AGENT-BASED SIMULATION AND MODEL INTEGRATION

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

2. 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

3. 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.

4. 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

5. MODEL INTEGRATION EXAMPLE:OR/MS <-> OR/MS Demand Forecasting [Multiple regression] Volume Volume Transshipment [Linear programming] Manufacturing [Discrete event simulation] Mfg_Expense Pricing [Optimization] Dist_Expense Price Mfg_Expense Financial [Monte Carlo simulation] Dist_Expense Volume Net Income Revenue

6. 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?

7. 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

8. 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

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

10. 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

11. I Financial Savings Security Do Nothing Group Life S Food E Water Well Being Basic D Shelter X Print Rumor Communicate Communication U Electronic True C Person Infected Exposure Carrier Environ. Immune T CHALLENGES OF ABS INTEGRATION (INTRA-PARADIGM): Agent Communication Intelligence Behavior Primitives I nitiate S earch Health E Liberty valuate Safety D ecide X ecute E U pdate Environment C ommunicate DNA-like Behaviors, Ports, and Channels architecture allows accurate representation of an agent’s intelligence and behavior T erminate

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

13. 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)

14. 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

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

16. 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

17. S I R MEASURED RESPONSE: EPIDEMIOLOGICAL MODELAS CALIBRATOR OF ABS • Susceptible-Infected-Recovered (SIR) model for population N=S+I+R with no disease mortality. • Mass action transmission process, rate b, linear recovery rate g.

18. 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

19. 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.

20. 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

21. 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

22. 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

23. USAREC AGENT PROCESS Process Channel Adjust factor strengths 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.

24. 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)

25. 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

26. BACKUP SLIDES

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

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

29. COMPARISON OF ABS and SYSTEM DYNAMICS

30. 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)

31. 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