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2019 | OriginalPaper | Chapter

EcoSim, an Enhanced Artificial Ecosystem: Addressing Deeper Behavioral, Ecological, and Evolutionary Questions

Authors: Ryan Scott, Brian MacPherson, Robin Gras

Published in: Cognitive Architectures

Publisher: Springer International Publishing

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Abstract

This chapter discusses individual-based models (IBMs) and uses the Overview, Design concepts, and Details (ODD) protocol to describe a predator-prey evolutionary ecosystem IBM called EcoSim. EcoSim is one of the most complex and large-scale IBMs of its kind, allowing hundreds of thousands of intricate individuals to interact and evolve over thousands of time steps. Individuals in EcoSim have a behavioral model represented by a fuzzy cognitive map (FCM). The FCM, described in this chapter, is a cognitive architecture well-suited for individuals in EcoSim due to its efficiency and the complexity of decision-making it allows. Furthermore, it can be encoded as a vector of real numbers, lending itself to being part of the genetic material passed on by individuals during reproduction. This allows for meaningful evolution of their behaviors and natural selection without predefined fitness. EcoSim has been enhanced to increase the breadth and depth of the questions it can answer. New features include: fertilization of primary producers by consumers, predator-prey combat, sexual reproduction, sex-linkage of genes, multiple modes of reproduction, size-based dominance hierarchy, and more. In addition to describing EcoSim in detail, we present data from default EcoSim runs to show potential users the types of data EcoSim generates. Furthermore, we present a brief sensitivity analysis of some variables in EcoSim, and a case study that demonstrates research that can be performed using EcoSim. In the case study, we elucidate some evolutionary and behavioral impacts on animals under two conditions: when primary production is limited, and when energy expenditure is reduced.
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Metadata
Title
EcoSim, an Enhanced Artificial Ecosystem: Addressing Deeper Behavioral, Ecological, and Evolutionary Questions
Authors
Ryan Scott
Brian MacPherson
Robin Gras
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-319-97550-4_14

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