Demo Data from SIGIR 2026 / reviewers in the wild / expert
Andrew Bongjune Choi
dblp:351/0990
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0009-0001-5014-5297ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Theory of computation · 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
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 › mechanism design
commitment |
0.7 | 1 | 2023 | I'll Tell You Tomorrow: Committing to Future Commitments · EC 2023 |
Algorithmic game theory and mechanism design › mechanism design
dynamic mechanism design |
0.7 | 1 | 2023 | I'll Tell You Tomorrow: Committing to Future Commitments · EC 2023 |
Algorithmic game theory and mechanism design
mechanism design |
0.7 | 1 | 2023 | I'll Tell You Tomorrow: Committing to Future Commitments · EC 2023 |
Algorithmic game theory and mechanism design › mechanism design › information design
information disclosure |
0.2 | 1 | 2023 | I'll Tell You Tomorrow: Committing to Future Commitments · EC 2023 |
Algorithmic game theory and mechanism design › mechanism design › contract theory
principal-agent problem |
0.2 | 1 | 2023 | I'll Tell You Tomorrow: Committing to Future Commitments · EC 2023 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | I'll Tell You Tomorrow: Committing to Future CommitmentsabstractThis paper studies a principal who commits to inform an agent about what the principal will eventually do. The principal wishes to promote the agent if and only if the state is good, and he gradually receives private information about the state. The agent always wishes to be promoted, but faces a decreasing outside option and would rather leave if she expects not to be promoted. The principal optimally induces the agent to stay by committing to commit, that is, by committing today to tell the agent tomorrow about her chances of promotion the day after. When the agent has a high initial outside option, with some probability, the principal promotes the agent regardless of his information---even if he realizes early that the state will turn out to be bad. The principal may ask the agent to stay until he fully observes the state, only to deny her promotion; this does not necessarily mean that the principal leads the agent on. We apply our results to worker retention, relationship-specific investment, and forward guidance. Andrew Bongjune Choi |
EC | 1 |