Emergence of cooperative behaviors in incomplete information networks: Evolutionary dynamics based on higher-order interactions and adaptive strategies

Addressing the dual challenges of information incompleteness and interaction heterogeneity, this study develops a novel modeling framework for higher-order interactions in networks with incomplete information, elucidating their co-evolutionary effects on collective dynamics. In real-world social systems, individual interaction networks are often constrained by information asymmetry, which leads to dynamic phenomena such as the emergence of cooperation and opinion polarization that deviate significantly from the predictions of traditional complete-information models. The binary interaction paradigm, predominant in existing research, inadequately captures the heterogeneity in individual information transmission. To this end, this study first constructs a cooperative evolution model under incomplete information networks. By introducing an information acquisition capability gradient and adaptive strategy update rules, it reveals the non-linear influence of micro-decision differences on macro-cooperation patterns. Secondly, a higher-order interaction framework with limited information is designed, and the synergistic effect between network topology and information dissemination efficiency is quantitatively analyzed. It is found that group heterogeneity will significantly change the equilibrium path of the game. Finally, multi-scenario simulations are carried out in the Prisoner’s Dilemma Game (PDG) and Snowdrift Game (SG) models. The results demonstrate that higher-order interactions can effectively enhance the rate of evolutionary stable cooperation. While such interactions may induce short-term fluctuations in overall cooperation levels, the implementation of information filtering mechanisms proves capable of fostering cross-group collaboration. Furthermore, our analysis reveals that the higher-order network structure plays a decisive role in determining the evolutionary bifurcation thresholds across different social dilemmas. This study offers a novel theoretical framework for social intervention policies, including rumor governance and public resource allocation. The model framework can not only be used to simulate group behavior under different information availability and higher-order interaction structures, but also provide a reference for optimizing social intervention strategies.