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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.

We present the experimental evidence of the dynamical traps model describing the human fuzzy rationality in the dynamical systems framework. The results of the experiments on virtual stick balancing are compared to the results of the previous studies on the dynamical trap effect. According to the results obtained, we suggest that the dynamical traps model actually captures certain essential features of human fuzzy rationality and therefore may serve as an alternative to the traditional notion of stable equilibrium in describing the behavior of human as an element of complex social systems.