1. Agent-Based Modeling and Simulation (ABMS) Bertan Badur badur@boun.edu.tr Department of Management Information Systems Boğaziçi University

2. 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

3. 1 Introduction Agent-based Modeling and Simulation (ABMS) –Paradigm –Modeling approach– in social sciensese –agents individual heterogeneity interractive emergence of structure – macro or social levels boundadly rational - adaptation and learning behavior Computational social science –Constucting models create, analyze, experiment

4. 2 Models Models –Building simplified representations of the phenomena social, natural Types of models: –Verbal - Natural languages –Analog - –Mathematical - equation based Theoretical 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)

5. 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, formation of networks

6. Theoretical Models Analytical models Restrictive assumptions –Rationality of agent –Representative agents –Equilibrium Contradicts with observations –Labaratory experiments about humman subjects as precision get higher explanatory power lower –closed form solutions Relaxation of assumptions –geting a closed form solution is impossible

7. Emprical Models Estimation of parameters of a single or set of equations from real world data Methods – statistics or machine learning – data mining –Rgeression –Nueural networks –Decisio trees E.g.: estimate behavior of cunsumer from opinion survays E.g.: behavior of an economy –Simultaneous equations

8. 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:

9. 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

10. Computational Compare –Output of the model and data from real world –if output model is similar to real world Validity of the model

11. 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

12. Simulation in Social Science In engineering or natural science –Prediction –E.g.: predict position of planets in the sollar system – motion of molecules – temperature (next day, hour) In social science –Uderstanding :

13. How to communicate Induction –Publich model (equeation, coefficients, significance) Deduction –Theorems, equeations Simulation –Publish the sude code or algorithm –Outputs: graphical,plots

14. 4 Agents Distributed Artifical Inteligence or multagent systems Agents –Searching internet:softbots, visards for Office Agents represents in ABMS –Individuals – consumers,producers, families –Organizations – governemts, merkets –biological entities – forest, crops What they do –Get information from their environment –Process information –Communicate with one onother via messaging –Learn from others, their own experiences

15. What is An Agent Four characterisitcs Woodridge & jannings, 1995) Autonomy Social ability –interract with other agents Reactivity –React to stimula comming from its environment Proactivity –Goal or goals

16. 5 Agent based Modeling and Simulation After –Modeling –Simulation –Agents ABMS: –A simulation paradigm used in social sciencees

17. 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

18. Emergence large scale effects of laocal interractions lower level to higher assumptions may be simple consequences may not be obvious –suprising

19. 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

20. Modeling Agents 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

21. Object Oriented Programming Classes – prototypes for each agent type Objects – agents - instances from each type Characteristics of agnet –İnstance variables Behavior –Methods Interraction between –Mesage sending Inheritance Polymorphism –Facilitates program development

22. Software High level languages – object oriented –Java, C++, C# Special packages –Swarm –Repast –NetLogo

23. 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

24. 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

25. Adventages Micro level  macro level phenomena  micro –Second order emergence Programming languages –more expresive then mathematical models –modular: object oriented approach Thought experiments

26. 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

27. Simulation Methods in Social Science Gilbert(2005) classification –System dynamics –Discrete event simulation – quing models –Multilevel –Microsimulation –Cellular autometa –Agent-based

28. Other Related Modeling Approaches 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 interaction – agent are isolated System dynamics –SD:aggregate – AMB: individual –top- down v.s. buttom up –Sets of differential equations – next time from current

29. 6 Applications Economics Demogrphy Business –Finance –Marketing / e-merketing –Organizational behavior –Opperations management Supply chain management / logistics –MIS User modeling, value of information, e-business, e-auctions Political science Socialogy / Antropology

30. 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

31. Modeling Examples (cont.) Industrial networks –Links between firms –Inovation networks- biotechnology, IT –Clustering of industries Supply chain management –Effectiveness of management policy –Order fulfilment –Procter & Gamble Diffusion –New product, technology, innovations Financial merkets –Santa Fe Stock market –speculative behavior Auctions –efficiency, profitability of e-auction mechanisms

32. Modeling 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

33. Modeling 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

34. Decision Support Systems (DSS) ABMs can be embedded into DSS to perform –what if analysis –Sensitivity analysis –Senario analysis User interface Model base –OR –Statistical –Analytical –simulation

35. 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

36. 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. –

37. 8 Conclusion Simulation in social science –third way of doing research ABMS –buttom up –agnets heterogenous adaptive, learning behavior –interractions –emergence