Dynamic Learning in Large Matching Markets

Anand Kalvit · Assaf Zeevi

Hall J #1036

Keywords: [ Algorithms ] [ Multi-armed Bandits ] [ infinitely many arms ] [ regret ] [ matching ] [ learning ] [ explore-then-commit ]


We study a sequential matching problem faced by "large" centralized platforms where "jobs" must be matched to "workers" subject to uncertainty about worker skill proficiencies. Jobs arrive at discrete times with "job-types" observable upon arrival. To capture the "choice overload" phenomenon, we posit an unlimited supply of workers where each worker is characterized by a vector of attributes (aka "worker-types") drawn from an underlying population-level distribution. The distribution as well as mean payoffs for possible worker-job type-pairs are unobservables and the platform's goal is to sequentially match incoming jobs to workers in a way that maximizes its cumulative payoffs over the planning horizon. We establish lower bounds on the "regret" of any matching algorithm in this setting and propose a novel rate-optimal learning algorithm that adapts to aforementioned primitives "online." Our learning guarantees highlight a distinctive characteristic of the problem: achievable performance only has a "second-order" dependence on worker-type distributions; we believe this finding may be of interest more broadly.

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