Game Theory II

From social contracts to climate agreements, individuals engage in groups that must collectively reach decisions with varying levels of equality and fairness. These dilemmas also pervade Distributed Artificial Intelligence, in domains such as automated negotiation, conflict resolution or resource allocation. As evidenced by the well-known Ultimatum Game --- where a Proposer has to divide a resource with a Responder --- payoff-maximizing outcomes are frequently at odds with fairness. Eliciting equality in populations of self-regarding agents requires judicious interventions.

The El Farol Bar problem is a game theory problem where actors must decide whether or not to go to a bar with limited information. We recently proposed to study this problem as a dynamical system of strategy distribution space and revealed its dynamics and attractors in this phase space [1]. However, the previous research was limited in that the phase space required N-1 dimensions to fully visualize where N is the number of decision strategies.

Institutions and investors alike have often been confronted with the question of which individuals are most eligible for the distribution of endowments. Here, we consider the Ultimatum game in a spatial setting and propose a hierarchy of interference mechanisms based upon the amount of information available to an external decision-maker and desired standards of fairness. Starting from previous findings on the spatial Prisoner’s Dilemma, we explore the differences arising from targeting different roles, but also the effects of mutation and stochasticity.

Anthropogenic climate change, public health measures or even group hunting, are some of the many collective endeavours characterized by uncertain, long-term and non-linear returns. We operationalize these scenarios in a collective-risk dilemma [1], where players can contribute into a public good over a number of rounds, and will only observe their payoff when the game ends. The non-linearity of returns is modelled through a threshold that determines the risk of collective loss.

The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, business and also policy. This anxiety is further energised by an AI race narrative that makes people believe they might be missing out. Whether real or not, a belief in this narrative may be detrimental as some stake-holders will feel obliged to cut corners on safety precautions, or ignore societal consequences just to "win".