Autonomous change of behavior for environmental context

An intermittent search model with misunderstanding search pattern

Hisashi Murakami, Yukio Gunji

    Research output: Contribution to journalArticle

    Abstract

    Although foraging patterns have long been predicted to autonomously adapt to environmental conditions, empirical evidence has been found in recent years. This evidence suggests that the search strategy of animals is open to change so that animals can flexibly respond to their environment. In this study, we began with a simple computational model that possesses the principal features of an intermittent strategy, ie, careful local searches separated by longer steps, as a mechanism for relocation, where an agent in the model follows a rule to switch between two phases, but it could misunderstand this rule, ie, the agent follows an ambiguous switching rule. Thanks to this ambiguity, the agent's foraging strategy can continuously change. First, we demonstrate that our model can exhibit an autonomous change of strategy from Brownian-type to Lévy type depending on the prey density, and we investigate the distribution of time intervals for switching between the phases. Moreover, we show that the model can display higher search efficiency than a correlated random walk.

    Original languageEnglish
    JournalMathematical Methods in the Applied Sciences
    DOIs
    Publication statusAccepted/In press - 2017

    Fingerprint

    Pattern Search
    Foraging
    Animals
    Correlated Random Walk
    Search Strategy
    Prey
    Ambiguous
    Computational Model
    Local Search
    Relocation
    Switch
    Model
    Interval
    Switches
    Demonstrate
    Context
    Strategy
    Evidence

    Keywords

    • Animal foraging
    • Intermittent strategy
    • Random search

    ASJC Scopus subject areas

    • Mathematics(all)
    • Engineering(all)

    Cite this

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    abstract = "Although foraging patterns have long been predicted to autonomously adapt to environmental conditions, empirical evidence has been found in recent years. This evidence suggests that the search strategy of animals is open to change so that animals can flexibly respond to their environment. In this study, we began with a simple computational model that possesses the principal features of an intermittent strategy, ie, careful local searches separated by longer steps, as a mechanism for relocation, where an agent in the model follows a rule to switch between two phases, but it could misunderstand this rule, ie, the agent follows an ambiguous switching rule. Thanks to this ambiguity, the agent's foraging strategy can continuously change. First, we demonstrate that our model can exhibit an autonomous change of strategy from Brownian-type to L{\'e}vy type depending on the prey density, and we investigate the distribution of time intervals for switching between the phases. Moreover, we show that the model can display higher search efficiency than a correlated random walk.",
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