Simulation of foraging behavior using a decision-making agent with Bayesian and inverse Bayesian inference: Temporal correlations and power laws in displacement patterns

Shuji Shinohara*, Hiroshi Okamoto, Nobuhito Manome, Pegio Yukio Gunji, Yoshihiro Nakajima, Toru Moriyama, Ung il Chung

*この研究の対応する著者

研究成果: Article査読

抄録

It has been stated that in human migratory behavior, the step length series may have temporal correlation and that there is some relationship between this time dependency and the fact that the frequency distribution of step length follows a power-law distribution. Furthermore, the frequency of occurrence of the step length in some large marine organisms has been found to switch between power-law and exponential distributions, depending on the difficulty of prey acquisition. However, to date it has not been clarified how the aforementioned three phenomena arise: the positive correlation created in the step length series, the relation between the positive correlation of the step length series and the form of an individual's step length distribution, and the switching between power-law and exponential distributions depending on the abundance of prey. This study simulated foraging behavior using the Bayesian decision-making agent simultaneously performing both knowledge learning and knowledge-based inference to analyze how the aforementioned three phenomena arise. In the agent with learning and inference, past experiences were stored as hypotheses (knowledge) and they were used in current foraging behavior; at the same time, the hypothesis continued to be updated based on new experiences. The simulation results show that the agent with both learning and inference has a mechanism that simultaneously causes all the phenomena.

本文言語English
論文番号111976
ジャーナルChaos, solitons and fractals
157
DOI
出版ステータスPublished - 2022 4月

ASJC Scopus subject areas

  • 統計物理学および非線形物理学
  • 数学 (全般)
  • 物理学および天文学(全般)
  • 応用数学

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