Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Takumi Yoshikawa

dblp:266/5723 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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.

Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › learning in games
best-response dynamics
0.412020
A Simple, Fast, and Safe Mediator for Congestion Management · AAAI 2020
Algorithmic game theory and mechanism design
equilibrium computation
0.412020
A Simple, Fast, and Safe Mediator for Congestion Management · AAAI 2020
Algorithmic game theory and mechanism design
mechanism design
0.412020
A Simple, Fast, and Safe Mediator for Congestion Management · AAAI 2020
Algorithmic game theory and mechanism design › mechanism design › implementation theory
mediator design
0.412020
A Simple, Fast, and Safe Mediator for Congestion Management · AAAI 2020
Algorithmic game theory and mechanism design
congestion games
0.112020
A Simple, Fast, and Safe Mediator for Congestion Management · AAAI 2020

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

ex-post nash equilibrium · 0.4best response dynamics · 0.4
YearPublicationVenuePosition
2020 A Simple, Fast, and Safe Mediator for Congestion Management
abstract
Congestion is a severe problem in cities. A large population with little information about each other's preferences hardly reaches equilibrium and causes unexpected congestion. Controlling such congestion requires us to collect information dispersed in the market and to coordinate actions among agents. We aim to design a mediator that a) induces a game with high social welfare in equilibrium, b) computes an equilibrium efficiently, c) works without common prior, and d) performs well even when only some of the agents in the market use the mediator. We propose a mediator based on a version of best response dynamics (BRD). We prove that, in a simple setting with two resources, “good behavior” (reporting truthfully and following the recommendation) forms an (approximate) ex-post Nash equilibrium in the mediated game; in the equilibrium, the welfare is close to the first-best when preferences diverge enough. Furthermore, under a certain behavioral assumption, those who are not using the mediator can always enjoy non-negative payoff gain by joining it even without the full participation of others. Additionally, our experimental results suggest that such results remain valid for more general settings.
Kei Ikegami, Kyohei Okumura, Takumi Yoshikawa
AAAI3