Intuitive Expertise and Perceptual Templates

What Can Game Theory Tell Us about an AI ‘Theory of Mind’?

Game theory includes a rich source of methods for analysing strategic interactions where there are a small number of agents, each having only a few choices. In more complex settings though, where there are many choices over indefinite time horizons involving large social groups, these methods are unlikely to fully capture the causes of agent behaviour. If agents are able to simplify the task of understanding what others might do by modelling the constraints of others, particularly unobservable cognitive constraints, then the possible behavioural outcomes can be similarly restricted, thereby reducing the complexity of a social interaction. Having a cognitive representation of the unobserved causal states of others is an aspect of a ‘Theory of Mind’ and it plays a central role in the psychology of social interactions. In this article I examine a selection of results on the theory of mind and connect these with the ‘game theory of mind’ to draw conclusions regarding the complexity of one-on-one and large-scale social coordination. To make this explicit, I will illustrate the relationship between the two psychological terms ‘introspection’ and ‘theory of mind’ and the economic analysis of game theory, while retaining as much as possible of the richness of the psychological concepts. It will be shown that game theory plays an important role in modelling interpersonal relationships for both biological and artificial agents, but it is not yet the whole story, and some psychological refinements to game theory are discussed.

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

This review looks at 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, 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 remain to be answered in all three fields in order to make progress in producing realistic models of human decision-making in the real world in which we live in.