Using a theory of mind to find best responses to memory-one strategies

Memory-one strategies are a set of Iterated Prisoner’s Dilemma strategies that have been praised for their mathematical tractability and performance against single opponents. This manuscript investigates best response memory-one strategies with a theory of mind for their opponents. The results add to the literature that has shown that extortionate play is not always optimal by showing that optimal play is often not extortionate. They also provide evidence that memory-one strategies suffer from their limited memory in multi agent interactions and can be out performed by optimised strategies with longer memory. We have developed a theory that has allowed to explore the entire space of memory-one strategies. The framework presented is suitable to study memory-one strategies in the Prisoner’s Dilemma, but also in evolutionary processes such as the Moran process. Furthermore, results on the stability of defection in populations of memory-one strategies are also obtained

Centralized vs. Personalized Commitments and their influence on Cooperation in Group Interactions

Before engaging in a group venture agents may seekcommitments from other members in the group and,based on the level of participation (i.e. the numberof actually committed participants), decide whether itis worth joining the venture. Alternatively, agents candelegate this costly process to a (beneficent or non-costly) third-party, who helps seek commitments fromthe agents. Using methods from Evolutionary GameTheory, this paper shows that, in the context of PublicGoods Game, much higher levels of cooperationcan be achieved through such centralized commitmentmanagement. It provides a more efficient mechanismfor dealing with commitment free-riders, those who arenot willing to bear the cost of arranging commitmentswhilst enjoying the benefits provided by the paying commitment proposers.We show also that the participationlevel plays a crucial role in the decision of whetheran agreement should be formed; namely, it needs tobe more strict in the centralized system for the agreementto be formed; however, once it is done right, it ismuch more beneficial in terms of the level of cooperationas well as the attainable social welfare. In short, ouranalysis provides important insights for the design ofmulti-agent systems that rely on commitments to monitoragents’ cooperative behavior

Corpus-based intention recognition in cooperation dilemmas.

Intention recognition is ubiquitous in most social interactions among humans and other primates. Despite this, the role of intention recognition in the emergence of cooperative actions remains elusive. Resorting to the tools of evolutionary game theory, herein we describe a computational model showing how intention recognition coevolves with cooperation in populations of self-regarding individuals. By equipping some individuals with the capacity of assessing the intentions of others in the course of a prototypical dilemma of cooperation—the repeated prisoner's dilemma—we show how intention recognition is favored by natural selection, opening a window of opportunity for cooperation to thrive. We introduce a new strategy (IR) that is able to assign an intention to the actions of opponents, on the basis of an acquired corpus consisting of possible plans achieving that intention, as well as to then make decisions on the basis of such recognized intentions. The success of IR is grounded on the free exploitation of unconditional cooperators while remaining robust against unconditional defectors. In addition, we show how intention recognizers do indeed prevail against the best-known successful strategies of iterated dilemmas of cooperation, even in the presence of errors and reduction of fitness associated with a small cognitive cost for performing intention recognition.</jats:p