1. MIS 643 Agent-Based Modeling and Simulation 2016/2017 Fall
Chapter 2
Models, Modeling Cycle and the
ODD Protocol

2. Outline
1. What is Model
2. Modeling Cycle
3. 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 Different ways of simplfing real systems
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
prizes, 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
clearly formulated purpose:
what search strategy maximizes musrooms found in a given time
Ignore trees and vegitables, soil type
Include: mushrooms 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 Based on
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. no idea about how a system works not formulate a model
e.g.: human consciousness

13. 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
Example: Stock market model
Volatility and trends of stock prices volumes,…

14. Fisrt Versions First version Too simple - lack of prcecesses structure
Inconsistant -

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

16. 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

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

18. 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

19. 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

20. 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

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

22. Basic Strategy Start with 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

23. 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

24. 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

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

26. 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 ,…

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

28. Choose scale, state variable, processes, parameters
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

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

30. 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

31. 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

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

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

34. 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?

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

36. 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

37. 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

38. 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

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

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

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

42. 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

43. 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

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

45. 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

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

47. Ants manipulability A computational model Model implementation
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

48. Ants
an ant – agent
properties
behavior

49. Creating the Ant Foraging Model

50. The ODD Protocol Originaly for decribing ABMs or IBMs
Useful for formulating ABMs as well.
What kind of things should be in ABM?
What bahavior agents should have?
What outputs are needed_
A way of think and describe about Agent Based Modeling
communicate models to others
ease implementation

51. The ODD Protocol ODD Owverwiew Design concepts and Details
Seven elements
Three elements overwiew what the model is about
One design element
Three elements deteild description of the model complete

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

53. 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

54. 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’s state – properties or attributes
Size, age, saving, opinion, memory

55. Some state variables constant
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

56. Global envionment: variables change over time usually not in space
Space : grids networks
Global envionment: variables change over time usually not in space
Temperature, tax rate , interest rate

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

58. time scale time scale space spatial resolution time rosolution
temporal extend
space
spatial resolution

59. Process overwiew and and Scheduleing
Structure v.s. Dynamics
Process that change the state variables of model entities
Describes the behavior or dynamics of model entity
Dercribe each process with a name
Selling buying biding influensing

60. 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

61. Model’s Schedule The order in which processes are executed
Action: model’s 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

62. 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

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

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

65. Initialization Model results depends on initial conditions
Price txx rate
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
Replicate the model

66. Input Data Environmental variables not parameters not initial values
usually change over time
policy variables
price promotions advertising expenditures
temperature
not parameters
they may change over time as well
not initial values

67. 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