Resource pooling improves system efficiency drastically in large stochastic systems, but its effective implementation in decentralized systems remains relatively underexplored. In this paper, we study the problem of incentivizing resourcing pooling when agents are self-interested, and their states are private information. Specifically, we study a standard multi-server queueing model in which each server is associated with an M/M/1 queue and aims to minimize its time-average job holding and processing costs. Our primary motivation is applications in the design of decentralized computing markets (potentially implemented on blockchains), among others.

We revisit the popular random matching market model introduced by Knuth (1976) and Pittel (1989), and shown by Ashlagi, Kanoria and Leshno (2013) to exhibit a “stark effect of competition”; in particular, with any difference in the number of agents on the two sides (“imbalance”), the short side agents obtain substantially better outcomes. We generalize the model to allow “partially connected” markets with each agent having an average degree d in a random (undirected) graph. Each agent has a (uniformly random) preference ranking over only their neighbors in the graph. We characterize stable matchings in large markets and find that the short side enjoys a significant advantage only for d exceeding log2 n where n is the number of agents on one side: For moderately connected markets with d = o(log2 n), we find that there is no advantage to being on the short side (for O(n1–∊) market imbalance), with agents on both sides getting a -ranked partner on average. Notably, this “mild competition” regime extends far beyond the connectivity threshold of d = Θ(log n). In contrast, for densely connected markets with d = ω(log2 n), we find a strong effect of competition, namely, short side agents get a log n-ranked partner on average, while the long side agents get a partner of (much larger) rank d/log n on average. Our results and analysis suggest that in general matching markets, being on the short side confers an advantage if and only if the number of short-side agents who remain unmatched is small relative to the market imbalance.

Waiting lists are common mechanisms for allocating scarce items without monetary transfers. Examples include the allocation of cadaver organs to patients in need of a transplant, public housing apartments to applicants, health care services to patients, and even spots at childcare centers to parents. In all these markets waiting times play the role of prices in guiding the allocation and rationing items. But while prices are set by the designer, waiting times are endogenously determined by the number of agents waiting. Moreover, waiting times are not fixed, and continuously adjust as items arrive or agents join. When agents and items arrive stochastically over time, waiting times stochastically adjust over time.

We study the problem of maximizing payoff generated over a period of time in a general class of closed queueing networks with finite, fixed number of supply units which circulate in the system. Demand arrives stochastically, and serving a demand unit (customer) causes a supply unit to relocate from the "origin" to the "destination" of the customer. We consider general controls including entry control, pricing, and assignment. Motivating applications include shared transportation platforms and scrip systems.