Evolutionary Game Analysis of Medical Waste Disposal in China under Different Reward and Penalty Models

Evolutionary Game—Theoretic Approach for Analyzing User Privacy Disclosure Behavior in Online Health Communities

Privacy disclosure is one of the most common user information behaviors in online health communities. Under the premise of implementing privacy protection strategies in online health communities, promoting user privacy disclosure behavior can result in a “win–win” scenario for users and online health communities. Combining the real situation and evolutionary game theory, in this study, we first constructed an evolutionary game model of privacy disclosure behavior with users and online health communities as the main participants. Then, we solved the replication dynamic equations for both parties and analyzed the evolutionary stable strategies (ESSs) in different scenarios. Finally, we adopted MATLAB for numerical simulations to verify the accuracy of the model. Studies show that: (1) factors such as medical service support and community rewards that users receive after disclosing their private personal information affect user game strategy; and (2) the additional costs of the online health communities implementing the “positive protection” strategy and the expected loss related to the privacy leakage risk affect the online health communities’ game strategy. In this regard, this paper puts forward the following suggestions in order to optimize the benefits of both sets of participants: the explicit benefits of users should be improved, the internal environment of the communities should be optimized, the additional costs of the “positive protection” strategy should be reduced, and penalties for privacy leakages should be increased.