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Demo Data from EDBT 2026 / reviewers in the wild / expert

Daniel Peretz

dblp:340/3908 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0000-4737-645XORCID · reported

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

Artificial intelligence and machine learning · 1 · 1 since 2021Theory of computation · 1 · 1 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
1 paper
Algorithmic game theory and mechanism design · 100%
Artificial intelligence
1 paper
Trustworthy machine learning · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › mechanism design
contract theory		 0.712023
Ambiguous Contracts · EC 2023
Algorithmic game theory and mechanism design › mechanism design › contract theory
moral hazard		 0.712023
Ambiguous Contracts · EC 2023
Algorithmic game theory and mechanism design › mechanism design › contract theory
principal-agent problem		 0.712023
Ambiguous Contracts · EC 2023
Machine learning › Trustworthy machine learning
uncertainty modeling		 0.212023
Ambiguous Contracts · EC 2023

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

game theory · 1.3contract theory · 1.3
YearPublicationVenuePosition
2023 Ambiguous Contracts
abstract
In this paper we introduce a model of ambiguous contracts, capturing many real-life scenarios where agents engage in contractual relations that leave some degree of uncertainty. In this paper we introduce a model of ambiguous contracts, capturing many real-life scenarios where agents engage in contractual relations that leave some degree of uncertainty. Our starting point is the celebrated hidden-action model and the classic notion of a contract, where the principal commits to an outcome-contingent payment scheme for incentivizing an agent to take a costly action.
Paul Dütting, Michal Feldman, Daniel Peretz
 EC3