About

Eran Shmaya is a professor in the Department of Economics at Stony Brook University. His office is located in N631 Social and Behavioral Sciences Building. He teaches graduate courses including Microeconomics and Game Theory. His research focus, background, and key contributions are not detailed on the page.

Research topics

• Machine Learning
• Computer Science
• Mathematical optimization
• Mathematical economics
• Mathematics
• Computer Security
• Artificial Intelligence
• Economics
• Business
• Industrial organization
• Econometrics
• Operations research
• Microeconomics

Selected publications

• Bayesian Learning in Mean Field Games

  SIAM Journal on Control and Optimization · 2025-05-14

  articleOpen access1st authorCorresponding

  International audience

  PublisherOA PDFDOI

• Robust Monopoly Regulation

  American Economic Review · 2025-01-30 · 8 citations

  articleSenior author

  We study how to regulate a monopolistic firm using a robust-design, non-Bayesian approach. We derive a policy that minimizes the regulator’s worst-case regret, where regret is the difference between the regulator’s complete-information payoff and his realized payoff. When the regulator’s payoff is consumers’ surplus, he caps the firm’s average revenue. When his payoff is the total surplus of both consumers and the firm, he offers a piece rate subsidy to the firm while capping the total subsidy. For intermediate cases, the regulator combines these three policy instruments to balance three goals: protecting consumers’ surplus, mitigating underproduction, and limiting potential overproduction. (JEL D21, D42, D83, H25, L51)

  PublisherDOI

• Disentangling Exploration from Exploitation

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• Collective Upkeep

  arXiv (Cornell University) · 2024-07-06

  preprintOpen accessSenior author

  We design mechanisms for maintaining public goods which require periodic in-kind contributions, motivated by incentives problems facing crowd-sourced recommender systems. Utilitarian welfare is maximized by redistributive policies which are infeasible when group members can leave or misreport their preferences. An optimal mechanism reduces contributions for group members with low benefit-cost ratios to encourage participation; and pairs reduced contributions with restricted access to the good to ensure truthful reporting. At most two membership tiers are offered at the optimum, indicating that ecommerce and digital content platforms may benefit substantially from offering simple user-adjustable recommendation settings.

  PublisherOA PDFDOI

• Bayesian Learning in Mean Field Games

  arXiv (Cornell University) · 2024-01-31

  preprintOpen access1st authorCorresponding

  We consider a mean-field game model where the cost functions depend on a fixed parameter, called \textit{state}, which is unknown to players. Players learn about the state from a a stream of private signals they receive throughout the game. We derive a mean field system satisfied by the equilibrium payoff of the game and prove existence of a solution under standard regularity assumptions. Additionally, we establish the uniqueness of the solution when the cost function satisfies the monotonicity assumption of Lasry and Lions at each state.

  PublisherOA PDFDOI

• Disentangling Exploration from Exploitation

  arXiv (Cornell University) · 2024-04-29

  preprintOpen access

  Starting from Robbins (1952), the literature on experimentation via multi-armed bandits has wed exploration and exploitation. Nonetheless, in many applications, agents' exploration and exploitation need not be intertwined: a policymaker may assess new policies different than the status quo; an investor may evaluate projects outside her portfolio. We characterize the optimal experimentation policy when exploration and exploitation are disentangled in the case of Poisson bandits, allowing for general news structures. The optimal policy features complete learning asymptotically, exhibits lots of persistence, but cannot be identified by an index a la Gittins. Disentanglement is particularly valuable for intermediate parameter values.

  PublisherOA PDFDOI

• A Characterization of Optimal Queueing Regimes

  arXiv (Cornell University) · 2024-01-24

  preprintOpen accessSenior author

  We consider an M/M/1 queueing model where customers can strategically decide to enter or leave the queue. We characterize the class of queueing regimes such that, for any parameters of the model, the socially efficient behavior is an equilibrium outcome.

  PublisherOA PDFDOI

• Optimal Queueing Regimes

  2024-07-08 · 2 citations

  articleSenior author

  It is well known that in an M/M/1 queueing model where customers strategically decide whether or not to enter a queue, and if and when to renege, under a first-come-first-served regime, customers' selfish behavior produces an outcome that is socially suboptimal. Optimality could be achieved by adopting a different queuing regime. In particular, optimality is achieved by any regime where a new arriving customer is put in a position that is not the last. The priority slots regime, is also universally optimal. Under this regime, there is a sequence of slots indexed by the positive integers. A new arriving customer who enters the queue occupies the available slot with the smallest index. At any given time the customer occupying the slot with the smallest index is being served. A new customer who finds the first slot vacant preempts the current service. Once served, a customer leaves the system and frees the slot she occupied.

  PublisherDOI

• Disentangling Exploration from Exploitation

  National Bureau of Economic Research · 2024-05-01 · 5 citations

  reportOpen access

  Starting from Robbins (1952), the literature on experimentation via multi-armed bandits has wed exploration and exploitation.Nonetheless, in many applications, agents' exploration and exploitation need not be intertwined: a policymaker may assess new policies different than the status quo; an investor may evaluate projects outside her portfolio.We characterize the optimal experimentation policy when exploration and exploitation are disentangled in the case of Poisson bandits, allowing for general news structures.The optimal policy features complete learning asymptotically, exhibits lots of persistence, but cannot be identified by an index à la Gittins.Disentanglement is particularly valuable for intermediate parameter values.

  PublisherDOI

• Disentangling Exploration from Exploitation

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  articleOpen access
  PublisherDOI

Frequent coauthors

• Ehud Lehrer

  16 shared

• Nabil I. Al‐Najjar

  Kellogg's (Canada)

  12 shared

• Eilon Solan

  Tel Aviv University

  11 shared

• Leeat Yariv

  Centre for Economic Policy Research

  11 shared

• Federico Echenique

  University of California, Berkeley

  10 shared

• Yaron Azrieli

  9 shared

• Christopher P. Chambers

  9 shared

• Yingni Guo

  6 shared