Revenue vs. Welfare: A Comprehensive Analysis of Strategic Trade-offs in Online Food Delivery Systems
Yukun Zhang, Qi Dong
TL;DR
The paper tackles the tension between rapid GMV-driven growth and long-term social welfare in online food delivery platforms. It develops static and dynamic models that either maximize GMV or maximize total welfare, and then validates these frameworks with multi-agent simulations. Key findings show that GMV-focused policies deliver short-term gains but often undermine long-term stability and stakeholder satisfaction, while welfare-centric or hybrid approaches yield higher and more robust long-term welfare and participation. The work provides actionable insights for platform operators and policymakers, advocating dynamic, welfare-conscious strategies to harmonize growth with sustainability in platform ecosystems.
Abstract
This paper investigates the trade-off between short-term revenue generation and long-term social welfare optimization in online food delivery platforms. We first develop a static model that captures the equilibrium interactions among restaurants, consumers, and delivery workers, using Gross Merchandise Value (GMV) as a proxy for immediate performance. Building on this, we extend our analysis to a dynamic model that integrates evolving state variables,such as platform reputation and participant retention-to capture long-term behavior. By applying dynamic programming techniques, we derive optimal strategies that balance GMV maximization with social welfare enhancement. Extensive multi-agent simulations validate our theoretical predictions, demonstrating that while a GMV-focused approach yields strong initial gains, it ultimately undermines long-term stability. In contrast, a social welfare-oriented strategy produces more sustainable and robust outcomes. Our findings provide actionable insights for platform operators and policymakers seeking to harmonize rapid growth with long-term