MIS 643 Agent-Based Modeling and Simulation 2017/2018 Fall Chapter 1 Intorduction

1. MIS 643 Agent-Based Modeling and Simulation 2017/2018 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, socio-technical phenomena • agents • autonomous • having properties and actions (behavior) • individual heterogeneity • interactive with other agent and their environments • boundadly rational - adaptation and learning behavior • emergence of structure – macro or social levels • ABM - Computational modeling • Constucting models – a phenomena is modeled in terms of its agents, environment 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 – closed form solutions • Emprical: regression equations, neural networks • Single or SEM – 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 influences 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 • Labaratory 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 observes 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, SVM, • 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 • Simulati.on: 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 pseudocode or algorithm • Outputs: graphical ,plots, tables • Fit equations to the data generated by simulation

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 • Try to atchive goals

17. Chacteristics of an Agent in MAS • Multi-agent Systmms – branch of AI • 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 natural 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 progress • 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.: population dynamics • Simple population dysnamics - all members are the same homogenous • complex food web - how each member interact with others

20. Complex Adaptive Systems • 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. 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

22. 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 gooses

23. ABM - CAS • new computer technologies • simulate behaviors of interactiing agents • better uderstand arising complex patterns of natural and social systems • Alternative approcah - use simplified representations of complexity • sophisticated mathematical models • ABM computational methodology enableing modeling complex systems

24. Building Agent based Models • Problem • Agents • Cognitive and sensory charcteristics of agents • The actions they can carry out • Environment • Modeling • Conceptual model • Implementation - programming • Initial configration of the system • Run the model • Experimental setup • Observe the outcome • Often an emergent phenomena is looked for • Metamodel responce surface

25. A Generic ABM Simulation replication • Initialization • clear all memory • set time 0 • creatre amd initilize agents • set environmet parmeters • Repeat • increment time by one • at each time step • pass over all or some agents • perform some action • collect data • present data • until a stoping criteria • calcuate more statistics or outputs • present outputs

26. 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

27. 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

28. 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

29. 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

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

31. The Agent’s Environment • Agents are in social environment • Network of interractions with other agents • Similar in characteristics • Physical – locations • Neighbour • Cellular autometa • Interract only with their claose neighbours

32. 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

33. 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

34. 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

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

36. 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

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

38. 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)

39. 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

40. 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,...

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

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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: ABM, SD, DES

49. Example: Simple Population Dynamics • How population of a country/region evolves over time • Assumption: Population 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

50. 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,