1. MIS 585 Special Topics in MIS: Agent-Based Modeling 2015/2016 Fall Chapter 2 Models, Modeling Cycle and the ODD Protocol

2. Outline 1. What is Model 2. Modeling Cycle ODD Protocol

3. 1. What is a Model? A model is a purposeful simpoified representation of a real system In science: –How thinks work –Explain patterns that are observed –Predict systems bevaior in response to some change Social systems –Too complex or slowly changing to be experimentally studied

4. Models Formulate a model –design its assumptions and algorithms Different ways of simplfing real systems –Which aspect to include, which to ignore Purpase –The questions to be answered is the filter all aspects of the real system –irrelevant or insufficiently important –to answer the question are filtered out

5. Searching Mushrooms in a Forest Is there a best strategy for searching mushrooms? observation: –mushrooms in clusters An intuitive strategy: –scanning an area in wide sweeps –upon finding a mushroom turning to smaller scale sweeps –as mushrroms in clusters

6. Searching Mushrooms in a Forest What is large, small sweeps? and How long to search in smaller sweeps? Humans searching –pizzas, jobs, low price goods, peace with neighbors mushroom hunter –sensing radius is limited –must move to detect new mushrooms

7. Why develop a model for the problem try different search strategies –not obvious with textual models Purpose: –what search strategy maximizes musrooms found in a given time Ignore trees and vegitables, soil type Include: musrooms are distributed as clusters

8. Simplified hunter mushroom hunter –moving point –having a sensing radius –track of how many mushrooms found how much time passed since last mushroom fouınd

9. Formulate a model clusters of items (mushrooms) If the agent (hunter) finds an item smaller-scale movement If a critical time passes since last item found swithes back to more streight movement so as to find new clusters of items

10. Why model Here processes and behavior is simple in general what factors are important –regarding the question addresed by the model –not possible So –formulate –implement in computers –analize rigorously explore consequences of assuptions

11. First Formulation First formulation of the model –Preliminary understanding about how the system works –Proceses structure Based on –Empirical knowledge system’s behavior –Theory –Earlier models with the same purpose –Intiution or imagination

12. Good model 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

13. Stock Market Example Example: Stock market model –Volatility and trends of stock prices volumes,… First version –Too simple - lack of prcecesses structure –Inconsistant -

14. 2. The Modeling Cycle When developing a model –Series of tasks – systematically –consequences of simplfiing assumptions Iterating through the taasks –First models are –Too simple, too complex or wrong questions

15. The Modeling Cycle Modeling cycle:Grimm and Reilsbeck (2005) –Formulate the question –Assamble hypothesis –Choose model structure –Implement the model –Analyze the model –Communicate the model

16. Formulate the Question Clear research question Primary compass or filter for designing the model clear focus Experimentat may be reformulated E.g.: for MH Model –what strategies maximizes the rate of fining items if they are distributed in clusters

17. Assamble Hypothesis Whether an element or prosses is an esential for addresing the modeling questions - an hypothesis –True or false Modeling: –Build a model with working hypothsis –Test – useful and sufficient –Explanation, prediction - observed phenomena

18. Assamble Hypothesis (cont.) 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

19. Assamble Hypothesis (cont.) 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

20. Assamble Hypothesis (cont.) Influence diagrams, flow charts Based on –Existing knowledge, simplifications –

21. Basic Strategy Start with simple as simple as possible even you are sure that some factors are important Gilbert: analogy null hypothesis in satatistics – agaainst my claim Implement as soon as possible

22. Guidelines 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

23. 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

24. E.g.: MH Model Esential process swithcing between large scale movementgs and small scale searching Depending on how long it has been since the hunter has found an item.

25. Choose scale, state variable, processes, parameters Variables derscribing environment Not all charcteristics –Relevant wtih the question Examples –Position (location)Age, gender, education, income, state of – mind,…

26. Choose scal, state variable, processes, parameters Example Parameter being constant Exchange rate between dolar and euro –Constrant for travelers, not for traders

27. Choose scale, state variable, processes, parameters 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 ignore variation in variables

28. Choose scale, state variable, processes, parameters Choose scales, entities, state variables processes and parameters Transfering hypothesis into equations rules Describing dynamics of entities

29. Choose scale, state variable, processes, parameters Variables – derscribing state of thr system The essential process – cause change of these variables In ABM –interacting individuals agent-agent, agent-environment –Variables – individual –parameters

30. E.g.: HM Model Space items are in and hunter moves Objects - agents –one hunter and items to be searched hunter –state variables time how many items found time last found –bevaior: search strategy

31. Implementation Mathematics or cpmputer programs To translate verbal conceptual model into annimated objects Implemented model has its own dynamics and life

32. Implementation Assumption may be wong or incomplete but impolementation is right –Allows to explore the consequences of assumption Start with the simplest - null model Set parameters, initial values of variables

33. 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 explain important characteristics of real systems When to stop iterations of the model cycle?

34. E.g.: HM Model Try different search algorithms –with different parameters to see which search algorithm – strategy is the best

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

36. 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

37. Example of a Model 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

38. Theoretical Models Analytical models Restrictive assumptions –Rationality of agent –Representative agents –Equilibrium Contradicts with observations –Labaratory experiments about humman subjects

39. Theoretical Models as precision get higher explanatory power lower –closed form solutions Relaxation of assumptions –geting a closed form solution is impossible

40. Emprical Models Historically mathematical differential equations Or emprical models represente by algberic or difference equations whose parameters are to be estimated

41. Simulation Models Simulation ABMS: –Represent indiduals as autonomous units, their interractions with each other and environment –Chracteristics – variables –and behavior Variables – state of the whole system

42. How ABM differs Units agents differ in terms of resourses, size history Adaptive behavior: adjust themselfs looking current state which may hold information about past as well. other agent environment or by forming expectations about future states Emergence: ABM across-level models

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

44. 3. Summery and Conclustions ABM relatively new –way of looking old as well as new problems –complex (adaptive) systems –improve understanding What is modeling What ABM brings Model development cycle

45. Ant An ant forgang food Model: –an abstracted describtion of a process, object event

46. Ants manipulability –textual – hard to manipulatfe –E.g.: what if all ants have the same behavior A computational model –takes inputs, manipulates by algorithms and produces outputs Model implementation –from textual to computer code

47. Ants an ant – agent –properties –behavior

48. Creating the Ant Foraging Model

49. 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

50. 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

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

52. 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

53. Entities, state variable scales 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

54. 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

55. Space : grids networks Global envionment: variables change over time usually not in space –Temperature tx rate

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

57. 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

58. 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

59. 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

60. 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

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

62. Initialization Number of agents Provide values for state variables of entities or environment

63. Initialization 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

64. 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

65. 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