mis 585 special topics in mis agent-based modeling 2015/2016 fall chapter 1 intorduction
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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
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