AGENT-BASED SIMULATION AND MODEL INTEGRATION

Agent-based Simulation (ABS)Model Integration

OR/MS <-> OR/MSABS <-> ABS: Bio-terrorism and traffic modelsABS <-> OR/MS:

ABS as Continuous Experimentation

Artificial labor market for US Army recruiting

CHARACTERISTICS OF AGENT-BASED SIMULATIONSimulation composed of one or more classes of agentsEach agent corresponds to one or more autonomous entities in the simulated domainAgents have behaviors, often defined by a set of simple rules (computational models of behavior)Agents can adapt dynamicallyAgents can communicate with environment and with each other“Bottom up”, emergent behavior results from nonlinear interactions of agentsInductive vs. deductive (computational explanation)Complexity emerges from simplicity

MODEL INTEGRATION

“The creation of complex models by the reuse and composition of existing validated models”Models may be from many different paradigms:

Optimization - DatabaseEconometric forecasting - Neural networksDiscrete event simulation - Partial diff. eqnsAgent-based simulation- Network flowMonte Carlo simulation - Markov chainsSystem dynamics etc, etc.

TYPES OF MODEL INTEGRATION

Black Box: independent solvers; parameter passingCommunicating Processes: partially interwoven solvers; parameter passingABS as Continuous Experimentation : All models work from the same synthetic environment

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 Volume

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Price

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Net Income Revenue

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 populaceCrowd psychology

Example 2: TrafficLand ABS developed at University of Aachen for modeling commuter trafficWhat are the obstacles to integrating these two ABS?

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 againstempirical data

TrafficLand: AN ABS FOR COMMUTER TRAFFIC

Simulates commuters’ decision-making and behaviorCommuters have options between work and home based upon

Expected travel timesPersonal characteristicsInteractions with other commuters

Heterogeneous agents

CHALLENGES OF ABS INTEGRATION : Agent Representation in Measured

Response

Gene1

Gene type: Gender

Gene value: 0001 - Male

Total (202)

1st Brigade

(36)

2nd Brigade

(26)

3rd Brigade

(47)

5th Brigade

(53)

6th Brigade

(40)

Plan to continue education 4.13 4.34 3.69 4.32 4.16 3.97Other employment interests 4.00 4.28 3.58 4.26 3.81 4.00Can't get out if don't like the Army 3.61 3.62 3.76 3.37 3.71 3.64Commitment too long 3.60 3.49 3.96 3.57 3.58 3.54Doesn't allow enough contact with family/friends 3.51 3.57 3.81 3.42 3.44 3.44No personal life in Army 3.47 3.54 4.12 3.26 3.43 3.26Want to stay close to home 3.42 3.44 3.65 3.19 3.46 3.49Do not want to be deployed overseas 3.34 3.40 3.58 3.16 3.27 3.42Other military services more appealing 3.26 3.32 3.72 2.91 3.36 3.17Be behind my civilian peers in career 3.25 3.44 3.50 3.14 3.12 3.24Army pay very low 3.20 3.24 4.04 3.02 3.04 3.00Have financial ability to pay for college 3.15 3.49 3.08 3.24 2.88 3.16Army too dangerous 3.15 3.32 3.38 3.17 3.10 2.87Basic training/boot camp too difficult 3.14 3.09 3.58 2.89 3.11 3.21Army life too difficult 3.12 3.20 3.16 2.91 2.96 3.50Family/friends have negative attitude of Army 2.74 2.63 3.12 2.35 2.71 3.13Army has no role to play in global environment 2.73 2.48 2.96 2.57 3.00 2.61Army conflicts with religious beliefs 2.32 2.23 2.38 2.35 2.23 2.46

= Highest mean score= 2nd highest mean score= 3rd highest mean score

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

CHALLENGES OF ABS INTEGRATION: Agent

Representation in TrafficLand

Agents consist of:Sensors: collection of observationsL-graphs: dynamic semantic networksSets of individual strategiesPreferences: pre-specified or inheritedSatisfaction measures for strategiesAction-executing modules

CHALLENGES OF ABS INTEGRATION (INTRA-PARADIGM): Agent Communication

Intelligence

Savings

GroupI

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

CHALLENGES OF ABS INTEGRATION

(INTRA-PARADIGM): Agent Communication in

TrafficLand

Agents communicate via:Direct messagesUsage of resourcesInheritance of characteristics and abilities

