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Simulating the Cost of Cooperation: A Recipe for Collaborative Problem-Solving

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  • Andrea Guazzini

    (Center for the Study of Complex Dynamics (CSDC), University of Florence, Via di San Salvi 12, 50135 Firenze, Italy
    Department of Education and Psychology, University of Florence, Via di San Salvi 12, 50135 Firenze, Italy)

  • Mirko Duradoni

    (Department of Information Engineering, University of Florence, Via Santa Marta 3, 50139 Firenze, Italy)

  • Alessandro Lazzeri

    (Department of Information Engineering, University of Pisa, Via Girolamo Caruso 16, 56122 Pisa, Italy)

  • Giorgio Gronchi

    (Center for the Study of Complex Dynamics (CSDC), University of Florence, Via di San Salvi 12, 50135 Firenze, Italy)

Abstract

Collective problem-solving and decision-making, along with other forms of collaboration online, are central phenomena within ICT. There had been several attempts to create a system able to go beyond the passive accumulation of data. However, those systems often neglect important variables such as group size, the difficulty of the tasks, the tendency to cooperate, and the presence of selfish individuals (free riders). Given the complex relations among those variables, numerical simulations could be the ideal tool to explore such relationships. We take into account the cost of cooperation in collaborative problem solving by employing several simulated scenarios. The role of two parameters was explored: the capacity, the group’s capability to solve increasingly challenging tasks coupled with the collective knowledge of a group, and the payoff, an individual’s own benefit in terms of new knowledge acquired. The final cooperation rate is only affected by the cost of cooperation in the case of simple tasks and small communities. In contrast, the fitness of the community, the difficulty of the task, and the groups sizes interact in a non-trivial way, hence shedding some light on how to improve crowdsourcing when the cost of cooperation is high.

Suggested Citation

  • Andrea Guazzini & Mirko Duradoni & Alessandro Lazzeri & Giorgio Gronchi, 2018. "Simulating the Cost of Cooperation: A Recipe for Collaborative Problem-Solving," Future Internet, MDPI, vol. 10(6), pages 1-17, June.
  • Handle: RePEc:gam:jftint:v:10:y:2018:i:6:p:55-:d:153175
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    References listed on IDEAS

    as
    1. Yan Ma & Zhenjiang Shen & Dinh Thanh Nguyen, 2016. "Agent-Based Simulation to Inform Planning Strategies for Welfare Facilities for the Elderly: Day Care Center Development in a Japanese City," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-5.
    2. Timothy Gowers & Michael Nielsen, 2009. "Massively collaborative mathematics," Nature, Nature, vol. 461(7266), pages 879-881, October.
    3. Galam, Serge, 2004. "Contrarian deterministic effects on opinion dynamics: “the hung elections scenario”," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 453-460.
    4. Valérie Chanal & Marie-Laurence Caron-Fasan, 2008. "How to invent a new business model based on crowdsourcing: the Crowdspirit ® case," Post-Print halshs-00370761, HAL.
    5. Elizabeth Hunter & Brian Mac Namee & John D. Kelleher, 2017. "A Taxonomy for Agent-Based Models in Human Infectious Disease Epidemiology," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(3), pages 1-2.
    6. Ernst Fehr & Simon Gächter, 2002. "Altruistic punishment in humans," Nature, Nature, vol. 415(6868), pages 137-140, January.
    7. Manfred Milinski & Dirk Semmann & Hans-Jürgen Krambeck, 2002. "Reputation helps solve the ‘tragedy of the commons’," Nature, Nature, vol. 415(6870), pages 424-426, January.
    8. Serge Galam, 2008. "Sociophysics: A Review Of Galam Models," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 409-440.
    9. M.-L. Caron-Fasan & V. Chanal, 2008. "How to invent a new business model based on crowdsourcing: the Crowdspiritcase," Post-Print halshs-00376837, HAL.
    10. Martin A. Nowak & Karl Sigmund, 1998. "Evolution of indirect reciprocity by image scoring," Nature, Nature, vol. 393(6685), pages 573-577, June.
    11. Christoph Engel & Lilia Zhurakhovska, 2016. "When is the risk of cooperation worth taking? The prisoner’s dilemma as a game of multiple motives," Applied Economics Letters, Taylor & Francis Journals, vol. 23(16), pages 1157-1161, November.
    12. Valerio Capraro & Hélène Barcelo, 2015. "Group Size Effect on Cooperation in One-Shot Social Dilemmas II: Curvilinear Effect," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-11, July.
    13. M.A. Nowak & K. Sigmund, 1998. "Evolution of Indirect Reciprocity by Image Scoring/ The Dynamics of Indirect Reciprocity," Working Papers ir98040, International Institute for Applied Systems Analysis.
    14. Valérie Chanal & Marie-Laurence Caron-Fasan, 2008. "How to invent a new business model based on crowdsourcing : the Crowdspirit ® case," Post-Print halshs-00486794, HAL.
    15. David G. Rand & Alexander Peysakhovich & Gordon T. Kraft-Todd & George E. Newman & Owen Wurzbacher & Martin A. Nowak & Joshua D. Greene, 2014. "Social heuristics shape intuitive cooperation," Nature Communications, Nature, vol. 5(1), pages 1-12, May.
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    Cited by:

    1. Camillo Donati & Andrea Guazzini & Giorgio Gronchi & Andrea Smorti, 2019. "About Linda Again: How Narratives and Group Reasoning Can Influence Conjunction Fallacy," Future Internet, MDPI, vol. 11(10), pages 1-14, October.
    2. Dongwei Guo & Mengmeng Fu & Hai Li, 2021. "Cooperation in Social Dilemmas: A Group Game Model with Double-Layer Networks," Future Internet, MDPI, vol. 13(2), pages 1-27, January.
    3. Peijie Jiang & Xiaomeng Ruan & Zirong Feng & Yanyun Jiang & Bin Xiong, 2023. "Research on Online Collaborative Problem-Solving in the Last 10 Years: Current Status, Hotspots, and Outlook—A Knowledge Graph Analysis Based on CiteSpace," Mathematics, MDPI, vol. 11(10), pages 1-20, May.

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