MIS 643 Agent-Based Modeling and Simulation 201 7 /201 8 Fall

1. MIS 643Agent-Based Modelingand Simulation2017/2018 Fall Chapter 3 The ODD Protocol

2. Outline 3.1 Introduction 3.2 What is ODD and Why Use It? 3.3 ODD Protocol

3. 3.1 Introduction • Formulating an ABM • from heuristic part • problem,ideas, data, hypothesis • to first formal regorous representation • In terms of: words, diagrams, equations • model structure

4. Purposes of Model Formulation • think explicitly all parts of the model • decisions designing it • Communicate the model • Basis of implementation • complete and explicit • Publishing results • complete accurate description

5. Describing ABMs • What characteristics • How to describe – concisely & clearly • In equation-based modeling • equations & parameter values • in statistical models: t, F statistics, p-values, accuricy measures,

6. Describing ABMs (cont.) • In ABMs • complex • no treditional notation • need standards – ODD • not only describe but thining abut the model

7. Learning Objectives • Overview and details elements of ODD • Introduction to design concepts element

8. 3.2 What is ODD and Why Use It? • in literature many ABMs are incomplete • impossible to • reimplement • replicate the results – key to science • Describing ABMs • easy to understand & yet to be complete • Strandardization: • what information, in what order • In ecology and social science

9. 3.3 The ODD Protocol • Originaly for decribing ABMs or IBMs • Useful for formulating ABMs as well. • What kind of thinks should be in AMB? • What bahavior agents should have? • What outputs are needed_ • A way of think and describe about ABMs

10. The ODD Protocol (cont.) • ODD: Owverview, Design concepts and Details. • Seven elements • Overview - three elements • what the model is about & how it is designed • Design concepts - one element • esential characteristics • Details – three elements • description of the model complete

11. ODD Elements • Overview:1 • 1 - Purpose • 2 - Entities, state variables and satates • 3 - Process overview and scheduling • 4 - Design Concepts • Details: • 5 - Initialization • 6 - Input data • 7 - Submodels

12. Design Concepts • Basic principles • Emergence • Adaptation • Objectives • Learning • Prediction • Sensing • Interraction • Stochasticity • Collectives • Observation

13. Purpose • Clear and concise statement of the question or problem addresed by the model • What system we are modeling? • What we are trying to learn?

14. Entities, State Variable, and Scales • What are its entities • The kind of thinks represented in the model • What variables are used to characterize them

15. Entities in ABMs • One or more types of agents • The environment in which agents live and interract • Local units or patches • Global environment that effect all agents

16. State Variables • State variables: how the model specify their state at any time • An agent’s state – properties or attributes • size, age, saving, opinion, memory • Some state variables constant • Gender, location of immobile agents • Varies among agents but stay constant through out the life of the agent

17. State Variables (cont.) • Behavioral strategy: • Searching behavior • Bidding behavior • Learning • not include deduced or calculated ones • Space : grids networks • usually discrete – patches • within patches are ignored

18. Global Variables • Global envionment: variables change over time usually not in space • Temperature, tax rate, prices • Usually not affected by agents • Exogenuous, • Provideded as data input or coming from submodels

19. Scales • Temporal & spatial scales • Temporal Scale: • How time is represented • How long a time is simulated • the temporal extend • How the passage of time is simuated • Most ABMs – discrete time • day, week, month, ... • temporal resolution or time step size,

20. Temporal Scale • processes and changes happening shoter then a time step are • summerized and • represened by how they make state variables jump from one time step to the next • E.g.: Stock market • daily time v.s. intradaily

21. Temporal Scale (cont.) • Temporal extent: typical length of a simulation • how much time # of time steps • outputs • system level phenomena v.s. • temporal resolution – agent level

22. Spacial Scale • if spacially explicit • total size or extent of space • grid size resolution • key behaviors, interactions, • spatial relations within a giid cell • are ignored only • only spatial effects among cells • E.g.: urban dynamics – single household • grid or patch • what happends within a house - ignored

23. Process Overview and Scheduleing • Structure v.s. Dynamics • Processes that change the state variables of model entities • Describes the behavior or dynamics of model entities • What are model entities are doing? • What behaviors they execure as time proceeds? • What updatres and change heppens in environment?

24. Process Overview and Scheduleing (cont.) • Write succinct description of each process • with a name • E.g.: selling, buying, biding, influensing

25. Observer Processes • not linked to 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 on a graphical display • Write statistical summaries to output files

26. Model’s Schedule • The order in which processes are executed • An ABMs schedule • concise and complete outline of the model • Action: model’s scedule is a sequence of actions

27. Model’s Schedule - Actions • Specifies • What model entities executes • What processes • in What order • E.g.: in NetLogo ask turtles [move] • Some ABMs - simple • For many ABMs schedule is complicated • Use a pseudocode

28. Design Concepts • How a model implements a set of basic concepts • standardized way of thinking important and unique characteristics of ABMs • E.g.: What outcomes emerge from what characteristics of agents and their environment • E.g.: What adaptive decision agents make • Questions like check lists

29. Design Concepts (cont.) • Basic principles • Emergence • Adaptation • Objectives • Learning • Sensing • Prediction • Interraction • Stochasticity • Collectives • Observation

30. Initialization • describe model world at the begining of simulation # of agents, their charateristics environmental variables • Somethimes: model results depends on initial conditions • Not depends on initial conditions • Comming from distributions • Run the model until memory of the initial state is forgoten the effect of initial valus disapear

31. Input Data • Environmental variables • usually change over time • policy variables • price, promotions, advertising expenditures • physical systems • temperature • not parameters • they may change over time as well • read in from data files as the model executes (not initial inputs)

32. Submodels • deiteld description o processes • submodels – model of one process • in ABM often indepenent of each other • designed and tested seperately • listed in overview-processes • In detail-submodels –full detail

33. Submodels (cont.) • describe: • agorithms rules or pseudocode or equations • but also • why we formulate the submodel • what literature it is based on • assumptions • where to get parameter values • how to test or calibrate the model