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 strategiesHow to map individual agent in MR to one in TL and vice versa

Agent BehaviorAgent behavior in MR is function of attributesAgent behavior in TL is dynamic based upon sensor data

Agent CommunicationInconsistent ACLs between MR and TLHow 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)

MODEL INTEGRATION: ABS <-> OR/MS

(INTER-PARADIGM)

Problems are less intractable in this situationSeveral 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 behaviorOR/MS models as ABS calibrators / validatorsABS as Continuous Experimentation: ABS as platform for OR/MS models which work in the virtual world established by the ABS

ABS AS “BLACK BOX”

Demand Forecasting

[Agent-based simulation]

Financial[Monte Carlo simulation]

Pricing[Optimization]

Manufacturing[Discrete event simulation]

Transshipment[Linear programming]

Volume Volume

Mfg_Expense

Dist_Expense

Price

Dist_Expense

Mfg_ExpenseVolume

Net Income Revenue

MEASURED RESPONSE: MATHEMATICAL MODELS AS DETERMINANTS OF AGENT

BEHAVIORS

Agent based Computational EnvironmentGenomic Computing

Behavior and Mobility ModelingEpidemiological Modeling and CalibrationPerson in the Loop

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 linear recovery rate

Idt

dR

ISN

I

dt

dI

SN

I

dt

dS

S I R

ABS AS CONTINUOUS EXPERIMENTATION

Simulation as a persistent processContinuous 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” interactionAgents in the ALM developed using existing OR/MS models as data mining tools from the data warehouseCalibrate the ALM using existing OR/MS modelsABS as test bed for OR/MS models

ABS AS CONTINUOUS EXPERI-MENTATION: PARALLEL

WORLDS

Real WorldEnvironment

Learn: Explore, Experiment, Analyze, Test, Predict

Implement

Behaviormodeling,

demographics,and calibration

Data collection,association,

trends, and parameterestimation

TimeCompression

Near exact replicaof the “real” world

SEAS architectureSupports millions ofArtificial agents

Decision Support Loop

SyntheticSyntheticEnvironmentEnvironment

The user(s) can seamlessly switch between real and virtual worlds through an intuitive user interface.

SCMERPCRMData

Warehouse

Simulation Loop

XML InterfacesXML Interfaces

UNIX/ORACLEUNIX/ORACLE

Real World and Real World and Simulation DatabasesSimulation Databases

Assess

DECISIONDECISION

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 bestlocations for Recruit

Stations?”

ARTIFICIAL LABOR MARKET

DEMAND MODEL:“What will be the recruit

pool by race, gender, and location next year?”

DATA WAREHOUSE

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

ABS AS CONTINUOUS EXPERIMENTATION: DESIRABLE

ATTRIBUTES OF AN ARTIFICIAL LABOR MARKET

Scalable Agent Compression Ratio = (# Agents / # Individuals) 1.

DecomposableMarkets can be segmented by any criteria, e.g., by region,by life style, by race, by gender, etc.

EvolutionaryAgents adapt to environment and to markets

Interaction with Real CounterpartAgents learn from behavior in the real environment

PersistentAlways available

Laboratory for new OR/MS model development

USAREC AGENT PROCESS

Adjust factor strengths

Adjust factor strengths

Budget amount Recruiter number…

Season = SpringGDP = 1.5%…

Port

Port

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.

USAREC AGENT UNIVERSE

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

SUMMARY

ABS <-> ABS Integration Reusability of simulations tends to be lowIntegration most likely to occur at “black box” levelIntegration of ABS requires consistent agent representation and communication protocols

ABS <-> OR/MS IntegrationOR/MS models link to ABS rather than to one anotherMay promote more consistency amongst modelsIntegrated dataABS can serve as integrative environment for using OR/MS models for data mining, calibration, and new analysis

BACKUP SLIDES

AGENT-BASED SIMULATION

Characteristics of ABSABS and DES (discrete event simulation)ABS and System DynamicsABS and Virtual or Synthetic Environments

COMPARISON OF AGENT-BASED and DISCRETE EVENT

SIMULATION

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

COMPARISON OF ABS and SYSTEM DYNAMICS

ABS System Dynamics

Process: Inductive Process: Deductive

Unit of analysis: agent / individual

Unit of analysis: feedback loop / structures

Focus: Exploratory research

Focus: Confirmatory research

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)

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