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March 3, 2026npj Complexity1 citationsOpen Access

Symmetry breaking in collective decision-making through higher-order interactions

DMDavid March-PonsRPRomualdo Pastor-SatorrasMMM. Carmen Miguel

Key Points

  • Higher-order interactions are critical for breaking symmetry in decision-making systems with equally valid options.
  • Simulations show that pairwise communication can lead to decision deadlock without group interactions to assist consensus.
  • The model introduces uncommitted agents that explore options and get influenced by others to commit to preferred choices.
  • These insights into social interactions may inform the design of more effective decision protocols for artificial systems.

Abstract

Abstract Collective decision-making is a widespread phenomenon in both biological and artificial systems, where individuals reach a consensus through social interactions. While traditional models of opinion dynamics and contagion focus on pairwise interactions, recent research emphasizes the importance of including higher-order group interactions and autonomous behavior to better reflect real-world complexity. In this work, we introduce a collective decision-making model inspired by social insects. In our framework, uncommitted agents can explore options independently and become committed, while social interactions influence these agents to prefer options already accepted by the group. Our model extends classical contagion models by incorporating multiple, mutually exclusive options and distinguishing between pairwise and higher-order social influences. Using simulations and analytical mean-field solutions, we show that higher-order interactions are essential for breaking symmetry in systems with equally valid options. We find that pairwise communication alone can cause decision deadlock, but adding group interactions allows the system to overcome stalemates and reach consensus. Our results emphasize the important roles of autonomous behavior and higher-order structures in collective decision-making. These insights could help us better understand social systems and design decision protocols for artificial swarms.

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Cite This Study

March-Pons et al. (2026) studied this question.

synapsesocial.com/papers/69a75b0fc6e9836116a21adehttps://doi.org/10.1038/s44260-026-00071-5
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