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AGENT-BASED SIMULATION AND MODEL INTEGRATION Alok Chaturvedi, Purdue University Daniel Dolk, Naval Postgraduate School Hans-Jürgen Sebastian, University.

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Presentation on theme: "AGENT-BASED SIMULATION AND MODEL INTEGRATION Alok Chaturvedi, Purdue University Daniel Dolk, Naval Postgraduate School Hans-Jürgen Sebastian, University."— Presentation transcript:

1 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

2 AGENT-BASED SIMULATION AND MODEL INTEGRATION Agent-based Simulation (ABS) Model Integration OR/MS ABS ABS: Bio-terrorism and traffic models ABS OR/MS: ABS as Continuous Experimentation Artificial labor market for US Army recruiting

3 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

4 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.

5 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

6 MODEL INTEGRATION EXAMPLE: OR/MS OR/MS Demand Forecasting [Multiple regression] Financial [Monte Carlo simulation] Pricing [Optimization] Manufacturing [Discrete event simulation] Transshipment [Linear programming] Volume Mfg_ExpenseDist_Expense Price Dist_Expense Mfg_Expense Volume Net IncomeRevenue

7 MODEL INTEGRATION: ABS 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?

8 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

9 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

10 CHALLENGES OF ABS INTEGRATION : Agent Representation in Measured Response Gene1 Gene type: Gender Gene value: 0001 - Male Gene information is extracted from the data to accurately represent the behavior of the agent Gene2 Gene type: Education Gene value: 0011 - High School 1 1 01 0 01 Decision Factors form the second helix

11 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

12 CHALLENGES OF ABS INTEGRATION (INTRA-PARADIGM): Agent Communication Intelligence Savings Group I D X S E U C T Financial Life Food Water Person Environ. Shelter Print Electronic Do NothingSecurity Basic Communication Exposure Rumor True Infected Immune Well Being Communicate Carrier I S E D X C U nitiate earch valuate ecide E ecute ommunicate erminate DNA-like Behaviors, Ports, and Channels architecture allows accurate representation of an agent’s intelligence and behavior Behavior Primitives T pdate Health Liberty Safety Environment

13 CHALLENGES OF ABS INTEGRATION (INTRA-PARADIGM): Agent Communication in TrafficLand Agents communicate via: Direct messages Usage of resources Inheritance of characteristics and abilities

14 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)

15 MODEL INTEGRATION: ABS 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

16 ABS AS “BLACK BOX” Demand Forecasting [Agent-based simulation] Financial [Monte Carlo simulation] Pricing [Optimization] Manufacturing [Discrete event simulation] Transshipment [Linear programming] Volume Mfg_ExpenseDist_Expense Price Dist_Expense Mfg_Expense Volume Net IncomeRevenue

17 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

18 MEASURED RESPONSE: EPIDEMIOLOGICAL MODEL AS CALIBRATOR OF ABS Susceptible-Infected-Recovered (SIR) model for population N=S+I+R with no disease mortality. Mass action transmission process, rate  linear recovery rate  SIR

19 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

20 ABS AS CONTINUOUS EXPERI- MENTATION: PARALLEL WORLDS Real World Environment Learn: Explore, Experiment, Analyze, Test, Predict Implement Behavior modeling, demographics, and calibration Data collection, association, trends, and parameter estimation Time Compression Near exact replica of the “real” world SEAS architecture Supports millions of Artificial agents Decision Support Loop SyntheticEnvironment The user(s) can seamlessly switch between real and virtual worlds through an intuitive user interface. SCM ERP CRM Data Warehouse Simulation Loop XML Interfaces UNIX/ORACLE Real World and Simulation Databases Real World and Simulation Databases Assess DECISION

21 ABS AS CONTINUOUS EXPERIMENTATION PROGRAMMING AGENTS: Data Mining using Econometric Models, Neural Networks, etc to Specify Preferences CALIBRATING AGENTS: OR/MS models to Validate Market Behavior OPTIMIZATION MODEL: “Where are the best locations for Recruit Stations?” ARTIFICIAL LABOR MARKET DEMAND MODEL: “What will be the recruit pool by race, gender, and location next year?” DATA WAREHOUSE

22 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

23 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

24 USAREC AGENT PROCESS Adjust factor strengths Budget amount Recruiter number … Season = Spring GDP = 1.5% … Port Process Channel 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.

25 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)

26 SUMMARY ABS 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 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


28 AGENT-BASED SIMULATION Characteristics of ABS ABS and DES (discrete event simulation) ABS and System Dynamics ABS and Virtual or Synthetic Environments

29 COMPARISON OF AGENT-BASED and DISCRETE EVENT SIMULATION DES relies upon probability distributions and equational representations “Bottom up” (ABS) vs. “Top down” (DES)

30 COMPARISON OF ABS and SYSTEM DYNAMICS ABSSystem Dynamics Process: InductiveProcess: Deductive Unit of analysis: agent / individual Unit of analysis: feedback loop / structures Focus: Exploratory research Focus: Confirmatory research

31 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)

32 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

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