World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
12630
World Ranking
4073
National Ranking
64

Research.com Recognitions

  • 2019 - ACM Fellow For contributions to AI and algorithmic game theory
  • 2012 - ACM AAAI Allen Newell Award For fundamental contributions at the intersection of computer science, game theory, and economics, most particularly in multi-agent systems and social coordination (broadly construed), which have yielded major contributions to all three disciplines.
  • 2010 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions in the area of multiagent systems and beyond, and for extraordinary service to the AI community.

Overview

Moshe Tennenholtz is affiliated with the Technion - Israel Institute of Technology in Israel. Their research spans the fields of Decision Sciences and Computer Science, with significant contributions in subfields such as Management Science and Operations Research, Artificial Intelligence, Economics and Econometrics, Information Systems, and Marketing.

Their work focuses on a range of main topics that include:

  • Game Theory and Applications
  • Auction Theory and Applications
  • Game Theory and Voting Systems
  • Consumer Market Behavior and Pricing
  • Blockchain Technology Applications and Security
  • Privacy-Preserving Technologies in Data
  • Topic Modeling

Some of their recent papers are:

  • "Congestion Games with Agent Failures," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "PMI-Masking: Principled masking of correlated spans," 2020, arXiv (Cornell University)
  • "Competitive Search," 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
  • "Protecting the Protected Group: Circumventing Harmful Fairness," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Pareto-Improving Data-Sharing," 2022, 2022 ACM Conference on Fairness, Accountability, and Transparency

Frequent publication venues for their work include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of Artificial Intelligence Research
  • Electronic Proceedings in Theoretical Computer Science
  • SSRN Electronic Journal

The scientist frequently collaborates with several co-authors, including:

  • Ronen Gradwohl
  • Yotam Gafni
  • Roi Reichart
  • Oren Kurland
  • Itai Arieli

Moshe Tennenholtz has received several awards recognizing their contributions, such as:

  • ACM Fellow, 2019, for contributions to AI and algorithmic game theory
  • ACM AAAI Allen Newell Award, 2012, for fundamental contributions at the intersection of computer science, game theory, and economics, with impact on multi-agent systems and social coordination
  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), 2010, for significant contributions in multiagent systems and exceptional service to the AI community

Best Publications

  • R-max - a general polynomial time algorithm for near-optimal reinforcement learning

    Ronen I. Brafman;Moshe Tennenholtz

  • On social laws for artificial agent societies: off-line design

    Yoav Shoham;Moshe Tennenholtz

  • On the Synthesis of Useful Social Laws for Artificial Agent Societies (Preliminary Report).

    Yoav Shoham;Moshe Tennenholtz

  • Approximate mechanism design without money

    Ariel D. Procaccia;Moshe Tennenholtz

  • On the synthesis of useful social laws for artificial agent societies

    Yoav Shoham;Moshe Tennenholtz

  • On the emergence of social conventions: modeling, analysis, and simulations

    Yoav Shoham;Moshe Tennenholtz

  • Trust-based recommendation systems: an axiomatic approach

    Reid Andersen;Christian Borgs;Jennifer Chayes;Uriel Feige

  • Adaptive load balancing: a study in multi-agent learning

    Andrea Schaerf;Yoav Shoham;Moshe Tennenholtz

  • Ranking systems: the PageRank axioms

    Alon Altman;Moshe Tennenholtz

  • Artificial social systems

    Yoram Moses;Moshe Tennenholtz

  • An Algorithm for Multi-Unit Combinatorial Auctions

    Kevin Leyton-Brown;Yoav Shoham;Moshe Tennenholtz

  • Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System

    Elad Yom-Tov;Guy Feraru;Mark Kozdoba;Shie Mannor

  • Emergent Conventions in Multi-Agent Systems: Initial Experimental Results and Observations (Preliminary Report).

    Yoav Shoham;Moshe Tennenholtz

  • Bundling Equilibrium in Combinatorial auctions

    Ron Holzman;Noa E. Kfir-Dahav;Dov Monderer;Moshe Tennenholtz

  • Approximately optimal mechanism design via differential privacy

    Kobbi Nissim;Rann Smorodinsky;Moshe Tennenholtz

  • Choosing social laws for multi-agent systems: minimality and simplicity

    David Fitoussi;Moshe Tennenholtz

  • Strategyproof Approximation of the Minimax on Networks

    Noga Alon;Noga Alon;Michal Feldman;Michal Feldman;Ariel D. Procaccia;Moshe Tennenholtz;Moshe Tennenholtz

  • A note on competitive diffusion through social networks

    Noga Alon;Michal Feldman;Ariel D. Procaccia;Moshe Tennenholtz

  • Some Tractable Combinatorial Auctions

    Moshe Tennenholtz

  • Strong and correlated strong equilibria in monotone congestion games

    Ola Rozenfeld;Moshe Tennenholtz

  • A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients.

    Irit Hochberg;Guy Feraru;Mark Kozdoba;Shie Mannor

Frequent Co-Authors

Yoav Shoham
Yoav Shoham Stanford University
Michal Feldman
Michal Feldman Tel Aviv University
Noga Alon
Noga Alon Tel Aviv University
Ronen I. Brafman
Ronen I. Brafman Ben-Gurion University of the Negev
Uriel Feige
Uriel Feige Weizmann Institute of Science
Kevin Leyton-Brown
Kevin Leyton-Brown University of British Columbia
Ariel D. Procaccia
Ariel D. Procaccia Harvard University
Adam Tauman Kalai
Adam Tauman Kalai Microsoft (United States)
Elad Yom-Tov
Elad Yom-Tov Microsoft (United States)
Gil Kalai
Gil Kalai Hebrew University of Jerusalem

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