1. MIS 585 Special Topics in MIS Agent-Based Modeling 2015/2016 Fall Chapter 1 Intorduction

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, 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

4. 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

5. 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)

6. 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

7. 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

8. 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

9. 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

10. 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:

11. 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

12. 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

13. 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

14. 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

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

16. 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

17. 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

18. 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.

19. 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

20. 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.

21. 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

22. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. 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

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

34. 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

35. 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

36. 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

37. 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

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

39. Other Related Modeling Approaches System dynamics (SD) SDABM :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

40. SD v.s. ABM (cont.) E.g.: population dynamics E.g.: predator-pray E.g.: technology diffusion

41. 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)

42. 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

43. 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,...

44. Social Science Applications Economics Demogrphy Political science –party competitions –voting behavior Socialogy / Antropology History Law Interdisiplinary –Science dynamics –soio-technical systems

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

51. 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

52. 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

53. Analytical Solution Analytical solution even with frashman calculus dN/N = gdt integrating both sides InN + C = gt initial condition at time t=0 N= N 0, InN + C = g*0 so C = - InN 0, InN – InN 0 = gt InN/N 0 = gt taking exponent of both sides N/N 0 = e gt, N = N 0 e gt,

54. As an emprical model N 0 : the popution at an arbitary time calssed zero g: yearly growth rete to be estimated from real population data time(years) population(millions) 197035 197539 198042

55. 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

56. 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

57. 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

58. 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

59. 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. –

60. 8 Conclusion Simulation in social science –third way of doing research ABMS –buttom up –agnets heterogenous adaptive, learning behavior –interractions –emergence