Motivated by the recent popularity of machine learning training services, we introduce a contract design problem in which a provider sells a service that results in an outcome of uncertain quality for the buyer. The seller has a set of actions that lead to different distributions over outcomes. We focus on a setting in which the seller has the ability to commit to an action and the buyer is free to accept or reject the outcome after seeing its realized quality. Our model is related to Mussa and Rosen's classic paper on selling products of differing qualities and monopolist lottery pricing, as well as recent work on selling hidden actions.

In many settings, resources are allocated among agents repeatedly over time without the use of monetary transfers: consider, for example, allocating server-time to company employees, rooms to students, or food among food banks. Here, the central challenge is to allocate resources efficiently despite the absence of payments. In this work we study a simple online variant of the standard Fisher market, where we endow all agents with a budget of artificial credits, and then repeatedly run simultaneous first-price auctions for each item in each period. Owing to their simplicity, such mechanisms have been gaining in popularity, with several recent successful implementations, most notably, by Feeding America for US food banks. Our goal in this paper is to understand the incentive and efficiency properties of these mechanisms.

We study a repeated persuasion setting between a sender and a receiver, where at each time t, the sender shares information about a payoff-relevant state with the receiver. The state at each time t is drawn independently and identically from an unknown distribution, and subsequent to receiving information about it, the receiver (myopically) chooses an action from a finite set. The sender seeks to persuade the receiver into choosing actions that are aligned with her preference by selectively sharing information about the state. In contrast to the standard persuasion setting, we focus on the case where neither the sender nor the receiver knows the distribution of the payoff relevant state. Instead, the sender learns this distribution over time by observing the state realizations. We adopt the assumption common in the literature on Bayesian persuasion that at each time period, prior to observing the realized state in that period, the sender commits to a signaling mechanism that maps each state to a possibly random action recommendation. Subsequent to the state observation, the sender recommends an action as per the chosen signaling mechanism.

Social services often face the challenge of congestion due to their limited capacity relative to their demand. The congestion partly stems from the inclusionary intent of such services: a toll-free road is available to everyone, even those able to afford alternative tolled ones. A broad range of low- and middle-income households are eligible to apply for public housing. How can a social service provider reduce congestion and thus the efficiency loss associated with service delay? In this context, the two controls commonly used for managing congestion, pricing and centralized admission control, are inapplicable due to fairness and implementation considerations. However, the service provider may have control over the information about the system state that it shares with the users. Local traffic managers and public housing authorities have accurate information about the level of congestion for their corresponding services. As such, the service provider can leverage this informational advantage to persuade some of those with lower needs to forgo the service and reduce congestion in the system. In this paper, we study how effective such an informational lever is.

Non-monetary mechanisms for repeated resource allocation are gaining widespread use in many real-world settings. Our aim in this work is to study the allocative efficiency and incentive properties of simple repeated mechanisms based on artificial currencies. Within this framework, we make three main contributions:

We study the problem of optimal information sharing in the context of a service system. In particular, we consider an unobservable single server queue offering a service at a fixed price to a Poisson arrival of delay-sensitive customers. The service provider can observe the queue, and may share information about the state of the queue with each arriving customer. The customers are Bayesian and strategic, and incorporate any information provided by the service provider into their prior beliefs about the queue length before making the decision whether to join the queue or leave without obtaining service. We pose the following question: which signaling mechanism and what price should the service provider select to maximize her revenue?

We consider a model of nomadic agents exploring and competing for time-varying location-specific resources, arising in crowdsourced transportation services, online communities, and in traditional location-based economic activity. This model comprises a group of agents, and a set of locations each endowed with a dynamic stochastic resource process. Each agent derives a periodic reward determined by the overall resource level at her location, and the number of other agents there. Each agent is strategic and free to move between locations, and at each time decides whether to stay at the same node or switch to another one. We study the equilibrium behavior of the agents as a function of dynamics of the stochastic resource process and the nature of the externality each agent imposes on others at the same location. In the asymptotic limit with the number of agents and locations increasing proportionally, we show that an equilibrium exists and has a threshold structure, where each agent decides to switch to a different location based only on their current location's resource level and the number of other agents at that location. This result provides insight into how system structure affects the agents' collective ability to explore their domain to find and effectively utilize resource-rich areas. It also allows assessing the impact of changing the reward structure through penalties or subsidies.

In many markets, uncertainty about whether a trade is executed can be removed by paying a price premium. We use financial markets as a particular setting in which to study this trade-off. In particular, we assess the role of information in the choice between certain trade at a price premium in an intermediated dealer market and contingent trade in a dark pool. Our setting consists of intrinsic traders and speculators, each endowed with heterogeneous fine-grained private information as to an asset's value, that endogenously decide between these two venues. We solve for an equilibrium in this setting, and address three main questions: First, how does the level of information of a trader and her competitors affect their behavior-i.e., how does the choice between certain and contingent trade depend on information structure? Second, how does the level of premium for certain trade over contingent trade affect the strategic behavior of traders? And finally, how should market makers intermediating certain trade set transaction costs to maximize profit, in the presence of an option for contingent trade? We derive the following implications from our model:

No abstract available.

Recent years have seen extensive investigation of the information aggregation properties of prediction markets. However, relatively little is known about conditions under which a market will aggregate the private information of rational risk averse traders who optimize their portfolios over time; in particular, what features of a market encourage traders to ultimately reveal their private information through trades? We consider a market model involving finitely many informed risk-averse traders interacting with a market maker. Our main result identifies a basic asymptotic smoothness condition on the price in the market that ensures information will be aggregated under a portfolio convergence assumption. Asymptotic smoothness is fairly mild: it requires that, eventually, infinitesimal purchases or sales should see the same per unit price. Notably, we demonstrate that, under some mild conditions, cost function market makers (or, equivalently, market makers based on market scoring rules) satisfy the asymptotic smoothness requirement.