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Etan A. Green

dblp:243/5518 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · unresolved

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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design
auction theory
0.612022
The Science of the Deal: Optimal Bargaining on eBay Using Deep Reinforcement Learning · EC 2022
Algorithmic game theory and mechanism design › cooperative game theory
bargaining
0.612022
The Science of the Deal: Optimal Bargaining on eBay Using Deep Reinforcement Learning · EC 2022

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

deep reinforcement learning · 0.6
YearPublicationVenuePosition
2022 The Science of the Deal: Optimal Bargaining on eBay Using Deep Reinforcement Learning
abstract
Bargaining is ubiquitous. How can people bargain better? We train a reinforcement learning agent to bargain optimally in "Best Offer" listings on eBay, and we characterize its behavior in a manner that humans can use. As a buyer, the agent starts lower than human buyers and bargains longer. As the seller, the agent interprets offers as signals---of the buyer's willingness to pay and of the item's desirability---that human sellers ignore. Simple strategies derived from these agents purchase more items for lower prices than human buyers and sell more items for higher prices than human sellers.
Etan A. Green, E. Barry Plunkett
EC1