MIS 585 Special Topics in MIS: Agent-Based Modeling 2015/2016 Fall Chapter 3 The ODD Protocol

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

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

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

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,

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

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

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

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

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

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

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

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?

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

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

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

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

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

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,

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

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

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

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?

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

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

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

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

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

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

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 inigtial conditions Comming from distributions Run the model until memory of the initial state is forgoten the effect of initial valus disapear

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

Submodels deiteld description o prosseses submodels – model of one process in ABM often indepenent of each other designed and tested seperately listed in processes – now full detail

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