2019 - ACM Fellow For contributions to game theory, social choice theory, and mechanism design
2019 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the advancement of artificial intelligence through integration with economics and philosophy, including game theory, mechanism design, social choice, and ethics.
2015 - Fellow of John Simon Guggenheim Memorial Foundation
2008 - Fellow of Alfred P. Sloan Foundation
His primary areas of study are Voting, Mathematical economics, Artificial intelligence, Mathematical optimization and Nash equilibrium. His Voting research incorporates elements of Ranking and Theoretical computer science. His Mathematical economics research is multidisciplinary, relying on both Vickrey auction, Common value auction and Vickrey–Clarke–Groves auction.
The Artificial intelligence study combines topics in areas such as Computational complexity theory, Core, Game theory, Outcome and Key. His research in Mathematical optimization focuses on subjects like Multi-agent system, which are connected to Impossibility, Risk analysis and Strategy proofness. Vincent Conitzer usually deals with Nash equilibrium and limits it to topics linked to Stackelberg competition and Strategy.
Vincent Conitzer mainly focuses on Mathematical economics, Mathematical optimization, Voting, Mechanism design and Game theory. He combines subjects such as Welfare and Artificial intelligence with his study of Mathematical economics. Within one scientific family, Vincent Conitzer focuses on topics pertaining to Multi-agent system under Mathematical optimization, and may sometimes address concerns connected to Key.
His Voting study frequently links to related topics such as Theoretical computer science. While the research belongs to areas of Mechanism design, Vincent Conitzer spends his time largely on the problem of Outcome, intersecting his research to questions surrounding Optimization problem. Vincent Conitzer has included themes like Normal-form game, Repeated game and Equilibrium selection in his Nash equilibrium study.
Mathematical economics, Mathematical optimization, Computational complexity theory, Mechanism design and Welfare are his primary areas of study. His work on Zero-sum game as part of general Mathematical economics study is frequently connected to State, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His research on Mathematical optimization often connects related areas such as Principal.
His Computational complexity theory study frequently draws connections to other fields, such as Nash equilibrium. Vincent Conitzer interconnects Evolutionarily stable strategy and Algorithmic game theory in the investigation of issues within Nash equilibrium. Vincent Conitzer has researched Mechanism design in several fields, including Linear programming, Online advertising and Time horizon.
The scientist’s investigation covers issues in Mathematical optimization, Context, Computational complexity theory, Smart grid and Job shop scheduling. His Mathematical optimization study incorporates themes from Game theoretic, Game theory and Representation. You can notice a mix of various disciplines of study, such as Online advertising, Computability, Isolation, Microeconomics and Uniqueness, in his Context studies.
Online advertising connects with themes related to Mechanism design in his study. Monotonic function is intertwined with Common value auction and Incentive in his research.
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.
When are elections with few candidates hard to manipulate
Vincent Conitzer;Tuomas Sandholm;Jérôme Lang.
Journal of the ACM (2007)
Handbook of Computational Social Choice
Felix Brandt;Vincent Conitzer;Ulle Endriss;Jérôme Lang.
Research Papers in Economics (2016)
Computing the optimal strategy to commit to
Vincent Conitzer;Tuomas Sandholm.
electronic commerce (2006)
New complexity results about Nash equilibria
Vincent Conitzer;Tuomas W Sandholm.
Games and Economic Behavior (2008)
Complexity of mechanism design
Vincent Conitzer;Tuomas Sandholm.
uncertainty in artificial intelligence (2002)
Complexity results about Nash equilibria
Vincent Conitzer;Tüomas Sandholm.
international joint conference on artificial intelligence (2003)
Stackelberg vs. Nash in security games: an extended investigation of interchangeability, equivalence, and uniqueness
Dmytro Korzhyk;Zhengyu Yin;Christopher Kiekintveld;Vincent Conitzer.
Journal of Artificial Intelligence Research (2011)
AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents
Vincent Conitzer;Tuomas Sandholm.
Machine Learning (2007)
Determining possible and necessary winners under common voting rules given partial orders
Lirong Xia;Vincent Conitzer.
Journal of Artificial Intelligence Research (2011)
Complexity of computing optimal stackelberg strategies in security resource allocation games
Dmytro Korzhyk;Vincent Conitzer;Ronald Parr.
national conference on artificial intelligence (2010)
Games and Economic Behavior
(Impact Factor: 1.265)
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