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Complex Dynamics of Single Agent Choice Governed by Dual-Channel Multi-Mode Reinforcement Learning

In: Strategic Innovative Marketing

Author

Listed:
  • Ihor Lubashevsky

    (University of Aizu)

  • Arkady Zgonnikov

    (University of Aizu)

  • Sergey Maslov

    (Russian Academy of Sciences)

  • Namik Goussein-zade

    (Russian Academy of Sciences)

Abstract

According to the modern theory of adaption of socioeconomic systems to unknown environments only the interaction between agents can be responsible for various emergent phenomena governed by decision-making and agent learning. Previously we advocated the idea that adopting a more complex model for the agent individual behavior including rational and irrational reasons for decision-making, a more diverse spectrum of macro-level behaviors can be expected. To justify this idea we have developed a model based on the reinforcement learning paradigm extended to including an additional channel of processing information; an agent is biased by novelty seeking, the intrinsic inclination for exploration. In the present paper we demonstrate that the behavior of the single novelty-seeking agent may be extremely irregular and the concepts of chaos can be used to characterize it.

Suggested Citation

  • Ihor Lubashevsky & Arkady Zgonnikov & Sergey Maslov & Namik Goussein-zade, 2017. "Complex Dynamics of Single Agent Choice Governed by Dual-Channel Multi-Mode Reinforcement Learning," Springer Proceedings in Business and Economics, in: Androniki Kavoura & Damianos P. Sakas & Petros Tomaras (ed.), Strategic Innovative Marketing, pages 561-567, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-33865-1_68
    DOI: 10.1007/978-3-319-33865-1_68
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    Cited by:

    1. Feng Li & Liang Cheng & Xi Wang & Xiaona He & Yiyu Wang, 2023. "The Effects of Spherical Video-Based Virtual Reality and Conventional Video on Students’ Descriptive Writing Achievement and Motivation: A Comparative Study," SAGE Open, , vol. 13(3), pages 21582440231, August.

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