Christopher P. Chambers

dblp:58/9615 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › mechanism design
dynamic mechanism design
0.212014
Dynamically eliciting unobservable information · EC 2014
Algorithmic game theory and mechanism design › mechanism design
information elicitation
0.212014
Dynamically eliciting unobservable information · EC 2014
Algorithmic game theory and mechanism design › mechanism design › information elicitation
proper scoring rules
0.212014
Dynamically eliciting unobservable information · EC 2014

Methods — techniques the papers use, named apart from their topics

menu design · 0.2backward induction · 0.2
YearPublicationVenuePosition
2014 Dynamically eliciting unobservable information
abstract
We 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
EC1