EDBT 2026 Demo / reviewers in the wild / expert
Sumit Goel
dblp:268/8300
· DBLP profile ↗
3ranked-venue papers
2as first author
3since 2021 · last 2024
0000-0003-3266-9035ORCID · 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 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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
2 papers |
Algorithmic game theory and mechanism design · 100% |
Topics — the 9 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design
mechanism design |
1.4 | 2 | 2024 | Optimality of Weighted Contracts for Multi-agent Contract Design with a Budget · EC 2024 Prizes and effort in contests with private information · EC 2023 |
Algorithmic game theory and mechanism design › mechanism design › algorithmic mechanism design
budget-feasible mechanism |
0.8 | 1 | 2024 | Optimality of Weighted Contracts for Multi-agent Contract Design with a Budget · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design
contract design |
0.8 | 1 | 2024 | Optimality of Weighted Contracts for Multi-agent Contract Design with a Budget · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design › contract design
multi-agent contracts |
0.8 | 1 | 2024 | Optimality of Weighted Contracts for Multi-agent Contract Design with a Budget · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design
contest design |
0.7 | 1 | 2023 | Prizes and effort in contests with private information · EC 2023 |
Algorithmic game theory and mechanism design › mechanism design › contract theory
effort elicitation |
0.7 | 1 | 2023 | Prizes and effort in contests with private information · EC 2023 |
Algorithmic game theory and mechanism design › mechanism design
private information |
0.7 | 1 | 2023 | Prizes and effort in contests with private information · EC 2023 |
Algorithmic game theory and mechanism design › resource allocation
prize allocation |
0.7 | 1 | 2023 | Prizes and effort in contests with private information · EC 2023 |
Algorithmic game theory and mechanism design › mechanism design
information asymmetry |
0.2 | 1 | 2023 | Prizes and effort in contests with private information · EC 2023 |
Methods — techniques the papers use, named apart from their topics
mechanism design · 0.7contest theory · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Optimality of Weighted Contracts for Multi-agent Contract Design with a BudgetabstractWe study a contract design problem between a principal and multiple agents. Each agent participates in an independent task with binary outcomes (success or failure), in which she may exert costly effort towards improving her probability of success, and the principal has a fixed budget which it can use to provide outcome-dependent rewards to the agents. Crucially, each agent's reward may depend not only on whether she succeeds or fails, but also on whether other agents succeed or fail, and we assume the principal cares only about maximizing the agents' probabilities of success, not how much of the budget it expends. A motivating example might be that of a sales manager who is endowed with a fixed budget by the firm and is tasked with incentivizing the salespeople to successfully close sales. The manager can observe whether each salesperson was able to sell the product or not, but now how much effort the salesperson exerted towards making the sale. Wade Hann-Caruthers, Sumit Goel |
EC | 2 |
| 2023 | Prizes and effort in contests with private informationabstractContests are situations in which agents compete with one another by exerting costly effort to win valuable prizes. In this paper, we consider contest environments where participants have private information about their ability and the contest designer can manipulate the values of different prizes to influence the effort exerted by them. In such environments, with the goal of understanding how different contests compare in terms of the effort they induce, we study the effect of two different interventions on effort: Sumit Goel |
EC | 1 |
| 2022 | Project Selection with Partially Verifiable Information
Sumit Goel, Wade Hann-Caruthers |
WINE | 1 |