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