MIS 585 Special Topics in MIS AgentBased Modeling

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 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 – 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 Net. Logo 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 – 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