AGENT-BASED SIMULATION AND MODEL INTEGRATION
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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
Mfg_Expense
Dist_Expense
Price
Dist_Expense
Mfg_ExpenseVolume
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
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0
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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
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