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Party CompetitionAn Agent-Based Model$
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Michael Laver and Ernest Sergenti

Print publication date: 2011

Print ISBN-13: 9780691139036

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691139036.001.0001

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Systematically Interrogating Agent-Based Models

Systematically Interrogating Agent-Based Models

(p.56) Chapter Four Systematically Interrogating Agent-Based Models
Party Competition

Michael Laver

Ernest Sergenti

Princeton University Press

This chapter develops the methods for designing, executing, and analyzing large suites of computer simulations that generate stable and replicable results. It starts with a discussion of the different methods of experimental design, such as grid sweeping and Monte Carlo parameterization. Next, it demonstrates how to calculate mean estimates of output variables of interest. It does so by first discussing stochastic processes, Markov Chain representations, and model burn-in. It focuses on three stochastic process representations: nonergodic deterministic processes that converge on a single state; nondeterministic stochastic processes for which a time average provides a representative estimate of the output variables; and nondeterministic stochastic processes for which a time average does not provide a representative estimate of the output variables. The estimation strategy employed depends on which stochastic process the simulation follows. Lastly, the chapter presents a set of diagnostic checks used to establish an appropriate sample size for the estimation of the means.

Keywords:   computer simulation, grid sweeping, Monte Carlo parameterization, stochastic processes, agent-based models

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