EDBT 2026 Demo / reviewers in the wild / expert
Konstantinos I. Stouras
dblp:269/4947
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0002-2398-9566ORCID · reported
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-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.
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › solution concepts in games › equilibrium concepts
bayes-nash equilibrium |
0.4 | 1 | 2020 | Prizes on Crowdsourcing Platforms: An Equilibrium Analysis of Competing Contests · EC 2020 |
Algorithmic game theory and mechanism design › mechanism design
contest design |
0.4 | 1 | 2020 | Prizes on Crowdsourcing Platforms: An Equilibrium Analysis of Competing Contests · EC 2020 |
Algorithmic game theory and mechanism design › incentive mechanism
crowdsourcing contests |
0.4 | 1 | 2020 | Prizes on Crowdsourcing Platforms: An Equilibrium Analysis of Competing Contests · EC 2020 |
Algorithmic game theory and mechanism design
equilibrium analysis |
0.4 | 1 | 2020 | Prizes on Crowdsourcing Platforms: An Equilibrium Analysis of Competing Contests · EC 2020 |
Algorithmic game theory and mechanism design › resource allocation
prize allocation |
0.4 | 1 | 2020 | Prizes on Crowdsourcing Platforms: An Equilibrium Analysis of Competing Contests · EC 2020 |
Methods — techniques the papers use, named apart from their topics
game theory · 0.4equilibrium analysis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Prizes on Crowdsourcing Platforms: An Equilibrium Analysis of Competing ContestsabstractOn a typical crowdsourcing platform solvers can self-select which (if any) of the concurrently running contests to participate in. Thus, firms which offer prizes and organize contests on these platforms are competing among themselves (for solver participation and effort). We formalize and model this competition among contests and examine the equilibrium outcomes. Our analysis reveals that, in general, there is a unique dominant strategy for each firm to offer multiple identical prizes. Moreover, when the quality of submitted solutions is sufficiently noise-driven (as opposed to effort-driven), we find that a single winner-take-all reward is the unique equilibrium allocation. Our analytical framework integrates and extends prior results of the monopolistic contest. Konstantinos I. Stouras, Sanjiv Erat, Kenneth C. Lichtendahl Jr. |
EC | 1 |