Demo Data from EDBT 2026 / reviewers in the wild / expert
Igor Douven
dblp:90/4384
· DBLP profile ↗
5ranked-venue papers
5as first author
3since 2021 · last 2026
0000-0003-3413-080XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 4 first-author · 3 since 2021Theory of computation · 1 · 1 first-author
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.
| Artificial intelligence
2 papers |
Multi-agent systems · 70% Optimization for machine learning · 30% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems
opinion dynamics |
0.5 | 1 | 2021 | Mis- and disinformation in a bounded confidence model · Artif. Intell. 2021 |
Computational social science and digital humanities › misinformation
misinformation spread |
0.5 | 1 | 2021 | Mis- and disinformation in a bounded confidence model · Artif. Intell. 2021 |
Machine learning › Optimization for machine learning
evolutionary computation |
0.4 | 1 | 2019 | Optimizing group learning: An evolutionary computing approach · Artif. Intell. 2019 |
Knowledge, reasoning and agents › Multi-agent systems › multi-agent learning
group learning |
0.4 | 1 | 2019 | Optimizing group learning: An evolutionary computing approach · Artif. Intell. 2019 |
Methods — techniques the papers use, named apart from their topics
bounded-confidence model · 1.0agent-based modeling · 1.0optimization · 0.4evolutionary computing · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Predicting Lockean from gradational accuracyabstract• No single scoring rule best predicts Lockean accuracy across all belief thresholds. • The Spherical score is optimal for lower thresholds; the Power3 rule is best for higher thresholds. • The widely used Brier and log scores rarely perform best for predicting categorical accuracy. • Most scoring rules work by rewarding calibration and sharpness, but some track additional components. • Empirical methods can complement a priori arguments in evaluating scoring rules. The debate about which scoring rule best measures the accuracy of our credences has largely been conducted on an a priori basis. We pursue an empirical approach, asking which rule best predicts a practical, decision-relevant criterion: Lockean accuracy, the ability to make correct categorical judgments based on a threshold of belief. Analyzing a large dataset of probability judgments, we compare the most widely used scoring rules (Brier, logarithmic, spherical, absolute error, and power rules) and find that, among them, there is no single best one. Instead, the optimal choice is context dependent: the Spherical score is the best predictor for lower belief thresholds, while the Power 3 rule is best at higher thresholds. In particular, the widely used Brier and log scores are rarely optimal for this task. A mediation analysis reveals that while much of a rule’s success is explained by its ability to reward calibration and sharpness, the Spherical and Brier rules retain significant predictive power independently of these standard virtues. Igor Douven, Nikolaus Kriegeskorte |
Int. J. Approx. Reason. | 1 |
| 2021 | Mis- and disinformation in a bounded confidence model
Igor Douven, Rainer Hegselmann |
Artif. Intell. | 1 |
| 2021 | Fuzzy concept combination: An empirical study
Igor Douven |
Fuzzy Sets Syst. | 1 |
| 2019 | Optimizing group learning: An evolutionary computing approach
Igor Douven |
Artif. Intell. | 1 |
| 2018 | From probabilities to categorical beliefs: Going beyond toy modelsabstractAccording to the Lockean thesis (LT), a proposition is believed just in case it is highly probable. While this thesis enjoys strong intuitive support, it is known to conflict with seemingly plausible logical constraints on our beliefs. One way out of this conflict is to make probability 1 a requirement for belief, but most have rejected this option for entailing what they see as an untenable skepticism. Recently, two new solutions to the conflict have been proposed that are alleged to be non-skeptical. We compare these proposals with each other and with the LT, in particular with regard to the question of how much we gain by adopting any one of them instead of the probability 1 requirement, that is of how likely it is that one believes more than the things one is fully certain of. Igor Douven, Hans Rott |
J. Log. Comput. | 1 |