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

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

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

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

  2. Outline Introduction to Modeling Modeling Heuristics Modeling Cycle ODD Protocol

  3. 2 Models • A model is a purposeful simpoified representation of a real system • In science: • How thinks work • Explain patterns that are oblerved • Predict systems bevaior in response to some change • Social systems • Too complex or slowly changing to be experimentally studied • Different ways of simplfing social systems • Which aspect to include , which to ignore • Purpase • The questions to be answered is the filter

  4. First formulation of the model • Preliminary understanding about how the system works • Processs structure • Based on • Empirical knowkedge system’s behavior • Theory • Earlier modles with the same purpose • İntiution or imagination

  5. Assumptions at first experimental • Test whether they are appropriate and useful • Need a criteria – model is a good representation of the real system • Patterns and regularities • Example: Stock market model • Volatility and trends of stock prices volumes,… • First version • Too simple - lack of prcecesse structure • İnconsistant -

  6. Mere realizm is a poor guideline for modeling • must be guided by a problem or question about a real system • not by just the system itself • Constraints are esential to modeling • on information understanding time • Modeling is hardwired into our brains • we use powerful modeling heuristics to solve problems

  7. Heuristics for Modeling • pleusable way or reasonalble approach that has often proved to be useful • Rephrase the problem • Draw simple diagrams • Inagine that you are indide the system • Try to idendify esential variables • identify assumptions • Use salami tactics

  8. The Modeling Cycle • When developing a model • Series of tasks – systematically • Iterating through the taasks • First models are • Too simple , too complex or wrong questions • Modeling cycle:Grimm and Reilsbeck (2005) • Formulate the question • Assamble hypothesis • Choose model structure • Implement the model • Analyze the model • Communicate the model

  9. Formulate the Question • Clear research question • Primary compass or filter for designing the model • clear focus

  10. Assamble Hypothesis • Whether an element od prosses is an esential for addresing the modeling questionis an hypothesis • True or false • Modeling: • Build a model with working hypothsis • Test – useful and sufficient • Explanation, prediction - observed phenomena • Hypothesis of the conceptual model • Verbally graphically • Based on Theory and and experience • Theory provides a framework to persive a system • Experience • Knowlede who use the sysem

  11. Assamble Hypothesis • Formulate many hypothesis • What process and structures are essentiaal • Start top-down • What factors have a strong influence on the phenomena • Are these factors independent or interacting • Are they affected by ohter important factors • Influence diagrams, flow charts • Based on • Existing knowledge, simplifications

  12. choose scal, state variable, processes, parameters • Variables derscribing environment • Not all charcteristics • Relevant wtih the question • Examples • Position (location)Age, gender, education, income, state of • mind ,…

  13. choose scal, state variable, processes, parameters • Example • Parameter being constant • Exchange rate between dolar and euro • Constrant for travelers, not for traders • Scale • Time and spatial • Grain: smalest slica of time or space • Extent: total time or area covered by the model • The gain or time spen: step over which we igore variation in variables

  14. choose scal, state variable, processes, parameters • Choose scales, entities, state variables processes and parameters • Transfering hypothesis into equations rules • Describing dynamics of entities • Variables – derscribing state of thr system • The essential process – cause change of these variables • İn ABM • interacting individuals • Variables – individual • parameters

  15. Implementation • Mathematics or cpmputer programs • To translate verbal conceptual model into annimated objects • Implemented model has its own dynamics and life • Assumption may be wong or incomplete but impolementation is right • Allows to explore the consequences of assumption • Start with the simplezt - null model • Set parameters , initial values of variables

  16. Analysis • Analysing the model and learing with the aid of the model • Most time consuming and demanding part • Not just implementing agents and run the model • What agents behavior can expalin important characteristics of real systems • When to stop iterations of the model cycle?

  17. Communication of the model • Communicate model and rsults to • Scientific community • Managers • Observations, experiments, findings and insights are only when • Others repreduce the finings independently and get the same insights

  18. 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

  19. 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

  20. Emprical Models • Historically mathematical differentjial equations • Or emprical models represente b algberic or difference equations whose parameters are estimated • Simulation • ABMS: • Represent indiduals as autonomous units, their interractions with each other and environment • Chracteristics – variables • and behavior • Variables – state of the whole system

  21. How ABM differs • Units agents differ in terms of resourses, size history • Adaptive behavior: adkust themselfs looking current state which may hold information bout past as well. other agent environment or by forming expectations about future states • Emergence:

  22. Skills • A new language for thiking about or derscribing models • Software • Strategy for model development and analysis

  23. The ODD Protocol • Originaly for decribing ABMs or IBMs • Useful for formulating ABNs as well. • Wha kind of thigs should be in AMB? • What bahavior agents should have? • What outputs are needed_ • A way of think and describe about ABModeling

  24. The ODD Protocol • ODD Owverwiew Design concepts and Details • Seven elements • Three elements overwiew what the odel is about • One design element • Three elements deteild description of the model complete

  25. Purpose • Statement of the question or problem addresed by the model • What system we are modeling_ • What we are trying to learn?

  26. Entities, state variable scales • What are its entities • The kind of thinks represented in the model • What variables are used to characterize them • ABMs • One or more types of agents • The environment in which agents live and interract • Local units or patches • Global environment • State variables: how the model specify their state at any time • An agent’sd state – properties or attributes • Size, age, saving, opinion, memory

  27. Behavioral strategy: • Searching behavior • Bidding behavior • Learning • Some state variables constant • Gender location of immobile agents • Varies among agents but stay constant through out the life of the agent • Space : grids networks • Global envionment: variables change over time usually not in space • Temperature tx rate

  28. Golbal Variables: • Usually not affected by agents • Exogenuous, • Provideded as data input or coming from submodels

  29. Process overwiew and and Scheduleing • Structure v.s. Dynamics • Process that change the state variables of model entities • Describes the behavior or dynamics of odel entity • Dercribe each process with a name • Selling buying biding influensing

  30. Observer Processes • Only processes that are not liked to one of the model entities • Modeler – creator of the model • Observe and record • What the model entities do • Why and when they do it • Display model’s status ona graphical display • Write statistical summaries to output files

  31. Model’s Schedule • The order in which processes are executed • Action: model’sd scedule is a sequence of actions • What model entities • What processes • What order • Some simple • For many ABMs schedule is complex • Use a pseudo code

  32. Design Concepts • How a model implements a set of basic concepts • standardized way of thinking important and unique characteristics of ABM • What outcomes emerge from what characteristics of agents and their environment

  33. Basic principles • Emergence • Adaptation • Objectives • Learning • Prediction • Interraction • Stochasticity • Collectives • Observation

  34. Initialization • Number of agents • Provide values for state variables of entities or environment • Model results depends on initial conditions • Price txx rate • Not depends on inigtial conditions • Comming from distributions • Run the model until memory of the initial state is forgoten the effect of initial valus disapear • Replicate teh model

  35. Input Data • Environmental variables • usually change over time • policy variables • price promotions advertising expenditures • pyjrt rcsöğşrd • temperatukre • not parameters • they may change over time as well • not initial values

  36. Submodels • deiteld description o fprosseses • not only agorithms or pseudo code • but • why we formulate the submodel • what literature is is based on • assumptions • where to get parameter values • how to test or calibrate the model