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Approaching (super)human intent recognition in stag hunt with the Naïve Utility Calculus generative model

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  • Lux Miranda

    (University of Central Florida)

  • Ozlem Ozmen Garibary

    (University of Central Florida)

Abstract

The human ability to utilize social and behavioral cues to infer each other’s intents, infer motivations, and predict future actions is a central process to human social life. This ability represents a facet of human cognition that artificial intelligence has yet to fully mimic and master. Artificial agents with greater social intelligence have wide-ranging applications from enabling the collaboration of human–AI teams to more accurately modelling human behavior in complex systems. Here, we show that the Naïve Utility Calculus generative model is capable of competing with leading models in intent recognition and action prediction when observing stag-hunt, a simple multiplayer game where agents must infer each other’s intentions to maximize rewards. Moreover, we show the model is the first with the capacity to out-compete human observers in intent recognition after the first round of observation. We conclude with a discussion on implications for the Naïve Utility Calculus and of similar generative models in general.

Suggested Citation

  • Lux Miranda & Ozlem Ozmen Garibary, 2023. "Approaching (super)human intent recognition in stag hunt with the Naïve Utility Calculus generative model," Computational and Mathematical Organization Theory, Springer, vol. 29(3), pages 434-447, September.
    • Handle: RePEc:spr:comaot:v:29:y:2023:i:3:d:10.1007_s10588-022-09367-y
    DOI: 10.1007/s10588-022-09367-y
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    References listed on IDEAS

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    1. Schlüter, Maja & Baeza, Andres & Dressler, Gunnar & Frank, Karin & Groeneveld, Jürgen & Jager, Wander & Janssen, Marco A. & McAllister, Ryan R.J. & Müller, Birgit & Orach, Kirill & Schwarz, Nina & Wij, 2017. "A framework for mapping and comparing behavioural theories in models of social-ecological systems," Ecological Economics, Elsevier, vol. 131(C), pages 21-35.
    2. Chathika Gunaratne & Nisha Baral & William Rand & Ivan Garibay & Chathura Jayalath & Chathurani Senevirathna, 2020. "The effects of information overload on online conversation dynamics," Computational and Mathematical Organization Theory, Springer, vol. 26(2), pages 255-276, June.
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