MIS 585 Special Topics in IMS Agent-Based Modeling Chapter 3: Exploring and Extedning Agent-Based Models

MIS 585

Special Topics in IMS

Agent-Based ModelingChapter 3:

Exploring and Extedning

Agent-Based Models

Outline

IntorductionThe Fire ModelThe Diffusion-Limited Aggregation

(DLA) ModelThe Segregation ModelThe El Farol ModelConclusion

Introduction

• This chapter is based on• IABM-WR: Chapter 3

Introduction• Learn how to modify and extend an ABM• Four characteristics of AB Modeling• 1 – Simple rules can be used to

generate complex phenomena• 2 – Randomness in individual behavior

can results in consistant patterns of population behavior

• 3 – Complex patterns can be “self-oranize” without any leader organizer

• 4 – Different models emphasize different aspects of the world

Rule 1

• many models – simple rules– without any complex math– deep understanding of the knowledge

domain

• E.g.: the fire model• with very simple rules about

spreading o fire form tree to tree• interesting things to say about how

likely a fire is sepead all over the forest

Rule 2

• seeing an ordered population behavior

• individual level need not be ordered

• by fixed rules• stochastic rules mey results

ordered patterns at the population level

Rule 3

• flock of bird - no leader bird or organizer abut how to fly

• self-organize without a leader

Rule 4• different models different aspects

of the world• For each model filter out some

aspects and remove others• Segragation model is about

– how urban population develop• but nothing to say about

– location of retail stores– or parks– or school districts

Outline



The Fire Model

• Description of the Fire Model• First Extension: Probablistic

Transitions• Second Extension: Adding Wind• Third Extension: Allow Long

Distance Transmission• Summary of the Fire Model• Advenced Modeling Applications

The Fire Model• many complex systems

– critical treshold– tipping point

• A small change in one parameter results in a large change in outcome

• model of a forest fire– spread of a disease– diffusion of innovation

• Sensitive to one parameter: percentage of trees – after some p

Tipping Point

• know where tipping point is and where you are

Description of the Fire Model

• percolation:• in 1987, Denish physisists and

complex systems theoritst, Per Bak• self-oganizing criticallity

A Simple Fire Model• (Wilenksly 1997a), NetLogo library

in Earch Science section• Fire Simple in: Simple Models >

IABM > Chapter 3 > Fire Extension > Fire Simple

• Only patches – four kind– greeen: unburn tree– red: burning tree– brown: burned tree– block: empty space

A Simple Fire Model (cont.)

• when setup– Left edeg of World – all red – on fire

• when running– the fire will ignate neighboring trees –

neighbor four

• continues • untill run out of trees

• Control parameter: density – a probability whether or not a pacth is

a tree

Initialization

• set up trees: a randomly seleted patch is a tree with a probablity density.

• make a column of burning treese at the left edge

• keep track of how many trees there are

setupglobals [initial-trees]patches-own []to setup ca ask patches [ if random 100 < density [set pcolor green] if pxcor = min-pxcor [set pcolor red] ] set initial-trees count patches with [pcolor = green]

reset-ticksend

Exercises

• Different initializations of trees• Inililize always fixed number of

trees• Find other ways of initialization of

threes and burning trees

iterations

• ask the burning trees to set fire to any neighboring (four neithbors) non-burning trees

go

to go

ask patches with [pcolor = red] [

ask neighbors4 with [pcolor = green] [

set pcolor red

]

]

tick

end

Stoping Condition

• iterations stop when all trees are burned

if all? patches [pcolor != red]

[stop]• i

final go procedureto go if all? patches [pcolor != red] [stop] ask patches with [pcolor = red] [ ask neighbors4 with [pcolor = green] [

set pcolor red ] set pcolor red - 3.5 ] tickend

Reporter

• write a reporter to return trees fireing

• and use the reporter propersly

Reporter

to report fire-trees

return patches with [pcolor = red]

end• change go fromask patches with [pcolor = red]• toask fire-trees

Monitoring percentage of burend trees

• Add a moitor to the interface to see the percentages of trees bured

• Add a moitor • to the reporter:(count patches with [shade-of? pcolor red]) / initial-trees * 100

Tipping Point

• Experiment with density parameters and see how percent of trees burned changes with the density of trees

• At 59% there is a tipping point• a silite change in input parameter

has a large effect on output

First Extension: Probablistic Transitions

• Many simplifying assumptions:• fire does not spread

deterministically• but many factors:

– wind, wood type, how close the branches are

• not certaion - with a proabability• Change the go procedure so that a

burning tree ignates fire to its neighbor with a probability

probabilistic spread• old go statement ask patches with [pcolor = red] [ ask neighbors4 with [pcolor = green] [ set pcolor red ]...• new go statement ask patches with [pcolor = red] [ ask neighbors4 with [pcolor = green] [ if random 100 < probability-of-spread [set pcolor red] ]...

