Modelling learning and decision making under information processing constraints

Abstract

This thesis explores the impact of information processing constraints on models of human learning and decision making. This is achieved through altering existing methods within the fields of economic decision making and reinforcement learning, with inspiration from information theory and the rational inattention economic framework. The result is two models, one of human decision making and one of human learning, which seek to represent the way that differences in individual information processing abilities impact learning and decision making. Data from human responses in a learning task is used to compare the accuracy of this model against existing methods. Results from experimentation show that these models achieve a high degree of accuracy while accounting for the impact of differences in information processing capacity utilized by participants during the learning task. These results further the understanding of how cognitive limitations impact human learning and decision making, and suggest ways in which this type of model could potentially benefit current approaches in artificial intelligence, by incorporating more human-like learning strategies.