MIS 585Special Topics in MIS:Agent-Based Modeling

2015/2016 Fall

Chapter 2Models, Modeling Cycle and

theODD Protocol

Outline

1. What is Model2. Modeling CycleODD Protocol

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

Models

• Formulate a model– 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

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

Searching Mushrooms in a Forest

• What is large, small sweeps? and • How long to search in smaller sweeps?• Humans searching

– pizzas, jobs, low price goods, peace with neighbors

• mushroom hunter– sensing radius is limited– must move to detect new mushrooms

Why develop a model for the problem

• try different search strategies– not obvious with textual models

• Purpose:– what search strategy maximizes

musrooms found in a given time• Ignore trees and vegitables, soil type • Include: musrooms are distributed as

clusters

Simplified hunter

• mushroom hunter– moving point– having a sensing radius– track of

• how many mushrooms found• how much time passed since last mushroom

fouınd

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

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

First Formulation

• First formulation of the model– 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

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

Stock Market Example

• Example: Stock market model– Volatility and trends of stock prices

volumes,…

• First version – Too simple - lack of prcecesses

structure– Inconsistant -

2. The Modeling Cycle

• When developing a model– Series of tasks – systematically– consequences of simplfiing assumptions

• Iterating through the taasks– First models are – Too simple , too complex or wrong

questions

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

Formulate the Question

• Clear research question• Primary compass or filter for designing

the model• clear focus• Experimentat may be reformulated• E.g.: for MH Model

– what strategies maximizes the rate of fining items if they are distributed in clusters

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

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

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

Assamble Hypothesis (cont.)

• Influence diagrams, flow charts• Based on

– Existing knowledge, simplifications

–

Basic Strategy

• Start with simple 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

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

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

E.g.: MH Model

• Esential process• swithcing between large scale

movementgs and small scale searching

• Depending on how long it has been since the hunter has found an item.

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 ,…

Choose scal, state variable, processes, parameters

• Example• Parameter being constant• Exchange rate between dolar and

euro– Constrant for travelers, not for traders

Choose scale, state variable, processes, parameters

• Scale– 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

Choose scale, state variable, processes, parameters

• Choose scales, entities, state variables processes and parameters

• Transfering hypothesis into equations rules

• Describing dynamics of entities

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

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

Implementation

• Mathematics or cpmputer programs • To translate verbal conceptual model

into annimated objects• Implemented model has its own

dynamics and life

Implementation

• Assumption may be wong or incomplete but impolementation is right– Allows to explore the consequences of

assumption

• Start with the simplest - null model• Set parameters , initial values of

variables

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?

E.g.: HM Model

• Try different search algorithms– with different parameters

• to see which search algorithm – strategy is the best

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

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

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

Theoretical Models

• Analytical models• Restrictive assumptions

– Rationality of agent– Representative agents– Equilibrium

• Contradicts with observations– Labaratory experiments about humman

subjects

Theoretical Models

• as precision get higher explanatory power lower– closed form solutions

• Relaxation of assumptions– geting a closed form solution is

impossible

Emprical Models

• Historically mathematical differential equations

• Or emprical models represente by algberic or difference equations whose parameters are to be estimated

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

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

Skills

• A new language for thiking about or derscribing models

• Software• Strategy for model development and

analysis

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

Ant

• An ant forgang food• Model:

– an abstracted describtion of a process, object event

Ants

• manipulability– 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

Ants

• an ant – agent– properties– behavior

Creating the Ant Foraging Model

The ODD Protocol

• Originaly for decribing ABMs or IBMs• Useful for formulating ABNs as well.• Wha kind of thigs should be in AMB?• What bahavior agents should have?• What outputs are needed_• A way of think and describe about

ABModeling

The ODD Protocol

• ODD Owverwiew Design concepts and Details

• Seven elements• Three elements overwiew what the

odel is about• One design element • Three elements deteild description of

the model complete

Purpose

• Statement of the question or problem addresed by the model

• What system we are modeling_• What we are trying to learn?

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

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’sd state – properties or attributes– Size, age, saving, opinion, memory

• 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

• Space : grids networks • Global envionment: variables change

over time usually not in space– Temperature tx rate

• Golbal Variables:• Usually not affected by agents • Exogenuous, • Provideded as data input or coming

from submodels

Process overwiew and and Scheduleing

• Structure v.s. Dynamics• Process that change the state

variables of model entities• Describes the behavior or dynamics

of odel entity• Dercribe each process with a name

– Selling buying biding influensing

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

Model’s Schedule

• The order in which processes are executed• Action: model’sd 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

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

• Basic principles• Emergence• Adaptation• Objectives• Learning• Prediction• Interraction• Stochasticity• Collectives• Observation

Initialization

• Number of agents• Provide values for state variables of

entities or environment

Initialization

• Model results depends on initial conditions– Price txx rate

• 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

– Replicate teh model

Input Data

• Environmental variables– usually change over time– policy variables

• price promotions advertising expenditures

– pyjrt rcsöğşrd• temperatukre

• not parameters • they may change over time as well

• not initial values

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