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

Ihor Lubashevsky (), Arkady Zgonnikov (), Sergey Maslov () and Namik Goussein-zade ()
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Ihor Lubashevsky: University of Aizu
Arkady Zgonnikov: University of Aizu
Sergey Maslov: Russian Academy of Sciences
Namik Goussein-zade: Russian Academy of Sciences

A chapter in Strategic Innovative Marketing, 2017, pp 561-567 from Springer

Abstract: 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.

Keywords: Reinforcement learning; Intrinsic motivation; Novelty; Complex dynamics; Emergence; Agent-based (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-33865-1_68

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DOI: 10.1007/978-3-319-33865-1_68

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