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Critical interplay between density-dependent predation and evolution of the selfish herd

Published:06 July 2013Publication History

ABSTRACT

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that the predator attack mode plays a critical role in the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of "domains of danger." Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work verifies Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.

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          • Published in

            cover image ACM Conferences
            GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation
            July 2013
            1672 pages
            ISBN:9781450319638
            DOI:10.1145/2463372
            • Editor:
            • Christian Blum,
            • General Chair:
            • Enrique Alba

            Copyright © 2013 ACM

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            Publication History

            • Published: 6 July 2013

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            GECCO '13 Paper Acceptance Rate204of570submissions,36%Overall Acceptance Rate1,669of4,410submissions,38%

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