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    Area of Science:

    • Evolutionary Game Theory
    • Network Science
    • Computational Social Science

    Background:

    • Traditional evolutionary game theory (EGT) assumes static, well-mixed populations.
    • Real-world systems feature agents dynamically forming/breaking connections based on incentives.
    • Existing models often overlook the impact of strategic network formation on evolutionary dynamics.

    Purpose of the Study:

    • To introduce a novel framework integrating EGT with strategic network formation.
    • To model the co-evolution of strategies and intercommunity links.
    • To analyze how strategic connections influence collective behavior in various games.

    Main Methods:

    • Developed a strategic connections model (SCM) based on pairwise stability.
    • SCM models two-stage optimization for mutual incentives and payoff improvements.
    • Applied the framework to prisoner's dilemma, rock-paper-scissors, and congestion games.

    Main Results:

    • Intercommunity connections significantly reshape population-level outcomes.
    • Cooperation emerges and persists in prisoner's dilemma under specific conditions.
    • Rock-paper-scissors dynamics are altered, potentially suppressing characteristic cycles.
    • Congestion games show improved resource allocation and infrastructure usage.

    Conclusions:

    • Strategic intercommunity links are critical in shaping collective behavior.
    • The framework provides insights into inter/intracommunity dynamics in diverse systems.
    • Highlights the importance of considering strategic network formation in evolutionary models.