1. MIS 643 Agent-Based Modeling and Simulation 2016/2017 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 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

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 inigtial 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 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)

32. 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

33. 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