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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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Introduction to State-and Prediction-Based Theory

Introduction to State-and Prediction-Based Theory

(p.29) Chapter 3 Introduction to State-and Prediction-Based Theory
Modeling Populations of Adaptive Individuals
Steven F. Railsback, Bret C. Harvey
Princeton University Press

This chapter discusses the state- and prediction-based theory (SPT) and its use in individual-based models (IBMs). The fundamental concept of modern theory in behavioral ecology is that behavior acts to maximize a specific measure of fitness at a specific future time, and that this fitness measure incorporates multiple elements, such as the need to avoid predators, the need to avoid starvation, and the benefits of energy accumulation for reproduction. This concept has been applied widely and successfully in dynamic state variable modeling (DSVM), and SPT was developed as a way of using the same principle in IBMs when feedback from the behavior of other individuals, combined with unpredictable environmental conditions, make the assumption of optimality used by DSVM impossible. The chapter then looks at the differences between SPT and DSVM. To model populations of adaptive individuals, SPT is implemented using five steps. These steps include embedding SPT in an IBM that simulates the processes that drive behavior, both internal to the individual and external.

Keywords:   state-based theory, prediction-based theory, individual-based models, behavioral ecology, individual behavior, fitness measure, dynamic state variable modeling, populations, adaptive individuals

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