按需平台中的顺序报价：贪心排序的最优性
Sequential Offering in On-Demand Platforms: On the Optimality of Greedy Ranking
中文总结 AI 辅助
研究按需平台顺序报价问题，证明在保留工资分布满足非递增凸密度时贪心排序最优，任意分布下达到先知基准紧致比例，建议平台坚持贪心排序并优化工资。
中文摘要 AI 辅助
按需平台面临一个基本挑战：如何用可能拒绝报价的独立工人来完成时间敏感的工作。为了减少延误和未完成的工作，平台经常在每次拒绝后依次提高提供的工资。然而，这些动态价格调整与接触工人的具体顺序之间的相互作用一直被忽视。特别是，如果最合适的工人（例如，离工作地点最近的工人）也在序列中排名最早，那么这些工人将看到最低的提供工资并可能拒绝，导致较不合适的工人最终看到提高的工资并接受工作的糟糕系统结果。我们研究顺序报价问题，通过联合优化工人的排序和定价轨迹来最大化预期福利或平台利润。令人惊讶的是，我们的主要结果表明，如果保留工资分布具有非递增且凸的密度函数（例如，均匀分布、指数分布），则福利通过贪心排序和通过反向归纳优化的工资来实现最大化。对于任意分布，我们证明贪心排序达到了先知基准的紧致$n/(2n - 1)$比例。在超出分布假设的设置中的数值结果发现，福利损失远低于通用保证所允许的损失，即使在贪心被证明是次优的族中也是如此。这表明，与其向较差的匹配发送初始“低价”报价，平台应坚持贪心排序，并通过适当考虑下游报价的延续价值来优化工资报价。
英文摘要
On-demand platforms face the fundamental challenge of fulfilling time-sensitive jobs with independent workers who may decline offers. To minimize delays and unfulfilled jobs, platforms frequently raise the offered wage sequentially following each rejection. However, the interaction between these dynamic price adjustments and the specific sequence in which workers are approached has been overlooked. In particular, if the best-suited workers (e.g., closest to the job) are also ranked earliest in the sequence, then those workers would see the lowest offered wages and may decline, leading to poor system outcomes where less-suited workers end up seeing the raised wages and accepting the job. We study the sequential offering problem to maximize expected welfare or platform profit by jointly optimizing the ranking of workers and the pricing trajectory. Surprisingly, our main result establishes that if the reservation wage distribution exhibits a non-increasing and convex density function (e.g., Uniform, Exponential), welfare is maximized by greedy ranking and wages optimized via backward induction. For arbitrary distributions, we prove that greedy ranking achieves a tight $n/(2n - 1)$ fraction of the prophet benchmark. Numerical results for settings beyond the distributional assumptions find welfare losses well below those allowed by the universal guarantee, even in families where greedy is provably suboptimal. This suggests that rather than sending initial "low ball'' offers to worse matches, platforms should stick with greedy ranking and optimize the wage offerings by appropriately taking the continuation value of the downstream offers into consideration.
