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  4. Information design in service systems and online markets

Information design in service systems and online markets

File(s)
Lingenbrink_cornellgrad_0058F_11470.pdf (1.65 MB)
Permanent Link(s)
https://doi.org/10.7298/qn3k-1920
https://hdl.handle.net/1813/67405
Collections
Cornell Theses and Dissertations
Author
Lingenbrink, David Alan
Abstract

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.

Date Issued
2019-05-30
Keywords
Bayesian Persuasion
•
Information design
•
Operations research
•
Optimization
•
Applied mathematics
•
Computer science
•
queueing theory
•
Linear Programming
Committee Chair
Iyer, Krishnamurthy
Committee Member
Kleinberg, Robert David
Banerjee, Siddhartha
Degree Discipline
Operations Research and Information Engineering
Degree Name
Ph.D., Operations Research and Information Engineering
Degree Level
Doctor of Philosophy
Type
dissertation or thesis

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