How to promote the trade of health data: an evolutionary game theory-based analysis

Purpose Effective health data trading depends on active engagement from multiple stakeholders. This study uses evolutionary game theory combined with prospect theory to model interactions among key stakeholders and to examine the dynamics of strategy evolution and the factors influencing stakeholder participation. Design/methodology/approach A four-player evolutionary game model is developed to capture the behaviors of data providers, data demanders, individuals and the government. Numerical simulations are conducted to validate the theoretical results and assess the effects of key parameters. Findings The analysis shows that health data trading progresses through distinct stages, each associated with different stable strategy combinations. Government incentives and penalties strongly shape the behaviors of providers, demanders and individuals, although excessive subsidies can weaken regulatory commitment. Data providers are more sensitive to costs and payments than demanders, reflecting their dominant position. Individual perceptions of value, cost and privacy risk are central to sustaining data sharing. Dynamic adjustment of government rewards and penalties is essential for encouraging active participation across market stages and for enabling a gradual reduction in direct intervention. Originality/value This study offers an integrated behavioral and policy perspective on health data trading and advances understanding of health data monetization. It provides actionable insights for designing balanced incentive–regulation mechanisms that build trust, support sustainable market development and inform policymaking for responsible data trading.