A Dynamic Framework for Optimizing Reward Policies in the Sharing Economy

This study introduces a dynamic framework for optimizing reward policies in the sharing economy, leveraging game theory to enhance user participation, improve resource utilization, and sustain platform growth. By analyzing strategic interactions among participants, the research employs game-theoretic models, including Nash equilibrium, to optimize resource allocation and ensure equitable outcomes. The proposed framework addresses the limitations of static reward models by developing dynamic reward policies that adapt to real-time data and evolving user behaviors. Using a dynamic programming algorithm, the study formulates the reward allocation problem as an optimization challenge, recursively selecting users to incentivize in a way that maximizes platform efficiency and profitability within budget constraints. Scenario-based analyses validate the framework’s effectiveness, demonstrating its ability to adapt to varying market conditions and user behaviors. The integration of game theory ensures that reward policies are not only effective in driving engagement but also fair and scalable, offering actionable strategies for designers and policymakers to unlock the full potential of sharing economy ecosystems.