Agent-Based Modeling and Simulation (ABMS) Bertan Badur badur@boun.tr Department of

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