Contemplation vs. Intuition. A reinforcement learning approach

Inkoo Cho and Anna Rubinchik ()

No WP2015/3, Working Papers from University of Haifa, Department of Economics

Abstract: In a search for a positive model of decision-making with observable primitives, we rely on the burgeoning literature in cognitive neuroscience to construct a three-element machine (agent). Its control unit initiates either impulsive or cognitive element to solve a problem in a stationary Markov environment, the element "chosen" depends on whether the problem is mundane or novel, memory of past successes and the strength of inhibition. Our predictions are based on a stationary asymptotic distribution of the memory, which, depending on the parameters, can generate different "characters", e.g., an uptight dimwit, who could succeed more often with less inhibition, as well as a relaxed wise-guy, who could gain more with a stronger inhibition of impulsive (intuitive) responses. As one would expect, stronger inhibition and lower cognitive costs increase the frequency of decisions made by the cognitive element. More surprisingly, increasing the "carrot" and reducing the "stick" (being in a more supportive environment) enhances contemplative decisions (made by the cognitive unit) for an alert agent, i.e., the one who identifies novel problems frequently enough.

Keywords: the two-system decision-making; executive control; inhibition; adaptive learning; stochastic approximation (search for similar items in EconPapers)
JEL-codes: D01 (search for similar items in EconPapers)
Pages: 32
New Economics Papers: this item is included in nep-cbe, nep-cmp and nep-mic
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