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- Tim Roughgarden

Discipline name
D-index
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Citations
Publications
World Ranking
National Ranking

Engineering and Technology
D-index
56
Citations
16,042
140
World Ranking
904
National Ranking
390

2017 - Fellow of John Simon Guggenheim Memorial Foundation

2009 - ACM Grace Murray Hopper Award For his research combining computer science and game theory to analyze network routing among self-interested parties.

2006 - Fellow of Alfred P. Sloan Foundation

- Algorithm
- Artificial intelligence
- Computer network

Mathematical economics, Nash equilibrium, Mathematical optimization, Price of anarchy and Latency are his primary areas of study. His Mathematical economics study combines topics in areas such as Common value auction and Inefficiency. The various areas that Tim Roughgarden examines in his Nash equilibrium study include Network planning and design, Marginal cost, Game theory and Correlated equilibrium.

His research integrates issues of Function, Optimal mechanism, Scheduling and Probabilistic analysis of algorithms in his study of Mathematical optimization. In Price of anarchy, Tim Roughgarden works on issues like Outcome, which are connected to Hardness of approximation. His work deals with themes such as Network traffic control and Network performance, which intersect with Latency.

- How bad is selfish routing (1351 citations)
- Algorithmic Game Theory: Quantifying the Inefficiency of Equilibria (1177 citations)
- Algorithmic Game Theory: Computing in Games (1176 citations)

His scientific interests lie mostly in Mathematical economics, Mathematical optimization, Common value auction, Theoretical computer science and Nash equilibrium. His biological study spans a wide range of topics, including Computational complexity theory, Incentive and Price of anarchy. His Mathematical optimization research is multidisciplinary, incorporating elements of Network planning and design and Function.

His Common value auction study often links to related topics such as Mechanism design. His Nash equilibrium study incorporates themes from Correlated equilibrium, Shapley value and Latency. His research in Algorithmic game theory is mostly concerned with Algorithmic mechanism design.

- Mathematical economics (31.69%)
- Mathematical optimization (29.07%)
- Common value auction (16.28%)

- Mathematical economics (31.69%)
- Computational complexity theory (6.40%)
- Theoretical computer science (13.08%)

Tim Roughgarden spends much of his time researching Mathematical economics, Computational complexity theory, Theoretical computer science, Algorithm and Mathematical optimization. His work in the fields of Fair division overlaps with other areas such as Class. His studies deal with areas such as Game theory, Stochastic game and Algorithmic game theory as well as Computational complexity theory.

His Algorithmic game theory study combines topics from a wide range of disciplines, such as Revenue maximization and Auction theory. The Theoretical computer science study combines topics in areas such as Learnability and Robustness. The concepts of his Mathematical optimization study are interwoven with issues in Optimal mechanism and Rule of thumb.

- Almost envy-freeness with general valuations (63 citations)
- Beyond worst-case analysis (32 citations)
- Making the Most of Your Samples (27 citations)

- Algorithm
- Artificial intelligence
- Computer network

The scientist’s investigation covers issues in Mathematical economics, Monotone polygon, Submodular set function, Fair division and Algorithm. Specifically, his work in Mathematical economics is concerned with the study of Game theory. There are a combination of areas like Equilibrium point, Binary search algorithm, Optimization problem, Graph and Boolean function integrated together with his Monotone polygon study.

His research in Fair division intersects with topics in Discrete mathematics, Submodular function minimization and Pareto principle. His work carried out in the field of Algorithm brings together such families of science as Function and Maximization. His Class research spans across into areas like Communication complexity, Valuation, Polynomial, Proportionality and Discount points.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

How bad is selfish routing

Tim Roughgarden;Éva Tardos.

Journal of the ACM **(2002)**

2136 Citations

Algorithmic Game Theory: Computing in Games

Noam Nisan;Tim Roughgarden;Eva Tardos;Vijay V. Vazirani.

**(2007)**

1955 Citations

Algorithmic Game Theory: Quantifying the Inefficiency of Equilibria

Noam Nisan;Tim Roughgarden;Eva Tardos;Vijay V. Vazirani.

**(2007)**

1950 Citations

Selfish Routing and the Price of Anarchy

Tim Roughgarden.

**(2005)**

1030 Citations

The Price of Stability for Network Design with Fair Cost Allocation

Elliot Anshelevich;Anirban Dasgupta;Jon Kleinberg;Éva Tardos.

SIAM Journal on Computing **(2008)**

982 Citations

Universally Utility-maximizing Privacy Mechanisms

Arpita Ghosh;Tim Roughgarden;Mukund Sundararajan.

SIAM Journal on Computing **(2012)**

471 Citations

The price of anarchy is independent of the network topology

Tim Roughgarden.

symposium on the theory of computing **(2002)**

405 Citations

Computing correlated equilibria in multi-player games

Christos H. Papadimitriou;Tim Roughgarden.

Journal of the ACM **(2008)**

396 Citations

Intrinsic robustness of the price of anarchy

Tim Roughgarden.

symposium on the theory of computing **(2009)**

393 Citations

Stackelberg Scheduling Strategies

Tim Roughgarden.

SIAM Journal on Computing **(2004)**

386 Citations

Games and Economic Behavior

(Impact Factor: 1.223)

Cornell University

Hebrew University of Jerusalem

New York University

New York University

University of California, Santa Cruz

University of California, Irvine

Northwestern University

The University of Texas at Dallas

University of Pennsylvania

Carnegie Mellon University

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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