AGENTBASED SIMULATION AND MODEL INTEGRATION

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AGENT-BASED SIMULATION AND MODEL INTEGRATION

• Alok Chaturvedi, Purdue University
• Daniel Dolk, Naval Postgraduate School
• Hans-Jürgen Sebastian, University of Aachen
• IFIP WG7.6. Workshop on Virtual Environment for
  Advanced Modeling (VEAM)
• January 2-3, 2004
• Honolulu, HI

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AGENT-BASED SIMULATION AND MODEL INTEGRATION

• Agent-based Simulation (ABS)
• Model Integration
• OR/MS lt-gt OR/MS
• ABS lt-gt ABS Bio-terrorism and traffic models
• ABS lt-gt OR/MS
• ABS as Continuous Experimentation
• Artificial labor market for US Army recruiting

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CHARACTERISTICS OF AGENT-BASED SIMULATION

• Simulation composed of one or more classes of
  agents
• Each agent corresponds to one or more autonomous
  entities in the simulated domain
• Agents have behaviors, often defined by a set of
  simple rules (computational models of behavior)
• Agents can adapt dynamically
• Agents can communicate with environment and with
  each other
• Bottom up, emergent behavior results from
  nonlinear interactions of agents
• Inductive vs. deductive (computational
  explanation)
• Complexity emerges from simplicity

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MODEL INTEGRATION

• The creation of complex models by the reuse and
  composition of existing validated models
• Models may be from many different paradigms
• Optimization - Database
• Econometric forecasting - Neural networks
• Discrete event simulation - Partial diff. eqns
• Agent-based simulation - Network flow
• Monte Carlo simulation - Markov chains
• System dynamics etc, etc.

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TYPES OF MODEL INTEGRATION

• Black Box independent solvers parameter passing
• Communicating Processes partially interwoven
  solvers parameter passing
• ABS as Continuous Experimentation All models
  work from the same synthetic environment

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MODEL INTEGRATION EXAMPLEOR/MS lt-gt OR/MS
Demand Forecasting Multiple regression
Volume
Volume
Transshipment Linear programming
Manufacturing Discrete event simulation
Pricing Optimization
Mfg_Expense
Dist_Expense
Price
Mfg_Expense
Financial Monte Carlo simulation
Dist_Expense
Volume
Net Income
Revenue
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MODEL INTEGRATION ABS lt-gt ABS (INTRA-PARADIGM)

• Example 1 Measured Response bio-terrorist ABS
  developed at Purdue University uses 3 underlying
  models
• Epidemiological (smallpox, ebola)
• Traffic/transportation mobility of the populace
• Crowd psychology
• Example 2 TrafficLand ABS developed at
  University of Aachen for modeling commuter
  traffic
• What are the obstacles to integrating these two
  ABS?

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MEASURED RESPONSE AN ABS FOR BIO-TERRORISM

• Measured Response (MR) is a synthetic environment
  that simulates the consequences of a
  bio-terrorist attack in fictitious mid-sized
  cities.
• MR is developed on the Synthetic Environment for
  Analysis and Simulation (SEAS) platform.
• SEAS allows the creation of fully functioning
  synthetic economies that mirror the real economy
  in all its key aspects by combining large numbers
  of artificial agents with a relatively smaller
  number of human agents to capture both detail
  intensive and strategy intensive interactions.
• Over 450,000 artificial agents mimic the behavior
  of the citizens such as the feeling of well-being
  in terms of security (financial and physical),
  health, information, mobility, and civil
  liberties.
• MR models the rate of transmission of infections
  as a function of population density, mobility,
  social structure, and life style using an
  explicit spatial-temporal model.
• It uses the movement of individuals and the
  exposure of susceptible individuals to infected
  individuals to model the spread of disease.

