MIS 585

Special Topics in MIS

Agent-Based Modeling

2015/2016 Fall

Chapter 1

Intorduction

Outline

1 Introduction

2 Models

3 From Simulation to Social Simulation

4 Agemts

5 Agent-based Modeling and Simulation

6 Applications

7 Resources

8 Conclusions

1 Introduction

• Agent-based Modeling and Simulation (ABMS)– Paradigm, methodology– Modeling approach– aim – better undertand natural, social phenomena

• agents– autonomous – having properties and actions (behavior)– individual heterogeneity – interactive with other agent and their environments– emergence of structure – macro or social levels – boundadly rational - adaptation and learning behavior

• ABM - Computational modeling– Constucting models – a phenomena is modeled in terms of its

agents and their interactions• create, analyze, experiment with

Aim of the Course

• ABM – transformative representational technology

• better uderstand familiar topics

• make sense of and analyze – hiterto unexplained topics

• Developing ABM literacy – powerful, professional and life skill

• Restructuration– from one structuration of a domain to another

– change in representational infrastructure

• E.g.: from Roman to Hindu Arabic numerals in Europe – dificult to reprent large numbers and performe aritmetic operations

• E.g.: transformation of kinematics from vorbel to algebra

2 Models

• Models– Building simplified representations of the phenomena

• social, natural,business or socio-technical

• Types of models:– Verbal - Natural languages– Analog - – Mathematical – equation-based

• Analytical • Emprical: regression equations, neural networks

– Single or structural – interraction among variables– A relation between dependent and independent variables is estimated

from data • Differential / difference equations (System dynamics)

• Computational method– Computer programs– Inputs (like independent variables)– Outputs (like dependent variables)

Example of a Model

• Consumer behavior model:– How friends influence consumer choices of indivduals

• Buy according to their preferences• what one buys influeces her friends decisions

– interraction

• verbal• mathematical

– theoretical model– Emprical : statistical equations

• estimated from real data based on questioners

• simulation models of customer behavior– ABMS – interractions, learning, influence from networks

Mathematical Models

• Analytical models– closed form solutions

• Restrictive assumptions– Rationality of agent – rational choice theory– Representative agents– Equilibrium

• Contradicts with observations– abaratory experiments about humman subjects– Observations at macro level – stylized facts

• as precision get higher explanatory power lower• Relaxation of assumptions

– geting a closed form solution is impossible

Example: Consumer behavcior

• Consumer behavior models in economics• treat a typical consumer as a untility maximizing

agent• the consumer observe prices of goods/services• derives utiity from them• perfectly rational • Mathematical tools – at minimum calculus• Interraction of consumers in a market• two or three types of consumers• equilibrium is assumed

Emprical Models

• Estimation of parameters of a single or set of equations from real world data

• Methods – statistics, machine learning or data mining– Regression – single equation or SEM

– Nueural networks

– Decisio trees

• E.g.: estimate behavior of cunsumer from opinion survays

• E.g.: behavior of an economy – Simultaneous equations

3 From Simulation to Social Simulation

• Model of a system with suitable inputs and observing the corresponding outputs

• Uses of simulation Axelrod(1997)– 1-Prediction:

– 2-Performance:

– 3-Training:

– 4-Entertainment:

– 5-Education:

– 6-Proof

– 7-Understanding - Discovery:

Third Disipline

• Inductive– Discovery of patterns in emprical data

– E.g.: analysis of opinion data, econometirc models

• Deductive– Axioms – assumptions

– Proving consequences – theorems

– E.g.: proving Nash equilibrua in games

• Simulation– set of assumptions but not prove theorems

– generates data – analyzed inductively• anaysis of simulation outputs

• comparing with real data

Computational

• use computers or ICT as an instrument• other examples instuments restructuring science

– optical telecope - astronomy

– microsope – bioloy

– find other insruments restructuring sciences

• Compare– Output of the model and data from real world

– if output model is similar to real world

• Validity of the model

Experiments

• Experiment:– Applying some treatment to an isolated system and

observing what happens

• Common in natural sciences– Physics, chemistry

• Not common in social sciences– isolation – Mostly in psychology, new in experimental economics

