This paper shows that cooperation can be sustained until close to the end of a finite-horizon, continuous-time prisoners’ dilemma when there is informational asymmetry in how quickly players can respond. The simulated equilibrium closely replicates recent experimental results (Friedman and Oprea, 2012, AER). The core argument is extended to a class of canonical preemption games with private inf...

Abstract. The discontinuous dependence of the properties of a quantum game on its entanglement has been shown up to be very much like phase transitions viewed in the entanglement-payoff diagram [J. Du et al., Phys. Rev. Lett, 88, 137902 (2002)]. In this paper we investigate such phase-transition-like behavior of quantum games, by suggesting a method which would help to illuminate the origin of ...

We study the problem of development of intelligent machine learning applications to exploit the problems of adaptation that arise in multi-agent systems, for expected-long-termprofit maximization. We present two results. First, we propose a learning algorithm for the Iterated Prisoners Dilemma (IPD) problem. Using numerical analysis we show that it performs strictly better than the tit-for-tat ...

Stereotypes may influence the attitudes that individuals have towards others. Stereotypes, therefore, represent biases toward and against others. In this paper, we formalise stereotypical bias within trust evaluations. Then, using the iterated prisoners’ dilemma game, we quantitatively analyse how cooperation and mutual trust between self-interested agents are affected by stereotypical bias. We...

We have developed a new series of multi-agent reinforcement learning algorithms that choose a policy based on beliefs about co-players policies. The algorithms are applicable to situations where a state is fully observable by the agents, but there is no limit on the number of players. Some of the algorithms employ embedded beliefs to handle the cases that co-players are also choosing a policy b...