121 research outputs found
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
EUROPEAN CONFERENCE ON QUEUEING THEORY 2016
International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"
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