EDBT 2026 Demo / reviewers in the wild / expert
Oren Kislev
dblp:221/2836
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Theory of computation · 1
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational finance and economics · 87% Smart cities and intelligent transportation · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational finance and economics › pricing
dynamic pricing |
0.3 | 1 | 2018 | Surge Pricing Moves Uber's Driver-Partners · EC 2018 |
Computational finance and economics
market design |
0.3 | 1 | 2018 | Surge Pricing Moves Uber's Driver-Partners · EC 2018 |
Smart cities and intelligent transportation
driver behavior analysis |
0.1 | 1 | 2018 | Surge Pricing Moves Uber's Driver-Partners · EC 2018 |
Methods — techniques the papers use, named apart from their topics
natural experiment · 0.3difference-in-differences · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Surge Pricing Moves Uber's Driver-PartnersabstractWe study the impact of dynamic pricing (so-called "surge pricing") on relocation decisions by Uber's driver-partners and the corresponding revenue they collected. Using a natural experiment arising from an outage in the system that produces the surge pricing heatmap for a portion of Uber's driver-partners over 10 major cities, and a difference-in-differences approach, we study the short-run effect that visibility of the surge heatmap has on 1) drivers' decisions to relocate to areas with higher or lower prices and 2) drivers' revenue. We demonstrate that the ability to see the surge heatmap has a statistically significant impact on both outcomes. Ability to see the surge heatmap explains 10%-60% of Uber drivers' self-positioning decisions, attracts drivers toward areas with higher surge prices, and increases drivers' revenue on surged trips by up to 70%. This suggests that dynamic pricing helps drivers move to where riders' demand is largest, and that the resulting reduction in spatial search friction and spatial mismatch improves waiting times and welfare for both riders and drivers. Alice Lu, Peter I. Frazier, Oren Kislev |
EC | 3 |