A study of computational and human strategies in revelation games

Many negotiations in the real world are characterized by incomplete information, and participants’ success depends on their ability to reveal information in a way that facilitates agreements without compromising their individual gain. This paper presents an agent-design that is able to negotiate proficiently with people in settings in which agents can choose to truthfully reveal their private information before engaging in multiple rounds of negotiation. Such settings are analogous to real-world situations in which people need to decide whether to disclose information such as when negotiating over health plans and business transactions. The agent combined a decision-theoretic approach with traditional machine-learning techniques to reason about the social factors that affect the players’ revelation decisions on people’s negotiation behavior. It was shown to outperform people as well as agents playing the equilibrium strategy of the game in empirical studies spanning hundreds of subjects. It was also more likely to reach agreement than people or agents playing equilibrium strategies. In addition, it had a positive effect on people’s play, allowing them to reach significantly better performance when compared to people’s play with other people. These results are shown to generalize for two different settings that varied how players depend on each other in the negotiation.