Bayesian model of the social effects of emotion in decision-making in multiagent systems

  • Authors:

  • Celso M. de Melo;Peter Carnevale;Stephen Read;Dimitrios Antos;Jonathan Gratch

  • Affiliations:

  • Institute for Creative, Technologies, Waterfront, Playa Vista, CA;University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;Harvard University, Maxwell-Dworkin, Cambridge, MA;Institute for Creative Technologies, Playa Vista, CA

  • Venue:
  • Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
  • Year:
  • 2012

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Abstract

Research in the behavioral sciences suggests that emotion can serve important social functions and that, more than a simple manifestation of internal experience, emotion displays communicate one's beliefs, desires and intentions. In a recent study we have shown that, when engaged in the iterated prisoner's dilemma with agents that display emotion, people infer, from the emotion displays, how the agent is appraising the ongoing interaction (e.g., is the situation favorable to the agent? Does it blame me for the current state-of-affairs?). From these appraisals people, then, infer whether the agent is likely to cooperate in the future. In this paper we propose a Bayesian model that captures this social function of emotion. The model supports probabilistic predictions, from emotion displays, about how the counterpart is appraising the interaction which, in turn, lead to predictions about the counterpart's intentions. The model's parameters were learnt using data from the empirical study. Our evaluation indicated that considering emotion displays improved the model's ability to predict the counterpart's intentions, in particular, how likely it was to cooperate in a social dilemma. Using data from another empirical study where people made inferences about the counterpart's likelihood of cooperation in the absence of emotion displays, we also showed that the model could, from information about appraisals alone, make appropriate inferences about the counterpart's intentions. Overall, the paper suggests that appraisals are valuable for computational models of emotion interpretation. The relevance of these results for the design of multiagent systems where agents, human or not, can convey or recognize emotion is discussed.