• Computer simulations– chaning parameters - range– other factors randomly

• if the model is a good representation of the reality – Senario or what if analysis

Simulation in Social Science

• In engineering or natural science– Prediction

– E.g.: predict

– position of planets in the sollar system

– motion of molecules

– weather temperature (next day, hour)

• In social science– Uderstanding social phenomena, processes or mechanizms

– Proof of my claim or hypotheis

– Discover some new previously unknown patterns

– Policy/senario analysis

How to communicate

• Induction– Publich model (equeation , coefficients, significance)

• Deduction– Theorems, equeations

• Simulation– Publish the sude code or algorithm

– Outputs: graphical ,plots

4 Agents

• Distributed Artifical Inteligence (DAI) or multi-agent systems (MAS)

• Agents - software– Searching internet:softbots, visards for assistance

• Agents represents in ABMS– Individuals – consumers,producers, families– Organizations – governemts, merket makers– biological entities – animals, forest, crops

• What they do– Get information from their environment or from other agents– Process information, may have limited memory - forget – Communicate with one onother via messaging– Learn from others, their own experiences

What is An Agent

• Multi-agent Systmms • Four characterisitcs Woodridge & jannings, 1995)• Autonomy• Social ability

– interract with other agents or humans (users)

• Reactivity– React to stimula comming from its environment

• Proactivity– Goal or goals

5 Agent based Modeling and Simulation

• After– Modeling

– Simulation

– Agents

• ABMS:– A simulation paradigm used in social and natukral sciencees

to analyze or better understand these sysems consisting of autonomous, interaction, goal-oriented and boundadly rational actors so called agents situated in an environment.

Complex ;Adaptive Systems

• Complex systems - informally– difficult to understand– world we live getting more and more complex

• many complex interractions compared to past• as science and technology progres

• Simple to complex systems• Defined:• Systems with interracting many elements yet aggregate

behavior can not be predictable from individual elements– from interractions of individual elements– an emergent phenomena arises

• E.g.: simple population dynamics– all members are the same homogenous– complex food web – how each member interact with others

Emergence

• large scale effects of laocal interractions• lower level to higher• assumptions may be simple • consequences may not be obvious –suprising• Micro level macro level phenomena micro

– Second order emergence

• Properties Holland 2014– self-organized – order at the macro level

– Chaotic behavior: small change in initial condition hase huge effects on system out

– fat-tailed: extream values more then normal distibution

– Adaptive interactions.

Understaning Complex Systmes and Emergence

• Two funamental and distict challenges• Integrative understanding

– Try to figure out the aggregate pattern when knowing the indivdual behavior

• Differential understanding– The aggregate pattern is known

– Find indivdual behavior for that pattern

– Flocking behavior of birds

– V flocking of goose birds

• new coputer technologies• simulate behaviors of interactiing agents • better uderstand arising complex patterns of

natural and social systems• Or use simplified representations of complexity

– sophisticated mathematical models

• ABM computational methodology enableing modeling complex systems

Building Agent based Models

• Problem

• Agents– Cognitive and sensory charcteristics of agents

– The actions they can carry out

• Environment

• Modeling– programming

– Initial configration of the system

– Run the model

– Experimental setup

• Observe the outcome– Often an emergent phenomena is looked for

– Metamodel responce surface

A Generic ABM Simulation replication

• Initialization – clear all memory– set time 0– creatre amd initilize agent– set environmet parmeters

• Repeat– increment time by one– for each process

• pass over all or some agents• perform some action • collect data • present data

• until a stoping criteria• calcuate more statistics or outputs• present outputs

Model Development

• Implementation of the model– simulate the model

• Varification• Validation• Analysis of the model• Model development is an iterative process• starting with problem formulation• firet simple models• get complicated

Validity

• external – opperational validity• accuricy or adequecy of the model in matching the

real world data– experimental, archivial, survay

• Point prediction – natural systems• pattern predictions rubost processes -

– sequence of events similar not identical

• Artificial societies– Artificial merkets

– Abstract not real systems

Modeling Agents in ABM

• Agents– Reciving input from the environment

– Storing historical inputs and actions

– Actions and

– Distributing output

• Symbolic AI– Production systems

• Non symbolic – learning: adapting to changes– neural networks

– evolutionary algorithms such as genetic algorithms

• Object-oriented Programming

Object Oriented Programming

• Classes – prototypes for each agent type• Objects – agents - instances from each class• Characteristics of agnet - Instance variables• Behavior - Methods• Interraction between - Mesage sending• Inheritance/Polymorphism

