Modeling Reciprocal Behavior in Human Bilateral Negotiation

Reciprocity is a key determinant of human behavior and has been well documented in the psychological and behavioral economics literature. This paper shows that reciprocity has significant implications for computer agents that interact with people over time. It proposes a model for predicting people’s actions in multiple bilateral rounds of interactions. 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). The model is trained and evaluated over a series of negotiation rounds that vary players’ possible strategies as well as their benefit from potential strategies at each round. Results show that reasoning about reciprocal behavior significantly improves the predictive power of the model, enabling it to outperform alternative models that do not reason about reciprocity...

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Ya'akov Gal, Avi Pfeffer
• Keywords: AAAI 2007, Intelligent Agents, Model Represents Reciprocity, Multiple Bilateral Rounds, Social Factors