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M. Keith Chen

dblp:50/7876 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none

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%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 50% Computational social science and digital humanities · 50%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design
dynamic pricing
0.212016
Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform · EC 2016
Algorithmic game theory and mechanism design › dynamic pricing
surge pricing
0.212016
Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform · EC 2016
Computational social science and digital humanities › future of work
gig economy
0.112016
Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform · EC 2016
Computational finance and economics
labor market
0.112016
Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform · EC 2016
Algorithmic game theory and mechanism design › market design
two-sided market
0.112016
Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform · EC 2016

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

market design · 0.5dynamic pricing · 0.5
YearPublicationVenuePosition
2016 Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform
abstract
In many markets, new technologies allow traditional jobs to be divided into discrete tasks that are widely distributed across workers and dynamically priced given prevailing supply and demand conditions. This "sharing" or "gig" economy represents a more flexible work system, and is most common in two-sided markets in which a firm acts as a platform to connect service providers and consumers. One prominent example of this is the ride-sharing company Uber, which connects riders and driver-partners, and dynamically prices trips using a system known as "surge" pricing. In this talk, I discuss the practical problems of designing such a dynamic pricing system, how that dynamic pricing coordinates workers who can now earn compensation on a flexible schedule, and more broadly how the "gig" economy is evolving and growing as a form of market organization.
M. Keith Chen
EC1