Experimentation

• Experiment with both probability-of-spread and density parameters

• setting probability to 100 returns to the original model

• for a given density, high values of probability-of-spread is needed to get almost same percentage of spread

Second Extension: Adding Wind

• wind: – increases the chance of fire spread in

the directio it is blowing– decreases the chnage of fire spread

in the direction it is not blowing– no effect on the chance of spread in

the perpendicular direction• Implemented by two sliders

– speed from the south (negative from north)

– speed from the west (negative from east)

Initialization

• both form -25 to +25• they affect probability-of-spread parameter

• Implementation:• create a local variable probability initially set to probability-of-spread parameter

• creation by let, setting by set,

The pseudocodefor all red pathces ask their unborn neighbor threes - create and set probability to

probability-of-spread - determine the direction - adjust the probability according to the

diection - determine whether the neighbor will

burn or not if so -- set its color to red

Implementation• create and set probability to probability-

of-spread: let probability probability-of-spread

• determine the direction:compute the direction from you (the

green tree) to the burning tree(NOTE: “myself” is the burning tree (the

red patch) that asked you to execute commands)

let direction towards myself

adjust the probability

• if the burning tree is north of you

• so the south wind impedes the fire spreading to you

• so reduce the probability of spread if direction = 0 [

set probability probability - south-wind-speed ]

you green tree

burning tree myself


• if the burning tree is east of you

• so the west wind impedes the fire spreading to you

• so reduce the probability of spreadif direction = 90 [

set probability probability - west-wind-speed ]

burning tree myself

you: green tree


• if the burning tree is south of you

• so the south wind aids the fire spreading to you

• so increase the probability of spread

if (direction = 180 ) [ set probability probability + south-wind-speed ]

burning tree myself

you: green tree


• if the burning tree is west of you

• so the west wind aids the fire spreading to you

• so increase the probability of spread

if direction = 270 [

set probability probability + west-wind-speed ]

you

burning tree

catch on fire

• after determining the probability• set color of the tree just burningif random 100 < probability

[ set pcolor red ]

Explanation

• modify the probability of spread– increase or decrease accordingly

• for each neighnbor• first determine

– which direction the fire is trying to spead

– how wind effect the probability

• with the updated probability– the fie will spread to the neighboring

tree

New Predicates

• towards• ask

Experimentation

• set – density at %100– let the wind blow strong from the

south and west– set probability-of-spead fairly low

arond %38

• triangular to northeast

Third Extension: Allow Long Distance Transmission

• another effet of wind: enables fire to jump over long distances

• add a switch to control jumping• in NetLogo – control boolean

variables• add – big-jumps: on and off

– off: reduced to Fire 2 Model– on: ingnate new fire at some distance

in the direction of the wind

the code if random 100 < probability [ set pcolor red

;; if big jumps is on, then sparks can fly farther

if big-jumps? [ let target patch-at ( west-wind-speed / 5 )

( south-wind-speed / 5 ) if target != nobody and [pcolor] of target =

green [ ask target [ set pcolor red ];; to ignite the target

patch ] ;; end if target ] ;; end big-jump? ] ;; end random 100

• if big-jumps? is true• it looks for a target patch some distance

away in the direction of the wind• experiment with different parameters • observation: lines in the direction of wind

Figure 3.7• increases the probability of raaching the

other end but reusting patterns are different with – chunks of world not burned

• as new features are added modify the questions and the measures

Extensions

• probablistic sparks or locations

Summary of the Fire Model• three change to the basic model • affecting tipping point in different

ways• as model assumptions or mechnizms

change new measurse msy be needed

• in Extension 3 – whether the file will move to right edge

mey not be a good measure• Tipping points:

– characteristics of inputs and outputs– not the models’

Advanced Modeling Applications

• generalized into model of a percolation:

