in Experimental Design Alferes172 Heteroskedasticity in Regression Kaufman
173 An Introduction to Exponential Random Graph Modeling Harris
174 Introduction to Time Series Analysis Pickup
175 Factorial Survey Experiments Auspurg/Hinz
176 Introduction to Power Analysis: Two-Group Studies Hedberg
177 Linear Regression: A Mathematical Introduction Gujarati
178 Propensity Score Methods and Applications Bai/Clark
179 Multilevel Structural Equation Modeling Silva/Bosancianu/Littvay
180 Gathering Social Network Data adams
181 Generalized Linear Models for Bounded and Limited Quantitative Variables, Smithson and Shou
182 Exploratory Factor Analysis, Finch
183 Multidimensional Item Response Theory, Bonifay
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Agent-Based Models
Second Edition
Nigel Gilbert
University of Surrey, United Kingdom
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Series Editor’s Introduction
Almost 50 years ago Thomas Schelling published the first agent-based model (ABM) in the social sciences. It showed how relatively modest residential preferences on the part of individual households could result in marked patterns of neighborhood residential segregation. Since then, and especially recently, applications have blossomed in many fields ranging from opinion dynamics to supply chain management, from language evolution to disease epidemiology, from consumer behavior to urban planning. The second edition of Introduction to Agent-Based Models targets this broad audience. The author, Nigel Gilbert, is one of the founders of computational social science and an authority on agent-based models.
As Professor Gilbert defines it, agent-based modeling is “a computational method that enables a researcher to create, analyze, and experiment with models composed of agents that interact within the environment.” ABMs range from highly abstract simplified models to facsimile models that attempt to replicate real observations. They explicitly link micro and macro levels of analysis, as illustrated by Schelling’s model of households and neighborhoods. Because agent-based models incorporate dynamic interdependencies among the individual agents, the consequences for macrolevel change in these models are emergent, frequently nonlinear, and sometimes surprising, as was the case with Schelling’s model.
Like the first edition, the second edition of Introduction to Agent-Based Models is for beginners. It is suitable as a supplement for undergraduate as well as graduate courses in formal models, simulation, and computational social science; it is also a quick first introduction for any interested social science practitioner. The author carefully defines concepts, outlines the steps involved in planning, building, and reporting ABMs, and includes a helpful glossary. Readers are shown how to use the NetLogo modeling environment, freely available to students, teachers, and researchers worldwide, to build and run a simple ABM. NetLogo helps readers get their feet wet, even those with little background in coding. The second edition of Introduction to Agent-Based Models retains the strengths of the first but updates the material, expands the coverage of verification, validation, and documentation, and addresses some new topics such as the use of ABMs to inform public
