We study a gig economy platform's problem of finding optimal compensation schemes when faced with workers who myopically base their participation decisions on limited information with respect to their earnings. The stylized model we consider captures two key, related features absent from prior work on the operations of on-demand service platforms: (i) workers' lack of information regarding the distribution from which their earnings are drawn and (ii) worker decisions that are sensitive to variability in earnings. Despite its stylized nature, our model induces a complex stochastic optimization problem whose natural fluid relaxation is also a priori intractable. Nevertheless, we uncover a surprising structural property of the relaxation that allows us to design a tractable, fast-converging heuristic policy that is asymptotically optimal amongst the space of all policies that fulfill a fairness property. In doing so, via both theory and extensive simulations, we uncover phenomena that may arise when earnings are volatile and hard to predict, as both the empirical literature and our own data-driven observations suggest may be prevalent on gig economy platforms.
翻译:我们研究一个吉祥经济平台在面对工人时寻找最佳补偿计划的问题。 这些工人在参与决定时没有根据有关其收入的有限信息。我们认为,标准化模型捕捉了在按需服务平台运作前工作中缺少的两个关键相关特征:(一) 工人缺乏有关其收入来源分布的信息,以及(二) 工人决定敏感于收入差异性的决定。尽管其结构化性质,我们的模型却引发了一个复杂的随机优化问题,而自然流体放松也是先天难以解决的。然而,我们发现了一种令人惊奇的放松结构特性,使我们得以设计出一种可移植的、快速趋同的超常政策,该政策在满足公平财产的所有政策空间中具有同样的最佳性。在这样做时,我们通过理论和广泛的模拟,发现了当收入不稳定和难以预测时可能出现的现象,经验文献和我们自己的数据驱动的观察都表明,这些现象可能出现在工作经济平台上。