Model human behavior, emotions, mobility,
epidemiology, and well being
Calibrate the models based on theoretical results
Validate the results against empirical data
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TrafficLand AN ABS FOR COMMUTER TRAFFIC

• Simulates commuters decision-making and
  behavior
• Commuters have options between work and home
  based upon
• Expected travel times
• Personal characteristics
• Interactions with other commuters
• Heterogeneous agents

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CHALLENGES OF ABS INTEGRATION Agent
Representation in Measured Response
Decision Factors form the second helix
1
Gene information is extracted from the data to
accurately represent the behavior of the agent
1
0
0
1
0
1

• Gene2
• Gene type Education
• Gene value 0011 - High School

• Gene1
• Gene type Gender
• Gene value 0001 - Male

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CHALLENGES OF ABS INTEGRATION Agent
Representation in TrafficLand

• Agents consist of
• Sensors collection of observations
• L-graphs dynamic semantic networks
• Sets of individual strategies
• Preferences pre-specified or inherited
• Satisfaction measures for strategies
• Action-executing modules

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CHALLENGES OF ABS INTEGRATION (INTRA-PARADIGM)
Agent Communication
Intelligence
Behavior Primitives
I
nitiate
S
earch
Health
E
valuate
Liberty
Safety
D
ecide
X
ecute
E
U
pdate
Environment
C
ommunicate
DNA-like Behaviors, Ports, and Channels
architecture allows accurate representation of
an agents intelligence and behavior
erminate
T
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CHALLENGES OF ABS INTEGRATION (INTRA-PARADIGM)
Agent Communication in TrafficLand

• Agents communicate via
• Direct messages
• Usage of resources
• Inheritance of characteristics and abilities

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CHALLENGES OF ABS INTEGRATION (INTRA-PARADIGM)

• Agent Representation
• Conceptual models for agents are completely
  different in MR and TL
• Genes in MR are attributes genes in TL are
  strategies
• How to map individual agent in MR to one in TL
  and vice versa
• Agent Behavior
• Agent behavior in MR is function of attributes
• Agent behavior in TL is dynamic based upon sensor
  data
• Agent Communication
• Inconsistent ACLs between MR and TL
• How does an agent in TL communicate with an agent
  in MR?
• Bottom Line ABS have low level of reusability in
  traditional sense Black box integration may be
  best we can hope for (if applicable)

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MODEL INTEGRATION ABS lt-gt OR/MS
(INTER-PARADIGM)

• Problems are less intractable in this situation
• Several options exist
• Black box ABS as just another model with data
  aggregated to the right granularity (e.g., ABS as
  demand forecast model in previous example)
• OR/MS models as determinants of agent behavior
• OR/MS models as ABS calibrators / validators
• ABS as Continuous Experimentation ABS as
  platform for OR/MS models which work in the
  virtual world established by the ABS

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ABS AS BLACK BOX
Demand Forecasting Agent-based simulation
Volume
Volume
Transshipment Linear programming
Manufacturing Discrete event simulation
Pricing Optimization
Mfg_Expense
Dist_Expense
Price
Mfg_Expense
Financial Monte Carlo simulation
Dist_Expense
Volume
Net Income
Revenue
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MEASURED RESPONSE MATHEMATICAL MODELS AS
DETERMINANTS OF AGENT BEHAVIORS

• Agent based Computational Environment
• Genomic Computing
• Behavior and Mobility Modeling
• Epidemiological Modeling and Calibration
• Person in the Loop

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MEASURED RESPONSE EPIDEMIOLOGICAL MODELAS
CALIBRATOR OF ABS

• Susceptible-Infected-Recovered (SIR) model for
  population NSIR with no disease mortality.
• Mass action transmission process, rate b, linear
  recovery rate g.

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ABS AS CONTINUOUS EXPERIMENTATION

• Simulation as a persistent process
• Continuous availability of a virtual, or
  synthetic, environment for decision support (ex
  artificial labor market)
• Continuous, near real time sensor data from
  real world counterpart (via data warehouse)
• Parallel worlds interaction
• Agents in the ALM developed using existing OR/MS
  models as data mining tools from the data
  warehouse
• Calibrate the ALM using existing OR/MS models
• ABS as test bed for OR/MS models

