Towards modeling agent negotiators by analyzing human negotiation behavior

Abstract

Negotiation is a fundamental aspect of social interaction. Our research aims to contribute towards the creation of artificial agent negotiators that can be used for training purposes to improve human negotiation skills. To achieve that, we address the challenge of identifying differences in human negotiation styles and relating those differences to individuals' personality traits. In particular, we follow a data-driven approach by collecting data on how people negotiate against an agent using a fixed-response strategy during a task involving the partition of a set of items. We then use different machine learning techniques to: 1) analyze the relationship between negotiation styles and personality traits; 2) characterize changes in the human negotiation behavior during the game; 3) discover human behavior patterns in response to different offers by the agent player. Our analyses show how different personality traits lead to distinct behaviors during the negotiation. In turn, this data will allow us to build agent negotiators that have a rich behavioral repertoire and are able to adapt to human negotiation trainees, thus fostering more interesting learning experiences.