Information Design for Congested Social Services: Optimal Need-Based Persuasion

We study the effectiveness of information design in reducing congestion in social services catering to users with varied levels of need. In the absence of price discrimination and centralized admission, the provider relies on sharing information about wait times to improve welfare. We consider a stylized model with heterogeneous users who differ in their private outside options: low-need users have an acceptable outside option to the social service, whereas high-need users have no viable outside option. Upon arrival, a user decides to wait for the service by joining an unobservable first-come-first-serve queue, or leave and seek her outside option. To reduce congestion and improve social outcomes, the service provider seeks to persuade more low-need users to avail their outside option, and thus better serve high-need users. We characterize the Pareto-optimal signaling mechanisms and compare their welfare outcomes against several benchmarks. We show that if either type is the overwhelming majority of the population, information design does not provide improvement over sharing full information or no information. On the other hand, when the population is a mixture of the two types, information design not only Pareto dominates full-information and no-information mechanisms, in some regimes it also achieves the same welfare as the "first-best", i.e., the Pareto-optimal centralized admission policy with knowledge of users' types.Comment: Accepted for publication in the 21st ACM Conference on Economics and Computation (EC'20). 40 pages, 6 figure

Information design in service systems and online markets

In mechanism design, the firm has an advantage over its customers in its knowledge of the state of the system, which can affect the utilities of all players. This poses the question: how can the firm utilize that information (and not additional financial incentives) to persuade customers to take actions that lead to higher revenue (or other firm utility)? When the firm is constrained to ``cheap talk,'' and cannot credibly commit to a manner of signaling, the firm cannot change customer behavior in a meaningful way. Instead, we allow firm to commit to how they will signal in advance. Customers can then trust the signals they receive and act on their realization. This thesis contains the work of three papers, each of which applies information design to service systems and online markets. We begin by examining how a firm could signal a queue's length to arriving, impatient customers in a service system. We show that the choice of an optimal signaling mechanism can be written as a infinite linear program and then show an intuitive form for its optimal solution. We show that with the optimal fixed price and optimal signaling, a firm can generate the same revenue as it could with an observable queue and length-dependent variable prices. Next, we study demand and inventory signaling in online markets: customers make strategic purchasing decisions, knowing the price will decrease if an item does not sell out. The firm aims to convince customers to buy now at a higher price. We show that the optimal signaling mechanism is public, and sends all customers the same information. Finally, we consider customers whose ex ante utility is not simply their expected ex post utility, but instead a function of its distribution. We bound the number of signals needed for the firm to generate their optimal utility and provide a convex program reduction of the firm's problem

Persuading Risk-Conscious Agents: A Geometric Approach

We consider a persuasion problem between a sender and a receiver whose utility may be nonlinear in her belief; we call such receivers risk-conscious. Such utility models arise when the receiver exhibits systematic biases away from expected-utility-maximization, such as uncertainty aversion (e.g., from sensitivity to the variance of the waiting time for a service). Due to this nonlinearity, the standard approach to finding the optimal persuasion mechanism using revelation principle fails. To overcome this difficulty, we use the underlying geometry of the problem to develop a convex optimization framework to find the optimal persuasion mechanism. We define the notion of full persuasion and use our framework to characterize conditions under which full persuasion can be achieved. We use our approach to study binary persuasion, where the receiver has two actions and the sender strictly prefers one of them at every state. Under a convexity assumption, we show that the binary persuasion problem reduces to a linear program, and establish a canonical set of signals where each signal either reveals the state or induces in the receiver uncertainty between two states. Finally, we discuss the broader applicability of our methods to more general contexts, and illustrate our methodology by studying information sharing of waiting times in service systems.Comment: Accepted at Operations Research. Appeared as an extended abstract in The 15th Conference on Web and Internet Economics (WINE 2019

Queuing up for justice : elections and case backlogs

We analyze the impact of prosecutor elections on case backlogs. Previous evidence has shown that re-election pressures result in more cases going to trial. Since trials require time and resources, one can expect an effect on the queue. Two competing theories are developed: one of signaling quality in an asymmetric information environment and one of effort exertion, each of which can explain increased trials before election, but differ in their predictions regarding the impact on backlogs. A district-level, panel data set of caseload flows in North Carolina is analyzed. Evidence is presented that contested re-elections are associated with a decrease in the number of cases handled and an acceleration of the growth of the backlog. This suggests that retention concerns lead to signaling which causes distortions, re-allocating resources from disposing cases to prosecuting cases at trial

Public Economics and Startup Entrepreneurs

This paper surveys the various forms of market failure that can arise when innovating entrepreneurs consider entering an industry, and outlines possible implications for public policy. Externalities can arise from entrepreneurial activities such as spillover benefits from new innovations and spillover costs on incumbent firms. New entrepreneurs can also face various barriers to entry, either natural ones or those created by incumbent firms or government policy. They may also face problems in obtaining credit at efficient terms if there are information asymmetries in markets for either loan or equity finance. Finally, asymmetric information may also plague new firms in the hiring of workers. These various inefficiencies are of contradictory sign, some calling for an incentive to startup firms and other for the opposite. Thus, public policy is ambiguous and depends on the circumstances at hand.

Externalities in Economies with Imperfect Information and Incomplete Markets

This paper presents a simple, general framework for analyzing externalities in economies with incomplete markets and imperfect information. By identifying the pecuniary effects of these externalities that net out, the paper simplifies the problem of determining when tax interventions are Pareto improving. The approach indicates that such tax interventions almost always exist and that equilibria in situations of imperfect information are rarely constrained Pareto optima. It can also lead to simple tests, based on readily observable indicators of the efficacy of particular tax policies in situations involving adverse selection, signaling, moral hazard, incomplete contingent claims markets, and queue rationing equilibria