from which data emerges

Purpose is to explore plausible next states given the initial state

Purpose is to model causes of states

Stopping start: the initial state is not necessarily something that could emerge from the simulation itself

Running start: Difficult to match to a particular data set: data must be “grown” from a previous state

More descriptive: to fit data, correlations tend to be enforced without the modeling of cause

More causal: No data to fit, only relations between events

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Agent Based Simulation and VV&AAgent Based Simulation and VV&A

• Verification– Determination of whether a simulation expresses a theory well

• Validation– Determination of whether a simulation has fidelity with the real

world• Accreditation

– Determination that a simulation is useful for analysis of a particular domain

• Verification, Validation and Accreditation of agent based models is problematic– VV&A originated in physics models– The nature of social science has implications for agent based

VV&A

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Agent-Based Simulation and VerificationAgent-Based Simulation and Verification

• The more a simulation has the power to express a theory, the more the simulation is verified– A System Dynamics model of a verbal theory wouldn’t have a high

degree of “verification” unless that theory was about time-delays

• The referent of any mathematical or simulation model is a theory– In physics based models, verification is “doable”

• In physics-based models, verification is mainly about bugs• Replication, or using a different method to simulate the same theory, can

help debug agent based social models

• In social-science based agent models, verification is the central issue– Verification is about technology to represent an idea

• Newton had the technology of the calculus

– The technology to simulate social theories is not trivial• For example, a social theory about human learning may need a computer that can match a human in learning

– With knowledge of available tools and creativity, Verification is just a matter of good (scientific) taste, for now

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The Social Literature as the ReferentThe Social Literature as the Referent

• Fitting raw data is not enough for verification– Data can be over-fitted– One could “simulate” by never addressing cause, by only making

correlated things appear magically• Since “why” is not modeled, the simulation is not generally applicable• If it wont model a new situation, it wont model itself well either

– If there are no causes a level under the phenomena you model, you are only describing, not analyzing

• You can not explore the new levers to change outcomes, other than the ones you put in the simulation to begin with

• Data should be fitted through a theory of social science – Thoughtful models in the social literature are preferred to models from

other fields• Just because we have the tools to describe time delays, physical

phenomena, and epidemiology doesn’t relate them to social theory• Knowledge of all tools is needed to model the richness of the social world • Tested by: surveying the relative frequency of issues in the social literature

and comparing to the relative frequency of issues in an ABS

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Agent-Based Simulation and ValidationAgent-Based Simulation and Validation

• The more explanatory power an agent-based simulation has, the more the simulation is validated

• A simulation model should match the data in the world in the way that its theory matches it– Validation of agent based simulation is dependant on

verification: If an agent based simulation is not first verified, it will not be valid

– Validation of agent based simulation is dependant on the explanatory power of its referent theory as well

– Technology that enables verification enables exploratory creation of theories with explanatory power

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What can we expect from an ABS?What can we expect from an ABS?

• To address validation, let us ask, what can we expect from a theoretically perfect ABS?– Even if the agent based model was completely correct, it

still could not do long term prediction• The social world is full of “Schelling Points”: arbitrary phenomena

– We can expect it to display similar patterns to the real world, but not the exact data of the real world

• It should have the same correlative patterns– Links between events in a simulation should have a similar strength to

links between corresponding events in the real world• It should develop a distribution of plausible results similar to the real world

– Tested by “separating the test set from the training set”• It should be able to make a short term prediction of “types” of phenomena

– A live connection to data is essential• An agent based simulation *is* a theory

– It is a theory represented in a form amenable to computation

– The theory that best matches the (patterns in) data is the best theory

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Validating Agent-Based SimulationsValidating Agent-Based Simulations

– Data-Based vs. Theory-Based Agent models: How do we simulate both theory and data well?

• The trajectory of a theory-based simulation can be made to pass through particular data

– Random number massaging– Co-evolutionary “seeding”

• Because the data emerges from the simulation itself, it models the next state better

– It is validated if it models not only patterns in data, and the social literature well, but it also models causation well

• Ockham’s razor: If many known phenomena emerge from a few known phenomena, you have modeled a cause well

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Agent-Based Simulation and AccreditationAgent-Based Simulation and Accreditation

• Rating for a usage in a domain is based on correctness of past usage in that domain– Pattern-based correctness

• Social Science simulations are so complex, that scientific insight is needed in each new application– There is no way to generalize what tool will always be

good in advance for what domain– Accreditation efforts should be devoted to confirming that

a simulation does have expressive and explanatory power after the tool is chosen for the application

• When is a model ready for use in analysis? – When it predicts patterns in data and the occurrence of

“types” of events consistently when given new data

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Myths of Agent-Based VV&AMyths of Agent-Based VV&A

• “Chaos theory says there is no order, and any small change makes a big change in the outcome”– The social world is full of order and homeostasis

• “The cause of emergent phenomena is so complex that it is unknowable”– Cause is knowable because it is contained “in the box”

– Scientific experiments can tease out cause• Computer experiments can “hold all else the same” better

than real world experiments can• Statistics can find cause in Monte Carlo ABS

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Implications for Existing VV&A TechniquesImplications for Existing VV&A Techniques

• Exploratory Space and Risk Analysis– Testing simulations at the boundaries where it matters

– Nonlinearities in agent-based simulation means we don’t know where it matters

– “Agency” can be taken advantage of in strategic data farming

• Bottom-up VV&A– Making sure that the lower level is VV&A’d and that will

take care of the upper level

– But you don’t know what to emphasize in the lower level until after the emergence happens

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SummarySummary

• Agent Based Simulations model “Agency”– ABS are best used when mental processes and dynamic

networks are important

• ABS may be typed according to two dimensions– Cognitive/Reactive

– Data-Based/Theory-Based

• There is hope for Agent Based Simulation Verification, Validation and Accreditation– We have ways to measure

• Similar patterns to the real world correlative data• Match to the social theory in literature • Explanatory power (Ockham's Razor)