• Given a structure with probablistic transitions between locations

• how likely that an new entity diffuses form one side to another

• E.g.: – innovation diffusion– spread of diseases – epidemiology

Outline

IntorductionThe Fire ModelThe Diffusion-Limited

Aggregation (DLA) ModelThe Segregation ModelThe El Farol ModelConclusion

The Diffusion-Limit Aggregation (DLA) Model

• Description of the Diffusion-Limit Aggregation (DAL) Model

• First Extension: Probablistic Sticking• Second Extension: Neighbor

Influence• Third Extension: Different

Aggregates• Summary of the DAL Model• Advenced Modeling Applications

Intorduction• in ABMs no direct relation between

– complexity of resulting pattern and– complexity of the uderlying rules

• simple rules may create complex pattrns

• focus not on complex rules but on interractions

• ABMs are interesting:– not because what each agent does– what tghe agents do together

A very simple model

• what agents does – move randomly around the world

• and stop moving when a basic condition is met

• simple rules complex patterns may emerge

Discription of the DAL Model

• In many physical processes– creation of clouds to snowflakes, dust and

smoke

• particles aggregate interesting ways• DAL: an idealization of this process• Witten & Sander 1981, 1983• generate patterns found in nature

– cristals, foral, fungi, growth of human lungs

• social patterns– growth of cities

DLA NetLogo Model

• Starts with – many red particles– a green patch at the center

• iterations - GO forever– all particles more randomly “wigging” left

and right by a small random turns – and forward one unit– if any of its neighbors green– turn the patch its is on green and dies

• fractile like patterns formed by patches

Initialization• Setting the world:

– origine: center– max-pcor 100, max 100.– wrapping on– patch size 1

• number of particles– slider variable: num-particles– size 1.5, color: red– uniformly distributed around the world

• one patch at center with color green

setupto setup clear-all ask patch 0 0 [ set pcolor green ] create-turtles num-particles [ set color red set size 1.5 setxy random-xcor random-ycor

] reset-ticksend

iterations

• wiggle-angle– slider – varibale: 0 – 100 – default 60

• ask each particle;; • turn a random amount right and

left• move one unit forward• if you are touching a green patch,

turn your own patch green and then die

goto go ask turtles [ right random wiggle-angle left random wiggle-angle forward 1 if any? neighbors with [pcolor = green]

[ set pcolor green die ] ;; end if ] ;;end ask tickend

New Predicatcates

• any?• Exercise how would you do the

same thing without using any?

First Extension: Probablistic Sticking

• if a particle comes to contact with a stationary object with certainity it becomes stationary

• control the probablity of a particle becoming stationary

proablistic stoping

• since stopping is probablistic• a particle may be on top of another

green patch – we do not want to stop

• stop with a probability only when• on a black patch and one of the

neighbors are green• in simple version because

movemnt is forward by 1 unit it is not needed

part of go

if ( pcolor = black )

and ( any? neighbors with [pcolor = green] )

and ( random-float 1.0 < probability-of-sticking )

[ set pcolor green

die

] ;; end if

Slider

• Slider• variable: probablity-of-sticking– max value: 1.0– min value: 0.0– increment: 0.01– default: 0.5

•

Experimentation

• Experiment with different probabilities

• as probability of sticking goes to 1• simple model

– thiner branches as prob 1.0

• as prob 0.0 thicker strucutral branches

Second Extension: Neighbor Influence

• how probability of sticking is related to number of neighbors that are already stationary

• if a particle is moving • it is more likely to stick if

– two or three neighbors are stationary

– then only one

The problem• add a switch to the interface

– variable: neighbor-influence?– boolean variable. true or false– convension end with ? to inform

programmers

• Modify probablity of sticking– define local-prob

• if neighbor-influence? is off– the same: no neighbor effect

• if on: mulitply by fraction of green neighbors

pseudeocode

• declare and initiize local-prob• if neighbor-influence is TRUE then

make the probability proportionate to the number of green neighbors, otherwise use the original value

in other word; increase the probability of sticking the more green neighbors there are

• modify the stopping condition

part of the go... forward 1 let local-prob probability-of-sticking if neighbor-influence? [ set local-prob probability-of-sticking * (count neighbors with [ pcolor = green ] / 8) ] ;; end if neighbor-influeince if (pcolor = black) and ( any? neighbors with [pcolor = green] ) and ( random-float 1.0 < local-prob ) [ set pcolor green die ] ;; end if] ;; end ask...

