Authors: Michail Zak
A QRN simulating human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and of classical neural nets describing evolution of probabilities of these processes which represent the mental dynamics. The autonomy of the decision making process is achieved by a feedback from mental to motor dynamics which changes the stochastic matrix based upon the probability distributions. This feedback replaces an unavailable external information by an internal knowledgebase stored in the mental model in the form of probability distributions. As a result, the coupled motor-mental dynamics is described by a nonlinear version of Markov chains which can decrease entropy without an external source of information. Applications to common sense based decisions as well as to evolutionary games are discussed.
Comments: 24 Pages.
[v1] 2016-02-18 19:28:27
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