VLDB 2026 Research / reviewers in the wild / expert
Christopher P. Chambers
dblp:58/9615
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · unresolved
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 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 › mechanism design
dynamic mechanism design |
0.2 | 1 | 2014 | Dynamically eliciting unobservable information · EC 2014 |
Algorithmic game theory and mechanism design › mechanism design
information elicitation |
0.2 | 1 | 2014 | Dynamically eliciting unobservable information · EC 2014 |
Algorithmic game theory and mechanism design › mechanism design › information elicitation
proper scoring rules |
0.2 | 1 | 2014 | Dynamically eliciting unobservable information · EC 2014 |
Methods — techniques the papers use, named apart from their topics
menu design · 0.2backward induction · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Dynamically eliciting unobservable informationabstractWe answer the following question: At $t=1$, an expert has (probabilistic) information about a random outcome X. In addition, the expert will obtain further information about $X$ as time passes, up to some time t=T+1 at which X will be publicly revealed. (How) Can a protocol be devised that induces the expert, as a strict best response, to reveal at the outset his prior assessment of both X and the information flows he anticipates and, subsequently, what information he privately receives' (The protocol can provide the expert with payoffs that depend only on the realization of X, as well as any decisions he may take.) We show that this can be done with the following sort of protocol: At the penultimate time t=T, the expert chooses a payoff function from a menu of such functions, where the menu available to him was chosen by him at time $t=T-1$ from a menu of such menus, and so forth. We show that any protocol that affirmatively answers our question can be approximated by a protocol of the form described. We show how these results can be extended from discrete time to continuous time problems of this sort. Christopher P. Chambers, Nicolas S. Lambert |
EC | 1 |