Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Jeffrey Mensch

dblp:264/0683 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
2since 2021 · last 2024
0000-0002-5804-7923ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Theory of computation · 2 · 1 first-author · 2 since 2021

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
2 papers
Algorithmic game theory and mechanism design · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design
mechanism design		 1.522024
Monopoly, Product Quality, and Flexible Learning · EC 2024
Posterior-Mean Separable Costs of Information Acquisition · EC 2024
Algorithmic game theory and mechanism design › decision theory
information acquisition		 0.812024
Posterior-Mean Separable Costs of Information Acquisition · EC 2024
Algorithmic game theory and mechanism design › mechanism design
information design		 0.812024
Monopoly, Product Quality, and Flexible Learning · EC 2024
Algorithmic game theory and mechanism design › pricing
monopoly pricing		 0.812024
Monopoly, Product Quality, and Flexible Learning · EC 2024
Algorithmic game theory and mechanism design › decision theory
revealed preference		 0.812024
Posterior-Mean Separable Costs of Information Acquisition · EC 2024

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

stochastic choice · 0.8posterior-mean separable costs · 0.8mechanism design · 0.8information design · 0.8
YearPublicationVenuePosition
2024 Posterior-Mean Separable Costs of Information Acquisition
abstract
We analyze a problem of revealed preference given state-dependent stochastic choice data in which the payoff to a decision maker (DM) only depends on their beliefs about posterior means. Often, the DM must also learn about or pay attention to the state; in applied work on this subject, a convenient assumption is that the costs of such learning are linearly dependent in the distribution over posterior means:
Jeffrey Mensch, Komal Malik
 EC1
2024 Monopoly, Product Quality, and Flexible Learning
abstract
A seller offers a buyer a schedule of transfers and associated product qualities. After observing this schedule, the buyer chooses a flexible costly signal about his type. We show it is without loss to focus on a class of mechanisms that compensate the buyer for his learning costs. Focusing attention on these mechanisms allows one to employ techniques from both the information design and mechanism design toolkits.
Doron Ravid, Jeffrey Mensch
 EC2