Earning Sans Learning: Noisy Decision-Making and Labor Supply on Gig Economy Platforms. (arXiv:2111.00002v1 [cs.GT])

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.

Source: https://arxiv.org/abs/2111.00002

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