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.
When to (or not to) trust intelligent machines: Insights from an evolutionary game theory analysis of trust in repeated games