Experimentation

• 1 - it takes much longer to form structures as there is a lower problity of a moving particle stopping

• 2 – the structure is very tick and almost bloblike

• 3 – many one patch-wide branches

Third Extension: Different Aggregates

• Start with a fixed user determined number of seeds

• set a set of randomly selected patches to green

• not neccesarily at the center

Initialization

• Start with a user determined number of seed

• Give number of seeds from a silder• Insert a silder to the interface

– variable: num-seeds– nin value: 1– max value: 10– increment: 1– default: 10

part of the setup

ask n-of num-seeds patches [ set pcolor green ]

Observations

• selects exactly num-seeds initail patches as seeds

Summary of the DLA Model

• Three versions of the DLA model• simple rules complex patterns• first two extensions:

– particles decide when to stop– patterns – thicker and more

substantial

• third extension:– starting from multiple seeds


• AB Modeling straddles between mathematics of fractiles

• See mathematics section • fractiles subsection

Outline



The Segregation Model

• Description of the Segregation Model

• First Extension: Adding Multiple Ethnicities

• Second Extension: • Third Extension:• Summary of the Model• Advenced Modeling Applications

Introduction• two approaches to ABM• 1 – common in science start with a

known phenomenon• phenomenon-based modeling• aggregate pattern – reference

pattern• generate with agent rules• 2 – start with simple rules• to see what patterns develop• explaratory modeling• combination of them

• for high levels of the treshold:– the model predicts the conferamtion– checkerboard is seregated into areas of all

pennies and all dimes• surprise • for low levels of the treshold:

– pennies and dimes are clustered– global segregation occured dispied the

relative toleranc of individuals• There was no single individual in the

model who wanted segregated neighborhood

• but the group as a whole moved segregation

• macrobehavior from micromotives

Controversial• 1 – belived that housing segreagation is

due to individuals being prejudicate– prejiduce itself is not the cause of segregation, – emergent effect of aggregation of weak

predijuse

• to reduce housing segregation – not reduce predicuse

• 2 – people behavior with simple rules– critics: hummans have complex cognition and

social arrangements

• Oversimplifed model of people’s housing choice but it revels an unknown dynamics

Description of the Segregation Model

• NetLogo version:– Model Library > Simple Models >

IABM Textook > Chapter Three > Segregation Extensions > Segregation Simple.nlogo

• Initialization• Iterations

Initialization

• apporximately equal number of red and green turtles are distributed evenly around the world

• each turtle defermines wheter it is happy or not, depending on its neighbors of the same color with itself exceeds the %-similar-wanted treshold or not

iterations

• check whether there are unhappy turtles

• any unhappy turtle moves to a new location– turning a rondom amaout– moving forward by a random

amaount from 0 to 10– if not occupied, settels their– if this location is occupied oves again

setup: algorithm

• create a turtle on NUMBER randomly selected patches.

• note that slider's maximum value is 2500 which is a little less than the total number of patches

• make approximately half the turtles red and the other half green

setupto setup clear-all ask n-of number patches [ sprout 1 ] ask turtles [ set color one-of [red green] ]

update-variables reset-ticksend

update-variables

to update-variables

update-turtles

update-globals

end

Interface

• view:– origin: center– max-pxcor,max-pycor:25, wrapped

• Slider: – variable: number– nax: 2500– min: 500– default: 2000

updatre turtlesfor each turtle use More neighborhood: count the number of my neighbors that

are the same color as me set similar-nearby... count the total number of neighbors set total-nearby ... I’m happy if there are at least the

minimal number of same-colored neighbors

set happy? ...

update-turtlesto update-turtles ask turtles [ set similar-nearby count (turtles-on neighbors)

with [color = [color] of myself] set total-nearby count (turtles-on neighbors)

set happy? similar-nearby >= ( %-similar-wanted * total-nearby / 100 )

]end

updating globals• calculate the following to be monitored• calculate percentatage of similar

neighborspercent-similar: • on the average, what percent of a

turtle's neighbors are the same color as that turtle?

• calculate percentage of unhappy turtlespercent-unhappy• what percent of the turtles are

unhappy?

update-globalsto update-globals

let similar-neighbors sum [similar-nearby] of turtles

let total-neighbors sum [total-nearby] of turtles

set percent-similar (similar-neighbors / total-neighbors) * 100

set percent-unhappy (count turtles with [not happy?]) / (count turtles) * 100

end

Variables• globals:percent-similarpercent-unhappy

• turtles:happy?:• for each turtle, indicates whether at least %-similar-

wanted percent of that turtles' neighbors are the same color as the turtle

similar-nearby: • how many neighboring patches have a turtle with my

color?total-nearby: • sum of previous two variables

Variables

globals [

percent-similar

percent-unhappy

]

turtles-own [

happy?

