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David Lingenbrink

dblp:167/5339 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0001-6214-9941ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorTheory of computation · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › congestion games
queueing games
0.312017
Optimal Signaling Mechanisms in Unobservable Queues with Strategic Customers · EC 2017

Methods — techniques the papers use, named apart from their topics

signaling mechanism · 0.3revenue optimization · 0.3bayesian updating · 0.3
YearPublicationVenuePosition
2019 Persuading Risk-Conscious Agents: A Geometric Approach
Jerry Anunrojwong, Krishnamurthy Iyer, David Lingenbrink
WINE3
2017 Optimal Signaling Mechanisms in Unobservable Queues with Strategic Customers
abstract
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?
David Lingenbrink, Krishnamurthy Iyer
EC1