– from general agents to specific onces

• Heterogenous in– characteristics

– behaive differently

Software

• High level languages – object oriented– Java, C++, C#

• Special packages– Swarm

– Repast

– NetLogo

– MASON

The Agent’s Environment

• Agents are in social environment– Network of interractions with other agents

• Similar in characteristics

– Physical – locations • Neighbour

• Cellular autometa– İnterract only with their claose neighbours

Features of ABM

• Ontological correspondence– Computational agents in the model – real world actor– Desing the model, interpret results

• Heterogenous agents– Theories in economics – actors are identical– Preferences, rules of behavior are different

• Representation of the environment• Agent ınteractions• Bounded rationality

– Optimizing utility v.s. limited cognitive abilitiesi

• Learning – İndividual, population social levels

Adventages

• Micro level macro level phenomena micro – Second order emergence

• Programming languages– more expresive then mathematical models

– modular: object oriented approach

• No sofisticated mathematiical skills• Thought experiments

– policy evaluation, senatio analysis

• Enables to test different theories or hypothesis about a phenomena– E.g.: different consumer behavior theories

Limitations

• Expresing the results– particular example

• Rsults depends on– parameters– initaal conditions

• Model communication– reproducibility of results– use standard packages – limitaitons

• Interdiciplinary nature• Education in social science

– no programming courses

• May need computing power

Simulation Methods in Social Science

• Gilbert(2005) classification– System dynamics

– Discrete event simulation – quing models

– Multilevel

– Microsimulation

– Cellular autometa

– Agent-based Simulation

Other Related Modeling Approaches

• System dynamics (SD)

• SD ABM

:aggregate individual

top- down buttom-up

differential equations interacting agents • E.g.: Population dynamics• SD: a single variable for population

– an equation describing its rate of channge

– hard to include heterogenouty

• ABM: modeling population with heterogenous agents– fertatlty, migration or death rate depends on

– age, gender, income, etnicity, location

SD v.s. ABM (cont.)

• E.g.: population dynamics• E.g.: predator-pray• E.g.: technology diffusion

Microsimulation v.s. ABM

• Microsimulation– Large database – individuals

– Variables: income,education,gender….

– What the sample would be in the future

– Rules applied to every member in the sample

– Adventages:• Realistic data

– Disadventages:• State transformations difficut to estimate

• No agent-agent interaction – agent are isolated only interact with their environments

• Early simulations in social science (1957)

CA v.s. ABM

• CA:• interraction with their neighbor • with simple rules• CA agents have simple states usually a binary

variable – alife – death,

– not buy - buy, has the opinion – does not have

• Dynamics of physical, chemical systems• E.g.: Game of life

6 ABM Applications

• Eaarly adapting disiplines– chemistry, biology, material science

• Second wave– natural - physics,

– social – demography, political science, sociology

– geography - GIS

– crowd simulations

• Latter– business, economics,...

Social Science Applications

• Economics• Demogrphy• Political science

– party competitions

– voting behavior

• Socialogy / Antropology • History • Law• Interdisiplinary

– Science dynamics

– soio-technical systems

Business/MIS

• Business– Finance

– Marketing / e-merketing

– Organizational behavior

– Operations management• Supply chain management / logistics

– MIS• User modeling, value of information, e-business, e-auctions

Modeling Examples

• Urban models -Schelling(1971,1978)– Racial segregation– Grid cells,– Two types – rad,green

• Opinion dynamics– Agents have opinions -1 to +1 and degree of doubt – Interact randomly

• Consumer behavior• Marketing

– viral marketing WOM effects– efficiency of marketing strategies– Dynamics of markets:– U-Mart project

Modeling Examples (cont.)

