Strategyproof Approximation of the Minimax on Networks von Alon, Noga, Feldman, Michal, Procaccia, Ariel D., Tennenholtz, Moshe

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A note on competitive diffusion through social networks von Alon, Noga, Feldman, Michal, Procaccia, Ariel D., Tennenholtz, Moshe

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Coopetition Against an Amazon von Gradwohl, Ronen, Tennenholtz, Moshe

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Group Recommendations: Axioms, Impossibilities, and Random Walks von Lev, Omer, Tennenholtz, Moshe

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Sequential Voting with Confirmation Network von Babichenko, Yakov, Dean, Oren, Tennenholtz, Moshe

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On the emergence of social conventions: modeling, analysis, and simulations von Shoham, Yoav, Tennenholtz, Moshe

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Reputation-based persuasion platforms von Arieli, Itai, Madmon, Omer, Tennenholtz, Moshe

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Optimal Mechanism Design for Agents with DSL Strategies: The Case of Sybil Attacks in Combinatorial Auctions von Gafni, Yotam, Tennenholtz, Moshe

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Selling Data to a Competitor (Extended Abstract) von Gradwohl, Ronen, Tennenholtz, Moshe

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An Axiomatic Approach to Routing von Lev, Omer, Tennenholtz, Moshe, Zohar, Aviv

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Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System von Yom-Tov, Elad, Feraru, Guy, Kozdoba, Mark, Mannor, Shie, Tennenholtz, Moshe, Hochberg, Irit

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Multiunit Facility Location Games von Ben-Porat, Omer, Tennenholtz, Moshe

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Bidding games and efficient allocations von Meir, Reshef, Kalai, Gil, Tennenholtz, Moshe

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Worst-case Bounds on Power vs. Proportion in Weighted Voting Games with Application to False-Name Manipulation von Gafni, Yotam, Lavi, Ron, Tennenholtz, Moshe

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Worst-case Bounds on Power vs. Proportion in Weighted Voting Games with Application to False-Name Manipulation von Gafni, Yotam, Lavi, Ron, Tennenholtz, Moshe

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Approximate Mechanism Design without Money von Procaccia, Ariel D., Tennenholtz, Moshe

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Representative Committees of Peers von Meir, Reshef, Sandomirskiy, Fedor, Tennenholtz, Moshe

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Optimization with Uniform Size Queries von Feige, Uriel, Tennenholtz, Moshe

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Predicting Decisions in Language Based Persuasion Games von Apel, Reut, Erev, Ido, Reichart, Roi, Tennenholtz, Moshe

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R-MAX - A general polynomial time algorithm for near-optimal reinforcement learning von Brafman, R I, Tennenholtz, M

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