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Agent Based Models and RNetLogo

by Joseph Rickert If I had to pick just one application to be the “killer app” for the digital computer I would probably choose Agent Based Modeling (ABM). Imagine creating a world populated with hundreds, or even thousands of agents, interacting with each other and with the environment according to their own simple rules. What kinds of patterns and behaviors would emerge if you just let the simulation run? Could you guess a set of rules that would mimic some part of the real world? This dream is probably much older than the digital computer, but according to Jan Thiele’s brief account of the history of ABMs that begins his recent paper, R Marries NetLogo: Introduction to the RNetLogo Package in the Journal of Statistical Software, academic work with ABMs didn’t really take off until the late 1990s. Now, people are using ABMs for serious studies in economics, sociology, ecology, socio-psychology, anthropology, marketing and many other fields. No less of a complexity scientist than Doyne Farmer (of Dynamic Systems and Prediction Company fame) has argued in Nature for using ABMs to model the complexity of the US economy, and has published on using ABMs to drive investment models. in the following clip of a  2006 interview, Doyne talks about building ABMs to explain the role of subprime mortgages on the Housing Crisis. (Note that when asked about how one would calibrate such a model Doyne explains the need to collect massive amounts of data on individuals.)   Fortunately, the tools for building ABMs seem to be keeping pace with the ambition of the modelers. There are now dozens of platforms for building ABMs, and it is somewhat surprising that NetLogo, a tool with some whimsical terminology (e.g. agents are called turtles) that was designed for teaching children, has apparently become a defacto standard. NetLogo is Java based, has an intuitive GUI, ships with dozens of useful sample models, is easy to program, and is available under the GPL 2 license. As you might expect, R is a perfect complement for NetLogo. Doing serious simulation work requires a considerable amount of statistics for calibrating models, designing experiments, performing sensitivity analyses, reducing data, exploring the results of simulation runs and much more. The recent JASS paper Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: a Cookbook Using NetLogo and R by Thiele and his collaborators describe the R / NetLogo relationship in great detail and points to a decade’s worth of reading. But the real fun is that Thiele’s RNetLogo package lets you jump in and start analyzing NetLogo models in a matter of minutes. Here is part of an extended example from Thiele's JSS paper that shows R interacting with the Fire model that ships with NetLogo. Using some very simple logic, Fire models the progress of a forest fire.  Snippet of NetLogo Code that drives the Fire model to go if not any? turtles ;; either fires or embers [ stop ] ask fires [ ask neighbors4 with [pcolor = green] [ ignite ] set breed embers ] fade-embers tick end   ;; creates the fire turtles to ignite ;; patch procedure sprout-fires 1 [ set color red ] set pcolor black set burned-trees burned-trees + 1 end The general idea is that turtles represent the frontier of the fire run through a grid of randomly placed trees. Not shown in the above snippet is the logic that shows that the entire model is controlled by a single parameter representing the density of the trees.  This next bit of R code shows how to launch the Fire model from R, set the density parameter, and run the model. # Launch RNetLogo and control an initial run of the # NetLogo Fire Model library(RNetLogo) nlDir <- "c:>

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More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

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