• Industrial networks– Links between firms

– Inovation networks- biotechnology, ICT

– Clustering of industries

• Business ecosystems• Supply chain management

– Effectiveness of management policy

– Order fulfilment

– Procter & Gamble

Business/MIS Examples

• Diffusion– New product, technology, innovations

• Markets– modeling software markets – versioning decisions timing of

upgrading and how much and when

• Financial merkets– Santa Fe Stock market

– speculative behavior

• Auctions – efficiency, profitability of e-auction mechanisms

Business/MIS Examples (cont.)

• Strategic management– Profitability, efficiencey of business strategies– Competitive or cooperative strategies– outsourcing

• Organizational impact of information systems• Modeling simulation of business processes

– Common with discrete event simulation but – ABMS enables including behavior of humans

• Social Networks– Behaviour in social media– Dynamics off/on social networks

• How social networks evolve over time• network of networks

Business/MIS Examples (cont.)

• Industrial clusters– Similar firms in terms of what they produce (good services)

– Tend to be locatyed in the same geographical regions

• Software Engineering– Software upgrade quality improvement decisions in prsense

of network effects

• Modeling competition considering product life cycle diffusion of influences

Decision Support Systems (DSS)

• ABMs can be embedded into DSS to perform – What if analysis

– Sensitivity analysis

– Senario analysis

• User interface• Model base

– OR - optimzation – linear programming

– Statistical

– Analytical

– simulation

Example: Simple Populatgion Dynamics

• How population of a country/region evolves over time

• Assumption: Populatgion of a country increrases proportional with the current value of its population

• SD– one variable representing population N(t) as a function of

time – homogenous

• dN/dt = g*N – rate of change of population is proportional to curent value of N

• g: yearly growth rate of population• first order homogenous differential equation

Analytical Solution

• Analytical solution even with frashman calculus

dN/N = gdt

integrating both sides

InN + C = gt

initial condition at time t=0 N= N0,

InN + C = g*0 so C = - InN0,

InN – InN0 = gt

InN/N0 = gt taking exponent of both sides

N/N0 = egt,

N = N0egt,

As an emprical model

• N0 : the popution at an arbitary time calssed zero

• g: yearly growth rete to be estimated from real population data

• time(years) population(millions)

1970 35

1975 39

1980 42

Simulation in SD

• The differential equation can be simulated as well

• Excel simulation

• given an initial population and a estimated g value

• project population over time

growth rate 0,02

Time Population0 50,00001 51,00002 52,02003 53,06044 54,12165 55,20406 56,30817 57,43438 58,58309 59,7546

10 60,9497

0,000010,000020,000030,000040,000050,000060,000070,0000

1 2 3 4 5 6 7 8 9 10 11

Series1

ABM model

• At time 0• create set of egents representing age, gender,

education, income, etnicity, geography of population

• Each agent has a type has different fertality rate• As time progress

– with a probability have a chiild

– may die or migrate to another country

– new agents may migrate to the country

– but deterministically age increses by say 1 year

Example: Predator-Prey Interractions

• Lotka-Volterra disfferential equations

dPred/dt = K1*Prad*Prey – M*Pred

dPrey/dt = B* Prey - K2*Prad*Prey

Two coupled nonlinear diferential equations

ABM

State mehanisms

They have enery

İncreass when eat decreases when move

Prey may eat grass

Predators eat prey

7 Resources

• Associations:– North Americal Assoc. for Computational and

Organizational Sciences

– Posific Asean Assoc. for Agent-Based Approaches in Social Systems Science

– Eurapean Socaal Simulation Assoc.

• Journal:– Journal of Artifical Societies and Social Simulation

• web sides:– Acent Based Computational Economics by Tesfatsion

• Handbook of Computational Economics Vol 2– by Judd and Tesfation

Books

• Gilbert, N., Agent-Baded Models, Saga Pubnlications, 2008.

• North N.,J., Macal, C. M., Managing Business Compoexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, Oxford University Press, 2008.

• Railsback, S., F., Grimm, V., Agent-Based and Individual-Baded Modeling:A Practical Introduction, Princeton University Press, 2011.

• Robertson, D.,A., Caldart, A.,A., .The Dynamics of Strategy: Mastering Strategic Landscapes of the Firm, Oxford University Press, 2009.–

8 Conclusion

• Simulation in social science– third way of doing research

• ABMS– buttom up

– agnets• heterogenous

• adaptive, learning behavior

– interractions

– emergence