An Effective Conversation Tactic for Creating Value over Repeated Negotiations

Abstract

Automated negotiation research focuses on getting the most value from a single negotiation, yet real-world settings often involve repeated serial negotiations between the same parties. Repeated negotiations are interesting because they allow the discovery of mutually beneficial solutions that don't exist within the confines of a single negotiation. This paper introduces the notion of Pareto efficiency over time to formalize this notion of value creation through repeated interactions. We review literature from human negotiation research and identify a dialog strategy, favors and ledgers, that facilitates this process. As part of a longer-term effort to build intelligent virtual humans that can train human negotiators, we create a conversational agent that instantiates this strategy, and assess its effectiveness with human users, using the established Colored Trails negotiation testbed. In an empirical study involving a series of repeated negotiations, we show that humans are more likely to discover Pareto optimal solutions over time when matched with our favor-seeking agent. Further, an agent that asks for favors during early negotiations, regardless of whether these favors are ever repaid, leads participants to discover more joint value in later negotiations, even under the traditional definition of Pareto optimality within a single negotiation. Further, agents that match their words with deeds (repay their favors) create the most value for themselves. We discuss the implications of these findings for agents that engage in long-term interactions with human users.