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Group relations, resilience and the I Ching

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  • Schweitzer, Frank

Abstract

We evaluate the robustness and adaptivity of social groups with heterogeneous agents that are characterized by their binary state, their ability to change this state, their status and their preferred relations to other agents. To define group structures, we operationalize the hexagrams of the I Ching. The relations and properties of agents are used to quantify their influence according to the social impact theory. From these influence values we derive a weighted stability measure for triads involving three agents, which is based on the weighted balance theory. It allows to quantify the robustness of groups and to propose a novel measure for group resilience which combines robustness and adaptivity. A stochastic approach determines the probabilities to find robust and adaptive groups. The discussion focuses on the generalization of our approach.

Suggested Citation

  • Schweitzer, Frank, 2022. "Group relations, resilience and the I Ching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    • Handle: RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122004265
    DOI: 10.1016/j.physa.2022.127630
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    References listed on IDEAS

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    1. Frank Schweitzer & Tamas Krivachy & David Garcia, 2020. "An Agent-Based Model of Opinion Polarization Driven by Emotions," Complexity, Hindawi, vol. 2020, pages 1-11, April.
    2. Omid Askarisichani & Jacqueline Ng Lane & Francesco Bullo & Noah E. Friedkin & Ambuj K. Singh & Brian Uzzi, 2019. "Structural balance emerges and explains performance in risky decision-making," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. F. Schweitzer & L. Behera, 2009. "Nonlinear voter models: the transition from invasion to coexistence," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 301-318, February.
    4. Du, Haifeng & He, Xiaochen & Wang, Jingjing & Feldman, Marcus W., 2018. "Reversing structural balance in signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 780-792.
    5. Hołyst, Janusz A. & Kacperski, Krzysztof & Schweitzer, Frank, 2000. "Phase transitions in social impact models of opinion formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 285(1), pages 199-210.
    6. André Grow & Andreas Flache & Rafael Wittek, 2015. "An Agent-Based Model of Status Construction in Task Focused Groups," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-4.
    7. K. Kułakowski & M. Stojkow & D. Żuchowska-Skiba, 2020. "Heider balance, prejudices and size effect," The Journal of Mathematical Sociology, Taylor & Francis Journals, vol. 44(3), pages 129-137, July.
    8. Patrick Groeber & Frank Schweitzer & Kerstin Press, 2009. "How Groups Can Foster Consensus: The Case of Local Cultures," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(2), pages 1-4.
    9. Andres M Belaza & Kevin Hoefman & Jan Ryckebusch & Aaron Bramson & Milan van den Heuvel & Koen Schoors, 2017. "Statistical physics of balance theory," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
    10. Schweitzer, Frank, 2021. "Social percolation revisited: From 2d lattices to adaptive networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    11. Simon Schweighofer & Frank Schweitzer & David Garcia, 2020. "A Weighted Balance Model of Opinion Hyperpolarization," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(3), pages 1-5.
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    1. Ambra Amico & Luca Verginer & Giona Casiraghi & Giacomo Vaccario & Frank Schweitzer, 2023. "Adapting to Disruptions: Flexibility as a Pillar of Supply Chain Resilience," Papers 2304.05290, arXiv.org.

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