This 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.
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.