Approximating n-player behavioural strategy nash equilibria using coevolution

Coevolutionary algorithms are plagued with a set of problems related to intransitivity that make it questionable what the end product of a coevolutionary run can achieve. With the introduction of solution concepts into coevolution, part of the issue was alleviated, however efficiently representing and achieving game theoretic solution concepts is still not a trivial task. In this paper we propose a coevolutionary algorithm that approximates behavioural strategy Nash equilibria in n-player zero sum games, by exploiting the minimax solution concept. In order to support our case we provide a set of experiments in both games of known and unknown equilibria. In the case of known equilibria, we can confirm our algorithm converges to the known solution, while in the case of unknown equilibria we can see a steady progress towards Nash. Copyright 2011 ACM

Side-Payments and the Costs of Conflict.

Conflict and competition often impose costs on both winners and losers, and conflicting parties may prefer to resolve the dispute before it occurs. The equilibrium of a conflict game with side-payments predicts that with binding offers, proposers make and responders accept side-payments, generating settlements that strongly favor proposers. When side-payments are non-binding, proposers offer nothing and conflicts always arise. Laboratory experiments confirm that binding side-payments reduce conflicts. However, 30% of responders reject binding offers, and offers are more egalitarian than predicted. Surprisingly, non-binding side-payments also improve efficiency, although less than binding. With binding side-payments, 87% of efficiency gains come from avoided conflicts. However, with non-binding side-payments, only 39% of gains come from avoided conflicts and 61% from reduced conflict expenditures.contests, conflict resolution, side-payments, experiments

Learning in Repeated Games: Human Versus Machine

While Artificial Intelligence has successfully outperformed humans in complex combinatorial games (such as chess and checkers), humans have retained their supremacy in social interactions that require intuition and adaptation, such as cooperation and coordination games. Despite significant advances in learning algorithms, most algorithms adapt at times scales which are not relevant for interactions with humans, and therefore the advances in AI on this front have remained of a more theoretical nature. This has also hindered the experimental evaluation of how these algorithms perform against humans, as the length of experiments needed to evaluate them is beyond what humans are reasonably expected to endure (max 100 repetitions). This scenario is rapidly changing, as recent algorithms are able to converge to their functional regimes in shorter time-scales. Additionally, this shift opens up possibilities for experimental investigation: where do humans stand compared with these new algorithms? We evaluate humans experimentally against a representative element of these fast-converging algorithms. Our results indicate that the performance of at least one of these algorithms is comparable to, and even exceeds, the performance of people