EDBT 2026 Demo / reviewers in the wild / expert
Peter Caradonna
dblp:392/7793
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2025
0000-0002-4197-4739ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Theory of computation · 2 · 2 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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design
decision theory |
1.6 | 2 | 2025 | Identifying Restrictions on the Random Utility Model · EC 2025 Preference Regression · EC 2024 |
Algorithmic game theory and mechanism design › decision theory
random utility model |
0.9 | 1 | 2025 | Identifying Restrictions on the Random Utility Model · EC 2025 |
Algorithmic game theory and mechanism design › decision theory
revealed preference |
0.8 | 1 | 2024 | Preference Regression · EC 2024 |
Methods — techniques the papers use, named apart from their topics
observational equivalence · 0.9mass preserving swaps · 0.9regression · 0.8axiomatic analysis · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Identifying Restrictions on the Random Utility ModelabstractWe study identifying assumptions in the random utility model, the standard empirical paradigm in modern economics. Our main result characterizes those ex-ante restrictions which lead to identification. Our characterization utilizes a simple class of mass preserving swaps. These swaps take in two preferences which share a common upper and lower contour set but disagree on their ordering within these two sets. The output of this procedure is two preferences which are created by swapping the matching between the ordering of the upper and lower contour sets of the two input preferences. Any two distributions over preferences are observationally equivalent if and only if one can be recovered from the other by a finite sequences of such swaps. It follows that a random utility model is identified if it does not contain any two such distributions over preferences. Peter Caradonna, Christopher Turansick |
EC | 1 |
| 2024 | Preference RegressionabstractDating back to at least [Samuelson, 1938], the study of the testable implications of models of individual preference and decision making has occupied a central position within both empirical and theoretical economics. In behavioral and decision theory, experimental falsification, or the discovery of 'paradoxes' documenting widespread empirical inconsistency with respect to various axioms, has been a long-standing driver of progress.1 Related puzzles in macroeconomics and finance (e.g. the equity premium puzzle, see Mehra and Prescott 1985) have similarly led to the creation of new theories of individual behavior (Constantinides 1990, Epstein and Zin 1989). Peter Caradonna |
EC | 1 |