Exploring Bounded Rationality Through Computational Intelligence: A Comprehensive Review

Abstract

Bounded rationality departs from the traditional economic assumption of fully rational agents by highlighting the impact of cognitive and computational constraints on human decisions. This review synthesizes recent progress in computational intelligence that addresses how to model and enhance rationality within the bounds of these limitations. We discuss foundational theories, including Herbert Simon’s bounded rationality and Ariel Rubinstein’s algorithmic framework, alongside contemporary computational approaches involving heuristic search, machine learning, and multi-agent systems. Special attention is given to methods that bridge psychology, economics, and artificial intelligence, offering realistic models of decision-making and examining their consequences for economics, behavioral finance, and autonomous system design. The review concludes by identifying future research opportunities for creating more adaptable and robust agents capable of navigating complex environments under limited information and computational resources.