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Effects of Search Strategies on Collective Problem-Solving

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  • Shun Cao

    (Department of Information Science Technology, University of Houston, Houston, TX 77204-4007, USA)

Abstract

In today’s dynamic and complex social environments, collaborative human groups play a critical role in addressing a wide range of real-world challenges. Collective problem-solving, the process of finding solutions through the collaboration of individuals, has become imperative in addressing scientific and technical problems. This paper develops an agent-based model to investigate the influence of different search strategies (simple local search, random search, and adaptive search) on the performance of collective problem-solving under various conditions. The research involves simulations on various problem spaces and considers distinct search errors. Results show that random search initially outperforms other strategies when the search errors are relatively small, yet it is surpassed by adaptive search in the long term when the search errors increase. A simple local search consistently performs the worst among the three strategies. Furthermore, the findings regarding adaptive search reveal that the speed of adaptation in adaptive search varies across problem spaces and search error levels, emphasizing the importance of context-specific parameterization in adaptive search strategies. Lastly, the values of P s = 0.9 and P f = 0.2 obtained through human subject experiments in adaptive search appear to be a favorable choice across various scenarios in this simulation work, particularly for complex problems entailing substantial search errors. This research contributes to a deeper understanding of the effectiveness of search strategies in complex environments, providing insights for improving collaborative problem-solving processes in real-world applications.

Suggested Citation

  • Shun Cao, 2023. "Effects of Search Strategies on Collective Problem-Solving," Mathematics, MDPI, vol. 11(22), pages 1-16, November.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4642-:d:1279811
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    1. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    2. Wenjing Xu & Seyyed Ahmad Edalatpanah & Ali Sorourkhah, 2023. "Solving the Problem of Reducing the Audiences’ Favor toward an Educational Institution by Using a Combination of Hard and Soft Operations Research Approaches," Mathematics, MDPI, vol. 11(18), pages 1-21, September.
    3. Edoardo Mollona, 2008. "Computer simulation in social sciences," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 12(2), pages 205-211, May.
    4. Shun Cao & Neil G. MacLaren & Yiding Cao & Jason Marshall & Yingjun Dong & Francis J. Yammarino & Shelley D. Dionne & Michael D. Mumford & Shane Connelly & Robert W. Martin & Colleen J. Standish & Tan, 2022. "Group Size and Group Performance in Small Collaborative Team Settings: An Agent-Based Simulation Model of Collaborative Decision-Making Dynamics," Complexity, Hindawi, vol. 2022, pages 1-16, October.
    5. David G. Victor, 2006. "Toward Effective International Cooperation on Climate Change: Numbers, Interests and Institutions," Global Environmental Politics, MIT Press, vol. 6(3), pages 90-103, August.
    6. Arthur, W Brian, 1991. "Designing Economic Agents that Act Like Human Agents: A Behavioral Approach to Bounded Rationality," American Economic Review, American Economic Association, vol. 81(2), pages 353-359, May.
    7. Lihua Sun & L. Jeff Hong & Zhaolin Hu, 2014. "Balancing Exploitation and Exploration in Discrete Optimization via Simulation Through a Gaussian Process-Based Search," Operations Research, INFORMS, vol. 62(6), pages 1416-1438, December.
    8. Jan W. Rivkin, 2000. "Imitation of Complex Strategies," Management Science, INFORMS, vol. 46(6), pages 824-844, June.
    9. Daniel A. Levinthal, 1997. "Adaptation on Rugged Landscapes," Management Science, INFORMS, vol. 43(7), pages 934-950, July.
    10. Qixuan Tang & Chengjun Wang & Tao Feng, 2023. "Research on the Group Innovation Information-Sharing Strategy of the Industry–University–Research Innovation Alliance Based on an Evolutionary Game," Mathematics, MDPI, vol. 11(19), pages 1-16, October.
    11. Stephan Billinger & Nils Stieglitz & Terry R. Schumacher, 2014. "Search on Rugged Landscapes: An Experimental Study," Organization Science, INFORMS, vol. 25(1), pages 93-108, February.
    12. Ha Hoang & Frank T. Rothaermel, 2010. "Leveraging internal and external experience: exploration, exploitation, and R&D project performance," Strategic Management Journal, Wiley Blackwell, vol. 31(7), pages 734-758, July.
    13. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
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