similar-nearby

total-nearby

]

Interface tab • plot:

– name: percent similar– x-label: time, y-label: %– pen update: plot percent-similar

• plot:– name: percent unhappy– x-label: time, y-label: %– pen update: plot percent-unhappy

• monitors for percent-similar and percent-unhappy variables

go

to go

if all? turtles [happy?] [ stop ]

move-unhappy-turtles

update-variables

tick

end

moving turtlesto move-unhappy-turtles ask turtles with [ not happy? ] [ find-new-spot ]end

to find-new-spot rt random-float 360 fd random-float 10 if any? other turtles-here [ find-new-spot ] ;; keep going until we find an unoccupied patch

setxy pxcor pycor ;; move to center of patch

end

First Extension: Adding Multiple Ethnicities

• add a third, forth or even the fifth ethnicity

• modify the setup• originally

– makes all turtles red– ask half ot them to be green

• l

• other colors then red and green;• blue, yellow and orange• modify setup set colors [red green yellow blue orange]

• add globalsglobals [ ... colors]• initilizes the global variable colors to

be a list of the fife colors

Interface Tab

• allow the user to control number of ethnicities in the model

• add a slider– variable: number-of-ethnicities

• set bound from 2 to 5

setup• assign a color to each turtle from

the list of our colorsask turtles [ set color (item (random number-of-ethnicities) colors)

] • each turtle picks its color randomly

form the list, but up to the number-of-ethnicities

New Predicate: itemitem index list item index string• On lists, reports the value of the item in the

given list with the given index.• On strings, reports the character in the given

string at the given index• Note that the indices begin from 0, not 1. (The

first item is item 0, the second item is item 1, and so on.)

;; suppose mylist is [2 4 6 8 10]show item 2 mylist=> 6show item 3 "my-shoe"=> "s"

Explorations

• run the model with different number of ethnicities

• much longer to settel• however once setteled • percents similar displaid is

independnet of the number of ethnicities

• Why?

Second Extension: Allowing Diverse Tresholds

• Original model – every agent has the same similarity treshold– same level of tolerence to other

ethnicities in their neighborhood

• it is likely that different individuals has different levels of tolerance

• modify turtles-own:

turtles-own

turtles-own [

...

;; the threshold for this particular turtle

my-%-similar-wanted

]

modifications

• Modify initialization:• after assigning a color to each

turtle from the list of our colors• assign an individual level of %-

similar-wanted• Modify update turtles• change the update-turtles code

as well

setup

ask turtles [

set color (item (random number-of-ethnicities) colors)

set my-%-similar-wanted random %-similar-wanted

]

update turtles

to update-turtles ... ;; I’m happy if there are at least the minimal number of same-colored neighbors

set happy? similar-nearby >= ( my-%-similar-wanted * total-nearby / 100 )

]end

• percent similar monitor ends up with lower values at the end of the runs

• logical,• Some individuals are more tolorant

to other ethnicities

Third Extension: Adding Diversity-Seeking Individuals

• simplifying assumpltion:– individuals concent about too much

diversity

• individuals seeking some diversity in their neighborhood

• add %wdifferent-wanted propertiy just like %simlar-wanted

•

Modifications

• Create a %different-wanted slider• modify the update-turtle

procedure:• agents are happy only when the

number of similar agents nearby is greater than the %similar-wanted treshold and number of other agents nearby is greater than the %different-wanted treshold.

Experimentation

• percent similar result decreases as all agnets seeking diversity

• set of parameters the system never settles down– both %similar-wanted and %different-

wanted over 50

• In general, longer time to reach equilibrium.

Further Extensions

• 1 – make %different-wanted global variable agent specific %my-different-wanted

• 2 – some agents sought only diversity some others only similarity

Summary of the Segregation Model

• Different models emphisize different aspects of the world.

• Schling assumptions about peoples preferences and behavior.

