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Modeling Populations of Adaptive Individuals$
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Steven F. Railsback and Bret C. Harvey

Print publication date: 2020

Print ISBN-13: 9780691195285

Published to Princeton Scholarship Online: January 2021

DOI: 10.23943/princeton/9780691195285.001.0001

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Guidance for Using State-and Prediction-Based Theory

Guidance for Using State-and Prediction-Based Theory

Chapter:
(p.93) Chapter 8 Guidance for Using State-and Prediction-Based Theory
Source:
Modeling Populations of Adaptive Individuals
Author(s):
Steven F. Railsback, Bret C. Harvey
Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691195285.003.0008

This chapter outlines the guidance on using state- and prediction-based theory (SPT) to build models of populations and communities of adaptive individuals, detailing five steps unique to SPT. The most important aspect of SPT to remember is that one is not trying to build optimal, or even necessarily accurate, models of how an organism's behavior affects its future fitness. Instead, one is trying to find simplistic models that produce realistic behavior in contexts where optimization is impossible. While SPT can be used like dynamic state variable modeling (DSVM), as a framework for thinking about and modeling how an individual makes a particular decision, its main purpose is to model adaptive trade-off decisions in individual-based population models. Thus, using SPT is part of the larger process of developing, analyzing, and applying an IBM to address population-level questions, and the five steps therefore include that process.

Keywords:   state-based theory, prediction-based theory, populations, communities, adaptive individuals, dynamic state variable modeling, adaptive trade-off decisions, individual-based population models

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