000161031 001__ 161031
000161031 005__ 20251017144553.0
000161031 0247_ $$2doi$$a10.1016/j.chaos.2025.116552
000161031 0248_ $$2sideral$$a144239
000161031 037__ $$aART-2025-144239
000161031 041__ $$aeng
000161031 100__ $$aSun, Chengbin
000161031 245__ $$aCo-evolution of cooperation and resource allocation in the advantageous environment-based spatial multi-game using adaptive control
000161031 260__ $$c2025
000161031 5060_ $$aAccess copy available to the general public$$fUnrestricted
000161031 5203_ $$aIn real-life complex systems, individuals often encounter multiple social dilemmas that cannot be effectively captured using a single-game model. Furthermore, the environment and limited resources both play a crucial role in shaping individuals’ decision-making behaviors. In this study, we employ an adaptive control mechanism by which agents may benefit from their environment, thus redefining their individual fitness. Under this setting, a detailed examination of the co-evolution of individual strategies and resource allocation is carried. Through extensive simulations, we find that the advantageous environment mechanism not only significantly increases the proportion of cooperators in the system but also influences the resource distribution among individuals. Additionally, limited resources reinforce cooperative behaviors within the system while shaping the evolutionary dynamics and strategic interactions across different dilemmas. Once the system reaches equilibrium, resource distribution becomes highly imbalanced. To promote fairer resource allocation, we introduce a minimum resource guarantee mechanism. Our results show that this mechanism not only reduces disparities in resource distribution across the entire system and among individuals in different dilemmas but also significantly enhances cooperative behavior in higher resource intervals. Finally, to assess the robustness of our model, we further examine the influence of the advantageous environment on system-wide cooperation in small-world and random graph network models.
000161031 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es
000161031 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000161031 700__ $$ade Miguel-Arribas, Alfonso
000161031 700__ $$aWang, Chaoqian
000161031 700__ $$aXia, Haoxiang
000161031 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000161031 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000161031 773__ $$g199 (2025), 116552 [18 pp.]$$pChaos, solitons fractals$$tChaos, Solitons and Fractals$$x0960-0779
000161031 8564_ $$s7384876$$uhttps://zaguan.unizar.es/record/161031/files/texto_completo.pdf$$yVersión publicada
000161031 8564_ $$s2653177$$uhttps://zaguan.unizar.es/record/161031/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000161031 909CO $$ooai:zaguan.unizar.es:161031$$particulos$$pdriver
000161031 951__ $$a2025-10-17-14:12:28
000161031 980__ $$aARTICLE