• first extension: multiplicity of ethicities• second extension: from uniform agents

to heterogonous agents with different tresholds– individual differences and diversity

• Third extension: agents also seeking out diversity

Advanced Urban Modeling Applications

• Urban modeling systems– “residential preference” modles

• Integrated model of a city– commercial, industrial and governmental

models of urban policy

• E.g.:– CITIES Project: Center of Connected

Learning and Computer Modeling at Northwestern University

• Another goal of urban modeling:– ecological impact of cities and their

footprint on the environment.. E.g.: SLUCE Project at the University of Michigan

Outline



The El Farol Model

• Description of the Model• First Extension:• Second Extension:• Third Extension:• Summary of the Model• Advenced Modeling Applications

Introduction

• Add new reporters and monitors to collect data from an existing model

• You do not need to understand every part of a model to work with it

• El Farol model: list, regression• looking info tab

Description of the El Farol Model

• B. Artur• El Farol bounded rationality and

inductive reasoning• neoclasical economics – perfect

rationality

• El Farol bar• 100 total researchers lke Irish music• if they thougth it is crowded people

do not go– predict more then 60 people there

• assume attendence is publicly available

• but each agent has a limited memory• Each agent has a strategy

• no mater what strategy agents apply

• average bar population is 60• Even not have perfect information

find the optimal solution

Controls

• memory size: how many weeks of atendence they can remember

• number-strategies: number of strategies in his bag

• overcrowding-treshold: number of agent that make the bar crowded

First Extension: Color Agents That Are More Succeswful Predictors

• Each agent to keep track of• how often they go the the bar when it is

not crowded• Add a propertiy to agents: reword• update turtles-ownturtles-own [ strategies ;; list of strategies best-strategy ;; index of the current best

strategy attend? ;; true if the agent currently plans to

attend the bar prediction ;; current prediction of the bar

attendance reward ;; the amount that each agent

has been rewarded]

• Initilize revord to 0• modify setup create-turtles 100 [

set color white

move-to-empty-one-of home-patches

set strategies n-values number-strategies [random-strategy]

set best-strategy first strategies

set reward 0

update-strategies

]

modify go• update reward whenever agent goes to the bar

and it is not crowded• go procedure form if attendance > overcrowding-threshold [ ask crowded-patch [ set plabel "CROWDED" ] ]• to ifelse attendance > overcrowding-threshold [ ask crowded-patch [ set plabel "CROWDED" ] ] [ ask turtles with [ attend? ] [ set reward reward + 1 ] ]

modifications• visualization to display reword

appropriately• give different colors proportional to its

reword• relative to the maximum reword• update each agents color ask turtles [ set prediction predict-attendance best-

strategy sublist history 0 memory-size set attend? (prediction <= overcrowding-

threshold) ;; true or false ;; scale the turtle's color a shade of red

depending on its reward level (white for little reward, black for high reward)

set color scale-color red reward (max [ reward ] of turtles + 1) 0

]

• scale-color four parameters• 1 – base color• 2 – the variable linked to color – reward• 3 – first range value – a bit higher then

naximum reward• 4 – second range value – 0• if the first range value is less then the second

one then• the larger the linked variable the ligther the

color • if the second range value is less then the first

one then• the larger the linked variable the darker the

color

Second Extension: Average, Min and Max Rewords

• Adding monitors to get statistics of variables

• Monitors for reward• Monitor:

– name: Max Reward– reporter area: max [reward] or turtles

• Monitor:– name: Min Reward– reporter area: min [reward] or turtles

• Monitor:– name: Avg. Reward– reporter area: mean [reward] or turtles

Explorations

• as time progress, both average and max rewords increases but max increases more so some agents are doing better then others

Third Extension: Histogram Rework Values

• Create a new plot– name: Reward Distribution– mode of the pen: “bar”– pen update commands: histogram [reward] or turtles– plot setup commandsset-plot-y-renge 0 1

set-plot-x-range 0 (max [reward] or turtles + 1)

Explorations

• run the model severa times• first normal distribution• then some agents with high

rewards• so on they are performing better

than the average.

Summary of the El Farol Model

• Modify a model so that it provides more information then the original one

• first extension: visualization of success of agents

• second exension: statistics with some monitors; max, min, agerage rewards

• third extension: histogram of the data. Richer understanding

Further Extensions

• Why some agnets are doing well?• Investigate strategies• Do they change strategies more

often then the average performing agen?


• El Farol – ABM and machine learning

• agent chang strategies over time• Minority game

– financial markets– Santa Fe Stcok Market model

Extending models

• deterministic rules to probablistic rules

• new mechnisms that generate probablities– add different metrics

• different straring and stopping conditions

• global parameters to individual agents making agents heterogonous– parameters and new rules

Outline



Conclusion

• How to extend models in different ways

• How to explore how the models are related to key concepts of ABM

Documents

MIS 585 Special Topics in IMS Agent-Based Modeling Chapter 3: Exploring and Extedning Agent-Based Models