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Locally noisy autonomous agents improve global human coordination in network experiments

Author

Listed:
  • Hirokazu Shirado

    (Yale Institute for Network Science, Yale University
    Yale University)

  • Nicholas A. Christakis

    (Yale Institute for Network Science, Yale University
    Yale University
    Yale University
    Yale University)

Abstract

A networked colour coordination game, with humans interacting with autonomous software bots, shows that bots acting with small levels of random noise and being placed centrally in the network improves not only human–bot interactions but also human–human interactions at distant nodes.

Suggested Citation

  • Hirokazu Shirado & Nicholas A. Christakis, 2017. "Locally noisy autonomous agents improve global human coordination in network experiments," Nature, Nature, vol. 545(7654), pages 370-374, May.
    • Handle: RePEc:nat:nature:v:545:y:2017:i:7654:d:10.1038_nature22332
    DOI: 10.1038/nature22332
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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. Mike Farjam & Olexandr Nikolaychuk & Giangiacomo Bravo, 2018. "Does risk communication really decrease cooperation in climate change mitigation?," Climatic Change, Springer, vol. 149(2), pages 147-158, July.
    3. Gallo, Edoardo & Barak, Darija & Langtry, Alastair, 2023. "Social distancing in networks: A web-based interactive experiment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 107(C).
    4. Brent Simpson & Bradley Montgomery & David Melamed, 2023. "Reputations for treatment of outgroup members can prevent the emergence of political segregation in cooperative networks," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. Kinga Makovi & Anahit Sargsyan & Wendi Li & Jean-François Bonnefon & Talal Rahwan, 2023. "Trust within human-machine collectives depends on the perceived consensus about cooperative norms," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Atsushi Ueshima & Matthew I. Jones & Nicholas A. Christakis, 2024. "Simple autonomous agents can enhance creative semantic discovery by human groups," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    7. Arno Riedl & Ingrid M. T. Rohde & Martin Strobel, 2021. "Free Neighborhood Choice Boosts Socially Optimal Outcomes in Stag-Hunt Coordination Problem," CESifo Working Paper Series 9012, CESifo.
    8. Xin-Jie Zhang & Yong Tang & Jason Xiong & Wei-Jia Wang & Yi-Cheng Zhang, 2018. "Dynamics of Cooperation in Minority Games in Alliance Networks," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    9. Agnieszka Kowalska-Styczeń & Krzysztof Malarz, 2020. "Noise induced unanimity and disorder in opinion formation," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-22, July.
    10. Chowdhury, Soumyadeb & Budhwar, Pawan & Dey, Prasanta Kumar & Joel-Edgar, Sian & Abadie, Amelie, 2022. "AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework," Journal of Business Research, Elsevier, vol. 144(C), pages 31-49.
    11. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    12. Niccolo Pescetelli, 2021. "A Brief Taxonomy of Hybrid Intelligence," Forecasting, MDPI, vol. 3(3), pages 1-11, September.
    13. Malarz, Krzysztof & Kułakowski, Krzysztof, 2021. "Heider balance of a chain of actors as dependent on the interaction range and a thermal noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    14. Zhang, Xin-Jie & Tang, Yong & Xiong, Jason & Wang, Wei-Jia & Zhang, Yi-Cheng, 2020. "Ranking game on networks: The evolution of hierarchical society," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    15. Füller, Johann & Hutter, Katja & Wahl, Julian & Bilgram, Volker & Tekic, Zeljko, 2022. "How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    16. Julián Vicens & Josep Perelló & Jordi Duch, 2018. "Citizen Social Lab: A digital platform for human behavior experimentation within a citizen science framework," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-20, December.
    17. Soojong Kim, 2019. "Directionality of information flow and echoes without chambers," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-22, May.
    18. Tina Eliassi-Rad & Henry Farrell & David Garcia & Stephan Lewandowsky & Patricia Palacios & Don Ross & Didier Sornette & Karim Thébault & Karoline Wiesner, 2020. "What science can do for democracy: a complexity science approach," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-4, December.
    19. Kim, Dennie & Funk, Russell & Zaheer, Aks, 2020. "Structure in Context: A Morphological View of Whole Network Performance," SocArXiv x6q7g, Center for Open Science.
    20. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    21. Zhigang Cao & Cheng-zhong Qin & Xiaoguang Yang & Boyu Zhang, 2019. "Dynamic matching pennies on networks," International Journal of Game Theory, Springer;Game Theory Society, vol. 48(3), pages 887-920, September.

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