Strategic Information Processing from Behavioural Data in Iterated Games

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.

Complexity Economics in a Time of Crisis: Heterogeneous Agents, Interconnections, and Contagion

In this article, we consider a variety of different mechanisms through which crises such as COVID-19 can propagate from the micro-economic behaviour of individual agents through to an economy’s aggregate dynamics and subsequently spill over into the global economy. Our central theme is one of changes in the behaviour of heterogeneous agents, agents who differ in terms of some measure of size, wealth, connectivity, or behaviour, in different parts of an economy. These are illustrated through a variety of case studies, from individuals and households with budgetary constraints, to financial markets, to companies composed of thousands of small projects, to companies that implement single multi-billion dollar projects. In each case, we emphasise the role of data or theoretical models and place them in the context of measuring their inter-connectivity and emergent dynamics. Some of these are simple models that need to be ‘dressed’ in socio-economic data to be used for policy-making, and we give an example of how to do this with housing markets, while others are more similar to archaeological evidence; they provide hints about the bigger picture but have yet to be unified with other results. The result is only an outline of what is possible but it shows that we are drawing closer to an integrated set of concepts, principles, and models. In the final section, we emphasise the potential as well as the limitations and what the future of these methods hold for economics.

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.

A Co-Opetitive Automated Negotiation Model for Vertical Allied Enterprises Teams and Stakeholders

Upstream and downstream of supply chain enterprises often form a tactic vertical alliance to enhance their operational efficiency and maintain their competitive edges in the market. Hence, it is critical for an alliance to collaborate over their internal resources and resolve the profit conflicts among members, so that the functionality required by stakeholders can be fulfilled. As an effective solution, automated negotiation for the vertical allied enterprises team and stakeholder will sufficiently make use of emerging team advantages and significantly reduce the profit conflicts in teams with grouping decisions rather than unilateral decisions by some leader. In this paper, an automated negotiation model is designed to describe both the collaborative game process among the team members and the competitive negotiation process between the allied team and the stakeholder. Considering the co-competitiveness of the vertical allied team, the designed model helps the team members making decision for their own sake, and the team counter-offers for the ongoing negotiation are generated with non-cooperative game process, where the profit derived from negotiation result is distributed with Shapley value method according to contribution or importance contributed by each team member. Finally, a case study is given to testify the effectiveness of the designed model.