What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Law
- Game theory
Moshe Tennenholtz mainly investigates Mathematical economics, Mathematical optimization, Artificial intelligence, Mechanism design and Context.
His work carried out in the field of Mathematical economics brings together such families of science as Vickrey auction and Common value auction.
The concepts of his Mathematical optimization study are interwoven with issues in Implementation theory, Repeated game and Algorithm.
His research in Repeated game intersects with topics in Markov perfect equilibrium and Learning classifier system.
Many of his research projects under Artificial intelligence are closely connected to Shared environment with Shared environment, tying the diverse disciplines of science together.
Moshe Tennenholtz interconnects Facility location problem, Incentive compatibility and Computation in the investigation of issues within Mechanism design.
His most cited work include:
- R-max - a general polynomial time algorithm for near-optimal reinforcement learning (825 citations)
- On social laws for artificial agent societies: off-line design (419 citations)
- Approximate mechanism design without money (290 citations)
What are the main themes of his work throughout his whole career to date?
Moshe Tennenholtz mostly deals with Mathematical economics, Mathematical optimization, Nash equilibrium, Artificial intelligence and Context.
Moshe Tennenholtz combines subjects such as Common value auction and Combinatorial auction with his study of Mathematical economics.
His biological study spans a wide range of topics, including Time complexity, Task, Stochastic game, Strategy and Mechanism design.
In his work, Mechanism is strongly intertwined with Incentive compatibility, which is a subfield of Mechanism design.
The Nash equilibrium study combines topics in areas such as Correlated equilibrium and Price of anarchy.
His studies deal with areas such as Set and Complete information as well as Outcome.
He most often published in these fields:
- Mathematical economics (27.39%)
- Mathematical optimization (20.67%)
- Nash equilibrium (13.44%)
What were the highlights of his more recent work (between 2018-2021)?
- Incentive compatibility (6.20%)
- Ranking (5.17%)
- Information retrieval (4.65%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Incentive compatibility, Ranking, Information retrieval, The Internet and Data science.
His Incentive compatibility research also works with subjects such as
- Combinatorics that connect with fields like Probabilistic logic,
- Discrete mathematics most often made with reference to Mechanism.
The concepts of his Ranking study are interwoven with issues in Quality, Promotion and Relevance.
The Ranking research Moshe Tennenholtz does as part of his general Information retrieval study is frequently linked to other disciplines of science, such as Basis, Selection and Current, therefore creating a link between diverse domains of science.
As part of the same scientific family, he usually focuses on The Internet, concentrating on Internet privacy and intersecting with Higher education, Health services, User privacy and Mechanism design.
His Asymptotically optimal algorithm study improves the overall literature in Mathematical optimization.
Between 2018 and 2021, his most popular works were:
- Predicting human decisions with behavioral theories and machine learning. (12 citations)
- Rethinking search engines and recommendation systems: a game theoretic perspective (7 citations)
- Regression Equilibrium (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Law
- Game theory
Moshe Tennenholtz spends much of his time researching Data science, Speech recognition, Language model, Masking and Recommender system.
His Data science study combines topics in areas such as Single person, Similarity, Surprise, Homophily and Focus.
His work deals with themes such as Security token, Heuristic, Phrase and Training time, which intersect with Speech recognition.
His work often combines Language model and Pointwise mutual information studies.
His research in Recommender system intersects with topics in Game theoretic, Social Welfare and Search engine.
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