Reciprocity is a key determinant of human behavior. The ability to retaliate or reward others' actions in the absence of direct utility benefit has been shown to bring about and maintain cooperation, to induce punishment as well as forgiveness between players over time. The behavioral sciences literature has identified strategies that exhibit reciprocal qualities which under certain conditions satisfy conditions of optimality. These models were confined to simple, static interactions such as the prisoners' dilemma game, and assume rational behavior. Consequentially, they do not capture many of the dynamic settings and behavior that characterize human social interaction. This work proposes a model for bilateral interaction between people in an environment which provides an analog to real-world task settings. This environment varies players' possible strategies, dependency relationships and their rewards at each round. The model represents reciprocity as a tradeoff between two social factors: the extent to which players reward and retaliate others' past actions (retrospective reasoning), and their estimate about the future ramifications of their actions (prospective reasoning). Results show that a model that reasons about reciprocal behavior provides better predictive power than models that learn from people but do not reason about reciprocity, or play various game theoretic equilibria. In addition, retrospective reasoning was found to be more relevant than prospective reasoning in people's deliberation processes. These results suggest that the types of social factors that affect people's reciprocal interaction in the world can be defined and learned within a formal framework. |