Information Theory for Agents in Artificial Intelligence, Psychology, and Economics

Abstract

This review covers some of the central relationships between artificial intelligence, psychology, and economics through the lens of information theory, specifically focusing on formal models of decision-theory. In doing so we look at a particular approach that each field has adopted and how information theory has informed the development of the ideas of each field. A key theme is expected utility theory, its connection to information theory, and the Bayesian approach to decision-making and forms of (bounded) rationality. What emerges from this review is a broadly unified formal perspective derived from three very different starting points that reflect the unique principles of each field. Each of the three approaches reviewed can, in principle at least, be implemented in a computational model in such a way that, with sufficient computational power, they could be compared with human abilities in complex tasks. However, a central critique that can be applied to all three approaches was first put forward by Savage in The Foundations of Statistics and recently brought to the fore by the economist Binmore: Bayesian approaches to decision-making work in what Savage called `small worlds’ but cannot work in `large worlds’. This point, in various different guises, is central to some of the current debates about the power of artificial intelligence and its relationship to human-like learning and decision-making. Recent work on artificial intelligence has gone some way to bridging this gap but significant questions still need to be answered in all three fields in order to make progress on these problems.

A Maximum Entropy Model of Bounded Rational Decision-Making with Prior Beliefs and Market Feedback

“…Foley [14] discusses Rational expectations and boundedly rational behaviour in economics. Harré [15] gives an overview of information-theoretic decision-theory and applications in economics, and Foley [16] analyses information-theory and results on economic behaviour.…”

Entropy, Information, and the Updating of Probabilities

“…We adopt a Bayesian interpretation of probability as a degree of credibility: the degree to which we ought to believe that a proposition is true if only we were ideally rational. For a discussion of a decision theory that might be relevant to the economics and psychology of partially rational agents see [ 34 , 35 , 36 ]. An entropic framework for modelling economies that bypasses all issues of bounded rationality is described in [ 37 ].…”

The Influence of “Artificial Intelligence + Human–Computer Interaction” on Teachers’ Psychological Changes in Academic Management in Colleges

“…Human-computer interaction and AI are two crucial research fields in the intelligent information age (Horgan et al, 2018). The relationship between them has changed with their development and typical applications based on their deep integration are also reflected in education (Schafer, 2019;Harré, 2021). HCI provides research ideas and application requirements for AI, while AI greatly promotes the transformation and development of HCI technology in the modern world (Vedapradha et al, 2019;Sharma, 2020).…”