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<p>This study investigates the mechanisms and evolutionary dynamics of cross-border collaborative innovation under the Greater Eurasian Partnership framework, focusing on the strategic interactions among governments, Russian enterprises, and partner-country enterprises. While collaborative innovation between Russian enterprises, host governments, and enterprises in partner countries holds significant potential for enhancing Russia’s innovation capacity, advancing scientific frontiers, and fostering socioeconomic development, the interplay of multi-agent coordination—particularly the roles of governments, Russian enterprises, and partner-country enterprises—remains inadequately understood. This knowledge gap impedes the full realization of the GEP’s innovation potential. To address this, we develop a tripartite evolutionary game model involving governments, Russian enterprises, and partner-country enterprises. We integrate asymptotically stable equilibrium (ASE) analysis based on replicator dynamics and numerical simulations to elucidate the systemic dynamics and key determinants of collaborative innovation. The principal findings are as follows: (1) The decision-making behaviors of all three agents are interdependently influenced, exhibiting complex strategic interactions; (2) The system converges to an equilibrium state of active governmental support and bilateral enterprise collaboration when the payoffs of non-collaborative strategies fall below the joint returns from collaborative innovation, and governmental subsidies for collaboration outweigh the costs of passive engagement; (3) Increasing penalties for non-collaborative behaviors, enhancing risk-sharing mechanisms, and amplifying technological spillover effects significantly elevate the probability of collaborative strategy adoption. Theoretically, this study develops an evolutionary game-based analytical framework for understanding multi-agent collaborative innovation in transnational contexts. It proposes policy recommendations—including dynamic incentive-penalty mechanisms, risk-payoff alignment protocols, and cross-border intellectual property coordination—to inform the improvement of collaborative innovation governance within the Greater Eurasian Partnership framework.</p>

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Evolutionary game modeling of collaborative innovation: the case of the Greater Eurasian Partnership

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月27日

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  • YuHan Mei,
  • Xiangying Ma,
  • Liang Zhao

摘要

This study investigates the mechanisms and evolutionary dynamics of cross-border collaborative innovation under the Greater Eurasian Partnership framework, focusing on the strategic interactions among governments, Russian enterprises, and partner-country enterprises. While collaborative innovation between Russian enterprises, host governments, and enterprises in partner countries holds significant potential for enhancing Russia’s innovation capacity, advancing scientific frontiers, and fostering socioeconomic development, the interplay of multi-agent coordination—particularly the roles of governments, Russian enterprises, and partner-country enterprises—remains inadequately understood. This knowledge gap impedes the full realization of the GEP’s innovation potential. To address this, we develop a tripartite evolutionary game model involving governments, Russian enterprises, and partner-country enterprises. We integrate asymptotically stable equilibrium (ASE) analysis based on replicator dynamics and numerical simulations to elucidate the systemic dynamics and key determinants of collaborative innovation. The principal findings are as follows: (1) The decision-making behaviors of all three agents are interdependently influenced, exhibiting complex strategic interactions; (2) The system converges to an equilibrium state of active governmental support and bilateral enterprise collaboration when the payoffs of non-collaborative strategies fall below the joint returns from collaborative innovation, and governmental subsidies for collaboration outweigh the costs of passive engagement; (3) Increasing penalties for non-collaborative behaviors, enhancing risk-sharing mechanisms, and amplifying technological spillover effects significantly elevate the probability of collaborative strategy adoption. Theoretically, this study develops an evolutionary game-based analytical framework for understanding multi-agent collaborative innovation in transnational contexts. It proposes policy recommendations—including dynamic incentive-penalty mechanisms, risk-payoff alignment protocols, and cross-border intellectual property coordination—to inform the improvement of collaborative innovation governance within the Greater Eurasian Partnership framework.