Consider the dispatch of ridesharing trips to drivers lining up at airports, or the allocation of deceased donor organs to patients in transplant wait lists. In such dynamic queueing games, heterogeneity in short-lived items combined with agents' discretion to decline induce substantial cherrypicking, resulting in high rates of unfulfilled trips and discarded organs. Existing research typically focuses on easy-to-analyze dispatch policies, sometimes making fluid assumptions for tractability. In this work, we introduce a best-response-based solver for general dispatch policies with closed-form updates, which computes agents' equilibrium acceptance, entry, and reneging strategies as functions of queue length and queue position. For a family of dispatch policies where dispatch probabilities are queue-length independent and items are offered monotonically down the queue, we prove that our solver converges to a Markov perfect equilibrium in a finite number of iterations and that such an equilibrium always exists. For more general dispatch policies, we characterize their equilibria, and the solver converges in simulation despite the lack of theoretical guarantees. Via extensive numerical results, we show that the solver recovers known equilibria for policies that can be analyzed theoretically, and provides insights (beyond what can be gleaned from previous analysis) into more complex policies better suited for practice.

We study an all-pay contest where players with low abilities are filtered prior to the round of competing for prizes. These are often practiced due to limited resources or to enhance the competitiveness of the contest. We consider a setting where the designer admits a certain number of top players into the contest. The players admitted into the contest update their beliefs about their opponents based on the signal that their abilities are among the top. We find that their posterior beliefs, even with IID priors, are correlated and depend on players' private abilities, representing a unique feature of this game. We explicitly characterize the symmetric and unique Bayesian equilibrium strategy and compare it with a contest that admits all players. We also discuss a two-stage extension where players with top first-stage efforts can proceed to the second stage competing for prizes.

The goal of shared rides is to transport compatible riders together in a carpool-type service, thereby reducing vehicle miles and emissions. However, platforms such as Uber and Lyft have long struggled to maintain a healthy shared rides product. Low prices are needed to attract riders and achieve enough density for efficient matching, but are risky for platforms. We analyze this tension in status-quo pricing policies used by platforms, and propose a new pricing policy that encourages matches and improves efficiency.

Online service platforms have transformed how customers and suppliers connect in real-time, using centralized dispatch and pricing systems. However, by acting as "central planners", platforms risk undermining the workers' flexibility endorsed by the gig economy. Hence, there has been significant scrutiny on the classification of gig workers as independent contractors and their freedom in decisions that directly influence their earnings, such as prices. To alleviate such concerns, several platforms in the ride-hailing industry have adopted or tested decentralized pricing schemes, where workers set prices flexibly. However, this approach presents a complex trade-off. On the one hand, platforms' pricing systems enable an efficient matching process by balancing demand and supply. Individual suppliers' pricing decisions may overlook market-wide effects on supply-demand equilibrium. On the other hand, suppliers possess private information about their preferences and costs that platforms cannot easily infer and use for price discrimination. Decentralized pricing can accommodate supplier-side heterogeneity, potentially increasing workers' participation in the market.

We study the matching of jobs to workers waiting in a queue, for example a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in earnings from different trips incentivizes drivers to cherrypick, increasing riders' waiting times for a match, and resulting in poor reliability for riders, low average earnings for drivers, and a loss of throughput and revenue for the platform. Simple fixes by limiting dispatching transparency or drivers' flexibility are neither desirable nor fully effective. Optimal origin-destination based prices are incentive aligned in theory, but are hard to implement in practice due to operational constraints.