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Vulnerable robots positively shape human conversational dynamics in a human–robot team

Author

Listed:
  • Margaret L. Traeger

    (Yale Institute for Network Science, Yale University, New Haven, CT 06520; Department of Sociology, Yale University, New Haven, CT 06520)

  • Sarah Strohkorb Sebo

    (Department of Computer Science, Yale University, New Haven, CT 06520)

  • Malte Jung

    (Department of Information Science, Cornell University, Ithaca, NY 14853)

  • Brian Scassellati

    (Department of Computer Science, Yale University, New Haven, CT 06520)

  • Nicholas A. Christakis

    (Yale Institute for Network Science, Yale University, New Haven, CT 06520; Department of Sociology, Yale University, New Haven, CT 06520; Department of Biomedical Engineering, Yale University, New Haven, CT 06520; Department of Statistics and Data Science, Yale University, New Haven, CT 06520)

Abstract

Social robots are becoming increasingly influential in shaping the behavior of humans with whom they interact. Here, we examine how the actions of a social robot can influence human-to-human communication, and not just robot–human communication, using groups of three humans and one robot playing 30 rounds of a collaborative game ( n = 51 groups). We find that people in groups with a robot making vulnerable statements converse substantially more with each other, distribute their conversation somewhat more equally, and perceive their groups more positively compared to control groups with a robot that either makes neutral statements or no statements at the end of each round. Shifts in robot speech have the power not only to affect how people interact with robots, but also how people interact with each other, offering the prospect for modifying social interactions via the introduction of artificial agents into hybrid systems of humans and machines.

Suggested Citation

  • Margaret L. Traeger & Sarah Strohkorb Sebo & Malte Jung & Brian Scassellati & Nicholas A. Christakis, 2020. "Vulnerable robots positively shape human conversational dynamics in a human–robot team," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(12), pages 6370-6375, March.
    • Handle: RePEc:nas:journl:v:117:y:2020:p:6370-6375
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    Cited by:

    1. Zou, Tengjian & Ertug, Gokhan & Roulet, Thomas, 2024. "Learning from machines: How negative feedback from machines improves learning between humans," Journal of Business Research, Elsevier, vol. 172(C).
    2. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    3. Li, Wen-Jing & Chen, Zhi & Jin, Ke-Zhong & Wang, Jun & Yuan, Lin & Gu, Changgui & Jiang, Luo-Luo & Perc, Matjaž, 2022. "Options for mobility and network reciprocity to jointly yield robust cooperation in social dilemmas," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    4. Sætra, Henrik Skaug, 2020. "The foundations of a policy for the use of social robots in care," Technology in Society, Elsevier, vol. 63(C).

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