The Neuroeconomics of Learning and Information Processing; Applying Markov Decision Process
AbstractThis paper deals with cognitive theories behind agent-based modeling of learning and information processing methodologies. Herein, I undertake a descriptive analysis of how human agents learn to select action and maximize their value function under reinforcement learning model. In doing so, I have considered the spatio-temporal environment under bounded rationality using Markov Decision process modeling to generalize patterns of agent behavior by analyzing the determinants of value functions, and of factors that modify policy- action-induced cognitive abilities. Since detecting patterns are central to the human cognitive skills, this paper aspires at uncovering the entanglements of complex contextual pattern identification by linking contexts with optimal decisions that agents undertake under hypercompetitive market pressure through learning which have however, implicative applications in a wide array of social and macroeconomic domains.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 28883.
Date of creation: 14 Feb 2011
Date of revision:
Cognitive theory; Reinforcement Learning; Markov Decision Process; Glia; Action potential; policy pattern; Neuroeconomics;
Find related papers by JEL classification:
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-02-26 (All new papers)
- NEP-CBE-2011-02-26 (Cognitive & Behavioural Economics)
- NEP-EVO-2011-02-26 (Evolutionary Economics)
- NEP-NEU-2011-02-26 (Neuroeconomics)
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