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Selection strategy for data trading models based on dynamic evolutionary game

HAO Jun1,2, MU Mengdi2, LI Jianping2   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China;
    2 School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
  • Received:2025-08-01 Revised:2026-01-22 Online:2026-01-23

Abstract: Addressing the imbalance between on-exchange data trading and off-exchange circulation, this study investigates the synergistic effects of platform guidance strategies and governmental incentive-penalty mechanisms. A quadripartite evolutionary game model-comprising data suppliers, data users, trading platforms, and governmental entities-is constructed to analyze strategic response mechanisms and equilibrium formation, complemented by behavioral evolution simulations using data exchange and policy records. Key findings include: 1) Government policy orientation and regulatory intensity encourage platforms to adopt proactive guidance strategies, strengthening their influence on trading parties and increasing on-exchange transactions. Rising incentive costs and fiscal pressure drive government intervention toward a coordinated regime combining penalties with lower-intensity incentives. 2) Optimal strategy portfolios (platform proactivity, on-exchange participation, sustained high-regulation) emerge only when fiscal subsidies, platform incentives, and penalty thresholds collectively surpass behavioral response thresholds. 3) Differential incentives enhance platform guidance, penalties support compliance, and fiscal compensation helps suppliers internalize compliance costs.

Key words: data transaction, on-exchange trading, off-exchange circulation, evolutionary game, mode selection

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