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ABS AS CONTINUOUS EXPERI-MENTATION PARALLEL
WORLDS
Simulation Loop
Time Compression
Decision Support Loop
Near exact replica of the real world
Real World Environment
Assess
Synthetic Environment
Behavior modeling, demographics, and calibration
SCM ERP CRM Data Warehouse
SEAS architecture Supports millions of Artificial
agents
Data collection, association, trends, and
parameter estimation
Learn Explore, Experiment, Analyze, Test, Predict
Implement
DECISION
XML Interfaces UNIX/ORACLE Real World and
Simulation Databases
The user(s) can seamlessly switch between real
and virtual worlds through an intuitive user
interface.
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ABS AS CONTINUOUS EXPERIMENTATION
DATA WAREHOUSE
CALIBRATING AGENTS OR/MS models to Validate
Market Behavior
OPTIMIZATION MODEL Where are the best locations
for Recruit Stations?
PROGRAMMING AGENTS Data Mining using
Econometric Models, Neural Networks, etc to
Specify Preferences
DEMAND MODEL What will be the recruit pool by
race, gender, and location next year?
ARTIFICIAL LABOR MARKET
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ABS AS CONTINUOUS EXPERIMENTATION USAREC
ARTIFICIAL LABOR MARKET

• Agent-based simulation designed to capture the
  dynamics of a labor market
• Agents represent individuals, or cohorts, in the
  labor market
• Humans play role(s) of organizations
• Agents programmed with rules of engagement
  genetic structure

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ABS AS CONTINUOUS EXPERIMENTATION DESIRABLE
ATTRIBUTES OF AN ARTIFICIAL LABOR MARKET

• Scalable
• Agent Compression Ratio ( Agents /
  Individuals) ? 1.
• Decomposable
• Markets can be segmented by any criteria, e.g.,
  by region,
• by life style, by race, by gender, etc.
• Evolutionary
• Agents adapt to environment and to markets
• Interaction with Real Counterpart
• Agents learn from behavior in the real
  environment
• Persistent
• Always available
• Laboratory for new OR/MS model development

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USAREC AGENT PROCESS
Process
Adjust factor strengths
Channel
Port
Budget amount Recruiter number
Port
Season Spring GDP 1.5
Port
Ports and channels structure allow us to have
access to each agent in the Synthetic
Environment e.g. we can implement self
service, targeted advertisement, etc.
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USAREC AGENT UNIVERSE

• Only considered 1.4 million individuals, age
  17-21, interested in Army
• Modeled 100,000 agents to represent this
  population
• Agent compression ratio 14
• Agent DNA consists of (age, gender, race,
  mental_category, education, region)

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SUMMARY

• ABS lt-gt ABS Integration
• Reusability of simulations tends to be low
• Integration most likely to occur at black box
  level
• Integration of ABS requires consistent agent
  representation and communication protocols
• ABS lt-gt OR/MS Integration
• OR/MS models link to ABS rather than to one
  another
• May promote more consistency amongst models
• Integrated data
• ABS can serve as integrative environment for
  using OR/MS models for data mining, calibration,
  and new analysis

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BACKUP SLIDES
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AGENT-BASED SIMULATION

• Characteristics of ABS
• ABS and DES (discrete event simulation)
• ABS and System Dynamics
• ABS and Virtual or Synthetic Environments

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COMPARISON OF AGENT-BASED and DISCRETE EVENT
SIMULATION

• DES relies upon probability distributions and
  equational representations
• Bottom up (ABS) vs. Top down (DES)

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COMPARISON OF ABS and SYSTEM DYNAMICS
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CHALLENGES TO MODEL INTEGRATION

• Model Representation develop a uniform
  representation usable across paradigms
• exs structured models (Geoffrion)
  metagraphs (Blanning and Basu)
• graph grammars (C. Jones)
• Model Communication develop a mechanism for
  models to communicate with one another (e.g.,
  pass variables)

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CHALLENGES TO MODEL INTEGRATION

• Model Selection / Composition (Web services
  problem) which model(s) are the most
  appropriate for a problem and how do we sequence
  the solvers?
• Paradigm Tunnel Vision
• Algorithm vs. Representation Focus