Agent-Based Modeling and Simulation (ABMS) Bertan Badur Department of Management Information Systems Boğaziçi University

Emergence Chapter 8 of Agent-Based and Individual-Based Modeling: A Practical Introduction, by S. F. Railsback and V. Grimm BehaviorSpace Guide of NetLogo User Menual NetLogo’s Model Library Biology category –Simple Birth Rate –Flocking models

Outline 1.Introduction and Objectives 2.A Model with Less-Emergent Dynamics 3.Simulation Experments and BehaviorSpace 4.A Model with Complex Emergent Dynamics 5.Summary and Conclusions

1. Introduction and Objectives important and unique - ABMs emergence of –complex, often unexplained – system level –from – underlying processes unpredictable unexplanable in principle buy by simulation Properties: –not sum of properties of individuals –different result – individual properties –can not be predicted from individual properties

Corridor width in butterfly outcome – corridor width not sum of or individual property –how bfs move: uphill or random influenced from – movements and environment width – time sum of where individual bfs are qualitativly predictable to some extend –as q decreases – wirdth increases So width emerges from –movement of bfs: uphill or random –envirnonment

Level of emergence too low emposed –no need for an ABM –modeled with other methods too many results emerging complex ways from complex individual behavior –too compleicated to be understood Best –intermediate level of emergence

Learning Objectives Less or emergent results form ABMs Designing and alalysing simulation experiments BehaviorSpace Analys outputs by graphical or statistical methods

2. A Model with Less-Emergent Dynamics Biology Category of NetLogo’s Library – Simple Birth Rates –How birth rate differences of two spefies influence number of these species number of ofspring produced – time step probability of death – constant Experiments –red-fertility – at 2.0 –vary blue-fertility from 2.1 to 5.0 graph of –ticks until red extingtion v.s. blue-fertality

When birth rates are close, they - co-exitst for a long time As the difference in birth rates increases the time to red extinction decreases rapidly

3. Simulation Experments and BehaviorSpace Experiments – replicates of senarios –stochastic elements senarios – “treatment” statisticians senario defined –model, parameters, inputs, initial conditions vary blue-fertility from 2.1 to 5.0 in increments of 0.1 –30 senarios, 10 replicates for each –each run continues until nomore red turtles –output: ticks number extinction occured mean and standard deviation of time to extrnction v.s. blue fertility rate

Sensitivity Experiments Vary one parameter over a wide range and investigate how model resopnses How the model and system it represents –response one factor at a time BehaviorSpace - seperate program –run simulation experiments –save the results in a file Fuctions –Create senarios – varying global variables –Generate replicates – (repitations in NetLogo) of senarios –Collect results from each run and write to a file –Run some NetLogo commands at the end of each run

Using BehaviorSşpace From Tools menu open BehaviorSpace Create a new experiment – new Name of experiment Vary variables –blue-fertility from 2.1 to 5.0 with increments 0.1 [“ blue-fertility” [ ] ] Constants –[“ red-fertility” 2.0 ] –[“ carrying-capacity” 1000 ] repititions value 10 “Measure runs...” –ticks stop condition –red-count = 0

Programming note: How BehaviorSpace works At the start of each run –set variables in “Vary variables...” before entering setrup When “Measure runs...” is not checked –written to output after a run stops stop in go or “stop condition” in BehaviorSpace “Time limit” box When “Measure runs...” is checked –firet output – end of setup –then each time go completes –if a stop in go – no output at thet time –use a “stop condition” or “time limit” – produce ougtputat the end of go just before stoping

to obtain output at every time step 1- remove stoping statements from go put them to BehaviorSpace 2- put ticks as a first statement tıo go then stop so

4. A Model with Complex Emergent Dynamics Biology of NetLogo’s Library “Flocking” Reynolds (1987) How complex and realistic dynamocs can emerge from simple agent behavior that could not be predicted School of fishs and flocks of birds – emergent properties of how individual animals move against each other individuals behavior – adjusting their movement direction in response to direction and location of other nearby individuals (their “flockmates”)

Objective4 of turtles all other turtles within a radius – vision parameter Three objectives: –moving in the same direction with theri flockmates – align –moving towards the flockmates – cohere –maintaining a minimum seperation with others – seperate Parameters –maximum angle a turtle can rotatre –minimum seperation distance Complex results Parameters interract

two Properties of Results flockings complex flocks change characteristics change parameters – characteristics change Parameters interract –the effect of one parameter depends on the value of other Show two common characteristics of emergent dynamics –1- qualitative – hard to describe with numbers –state of the birth rate model with two numbers –2- it takes some time before the patterns emerge –worm-up period – dynamics gradually emerges

Level of emergence What results output? for the birth rate – obvious time until red turtles went extinct Emergence? –1 – results different from sum of individual properties –2- different type of results –3- not easity predicted Yes

Quantitative measures Number of turtles who have flockmates The mean number of flockmates per turtle The mean distance between a turtle and the nearest other turtle The standard deviation of heading over all turtles –a simple meadure of variability in direction get output from every ticks number of ticks – 500 number of replications – 10 one senario – “baseline” – default parameter values

Meadure runs using these reporters count turtles with [any? flockmates] mean [count flockmates] of turtles mean [min [distance myself] of other turtles] of turtles standard-deviation [headıng] of turtles add set turtlemates no-turtles crt population... end

Another Experiment if turtles adapt their direction considering only their closest neighbor change to find-flockmates ;; turtle procedure set flockmates other turtles in-radius vision end to to find-flockmates ;; turtle procedure set flockmates other turtles in-radius vision set flockmates flockmates with-min [distance myself] end

NetLogo brainteaser 1- why not use set flockmates min-one-of other turtles [distance myself] 2- or set flockmates other turtles with- min [distance myself]

Contrasting senarios Another common experiment type look at differences betwen two or more different senarios replicate and see how outputs are different in different “treatments”

5. Summary and Conclusions