Research

Efficient allocation of a divisible resource and its associated costs among consumers is a critical issue in many societal decision-making scenarios. This decision is especially difficult when consumers have heterogeneous incomes and private levels of resource utility. Common approaches in practice often overlook either utility heterogeneity or income disparity, leaving a significant gap between potential and actual outcomes. We develop and analyze a resource allocation method to bridge this gap. Our model incorporates consumer heterogeneity in both income levels and private utility from the resource. We formulate the problem of allocating he resource and its associated costs or savings as a mechanism design problem, aiming to maximize aggregate consumer welfare. We propose a mechanism that offers consumers income-dependent menus (IDM) of quantity and cost (or savings) options, and uncover structural properties of these menus. Our IDM approach significantly outperforms the considered alternatives in a numerical study calibrated using real-world data. In the realm of resource allocation where both income and resource utility levels are heterogeneous, significant welfare gains can be realized by judiciously leveraging the dual dimensions of heterogeneity. Implementing our IDM approach ensures that consumers receive and contribute to resources in a manner that reflects their financial capacities and utility levels, maximizing overall welfare.

Utility regulators are grappling to devise compensation schemes for customers who sell rooftop solar generation back to the grid. Regulators seek to induce an optimal level of rooftop solar adoption, trading off between its environmental benefits and the financial burden that it imposes, while simultaneously safeguarding the interests of utilities, solar system installers, and customers. This is a difficult balance to achieve, and numerous failed attempts have received considerable press coverage in the last three years. For example, tariff changes in Nevada induced SolarCity, the market leader in solar systems, to suspend operations in the state. Motivated by this, we formulate and analyze a social welfare maximization problem for the regulator, focusing on how the choice of tariff interacts with her competing objectives. We uncover the structural properties of a successful tariff, finding that the tariff structures used in most states are inadequate: to achieve welfare optimal outcomes, a tariff must be able to discriminate among customer usage tiers and between customers with and without rooftop solar. We also demonstrate that these flexibilities do not imply more degrees of freedom for the regulator.  Finally, we present a tariff structure with these two characteristics and show how it can be implemented as a simple buy-all, sell-all tariff while retaining its favorable properties. We illustrate our findings numerically using household-level data from Nevada and New Mexico.

Internet-based technology enables firms to disseminate real-time delay information to delay-sensitive customers. We study how such delay announcements impact service providers in a competitive environment with two service providers who compete for market share. We model the service providers' strategies based on an endogenous timing game, investigating strategies that emerge in equilibrium. We determine the service providers' market shares under the various game outcomes by analyzing continuous-time Markov chains, which capture customers' joining decisions, and by developing a novel computational technique to analyze the intractable asymmetric Join-the-Shortest Queue system, providing bounds on the market shares. We find that only the lower capacity service provider announces its real-time delay under intermediate system loads and highly imbalanced capacities. However, for most parameter settings, the mere presence of a competitor induces both providers to announce delays in equilibrium, leaving customers better off on average. We relate our findings to the single-provider delay announcement literature by discussing the impact of competition on service providers, delay announcement technology firms, and customers 

We study delay information disclosure policies in on-demand platforms, modeled as two-sided queues, with two user classes --- customers and providers --- who seek matches to each other using the platform. The primary objective is maximizing the rate at which these matches occur by adopting one of three information regimes: the occupancy regime (disclosing the current system occupancy to both user categories) or two distinct asymmetric regimes (sharing no information with one user class while sharing occupancy information with the other). The users of each class are strategic and decide whether to join based on the available delay information. We use continuous-time Markov chains to model the system as a two-sided queue and employ equilibrium analysis to characterize the users' joining behavior and the platform's match rate under each information regime. The optimal policy reveals a complex dependence on system parameters and is strongly influenced by the users' patience profiles. We demonstrate analytically that it is strategically advantageous to withhold delay information from one user class, especially when it consists of a substantial number of relatively delay-insensitive users. The optimality of the asymmetric information-sharing regimes becomes more prevalent as the discrepancy in the patience profiles or the market sizes of the two user classes increases. However, our extensive numerical analyses find that the occupancy regime proves to be optimal in many other settings. In cases where it falls short of optimality, the sub-optimality gap is usually minimal (on average, about 5%). Our findings hold crucial implications for platform managers, indicating that in such two-sided systems, the occupancy regime is a safe choice unless the two user classes exhibit a large patience profile discrepancy or a large market size imbalance. In such cases, opting for the occupancy information regime could adversely impact the platform's match rate. In such situations, carefully evaluating the chosen information-sharing strategy becomes imperative.