Transcript Verification, Validation and Accreditation of Agent

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Verification, Validation and Accreditation of AgentBased Simulations
Deborah Duong
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Purpose
• To introduce Agent-Based Simulation
• To propose measures of effectiveness for Agent-Based
Simulation
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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 ?
•
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 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 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 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&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 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
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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 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 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?
• 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 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 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&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 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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Summary
• 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)
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