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Cosmina Dorobantu

dblp:351/1569 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-7903-4796ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Theory of computation · 1 · 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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › mechanism design › auction design
ad auction
0.712023
Targeting versus Competition in Marketplace Design: Evidence from Geotargeted Internet Ads · EC 2023
Algorithmic game theory and mechanism design › auction theory
advertising auctions
0.712023
Targeting versus Competition in Marketplace Design: Evidence from Geotargeted Internet Ads · EC 2023
Algorithmic game theory and mechanism design › mechanism design
auction design
0.712023
Targeting versus Competition in Marketplace Design: Evidence from Geotargeted Internet Ads · EC 2023
Algorithmic game theory and mechanism design
market design
0.712023
Targeting versus Competition in Marketplace Design: Evidence from Geotargeted Internet Ads · EC 2023

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

natural experiment · 0.7difference-in-discontinuities · 0.7
YearPublicationVenuePosition
2023 Targeting versus Competition in Marketplace Design: Evidence from Geotargeted Internet Ads
abstract
How should market designers trade off targeting and competition? We study a natural experiment in the release of new targeting technology for online ads. A platform in our study introduced targeting into select geographic markets based on a discontinuity in local characteristics. We find that advertisers used new targeting to avoid low quality ad inventory. This led to a reduction in ad impressions. When advertisers avoided this inventory, they retreated into smaller, less competitive ad auctions featuring fewer bidders for available ad space. The reduction in competition lowered click prices in the treated areas. Nonetheless, the effects on platform revenue growth were positive. Better targeting improved the consumer experience of advertising. This led to higher consumer clickthrough rates, which raised the platform's revenue by increasing the quantity of clicks sold. The higher click volumes offset the revenue effects of the decrease in prices.
Bo Cowgill, Cosmina Dorobantu
EC2