About

John R. Birge is the Hobart W. Williams Distinguished Service Professor of Operations Management at The University of Chicago Booth School of Business. His research focuses on the mathematical modeling of systems under uncertainty, with a particular emphasis on maximizing operational and financial goals through the methodologies of stochastic programming and large-scale optimization. Birge was initially drawn to this area by a desire to apply mathematics in a useful and practical way. His work demonstrates how special problem structures can enable efficient solutions to complex decision-making problems under uncertainty. His research has received support from a variety of organizations including the National Science Foundation, Ford Motor Company, General Motors Corporation, the National Institute of Justice, the Office of Naval Research, the Electric Power Research Institute, and Volkswagen of America. Birge has published extensively and has been recognized with several prestigious awards, including the Best Paper Award from the Japan Society for Industrial and Applied Mathematics, the Institute for Operations Research and the Management Sciences Fellows Award, the Institute of Industrial Engineers Medallion Award, and election to the National Academy of Engineering.

Research topics

• Computer science
• Mathematical optimization
• Mathematics
• Business
• Economics

Selected publications

• Trafficability Constrained Harvest Scheduling Under Weather Risk: Evidence from Sugar Beet Logistics

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Evolutionary Model of a Token-Based Data Market

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Mechanism Design for Decentralized Risk Detection: Strict Propriety, Network Coalitions, and the Backfiring Mandate

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Managing Multitier Inventory Networks with Expediting Under Normal and Disrupted Modes

  Manufacturing & Service Operations Management · 2026-03-26

  article

  Problem definition: We collaborate with an industrial partner whose supply chain uses multiple tiers, locations, and shipping speeds to efficiently serve customers. In practice, our partner also faces the possibility of upstream disruptions, which limit inventory availability. We model these key features of our partner’s network as a multiechelon distribution system (central warehouse and retailers) with expediting and disruptions. Methodology/results: We prove a novel stochastic program lower bound on optimal cost in this model and use this program to develop a heuristic base-stock policy. Our analysis demonstrates that there is a pronounced benefit from centralized inventory (i.e., holding inventory at the central warehouse) in distribution systems with expediting and disruptions as it can be used to both clear backlogs through expediting and hedge against future disruptions. Further, in the disrupted mode, we provide a simple criterion to determine when decentralization (i.e., holding inventory at the retailers) is preferred over complete centralization. Then, we validate our policies using data from our partner’s nationwide distribution network in the United States. Managerial implications: We provide novel inventory policies for managing a distribution system with expediting and disruptions that are understandable and implementable in practice. Our analysis provides the insight that facilitating the right level of central warehouse inventory is a critical hedge for improving performance in these systems. Finally, our industrial partner’s data suggest that our policies can provide significant cost savings in practice. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0249 .

  PublisherDOI

• Mechanism Design for Decentralized Risk Detection: Strict Propriety, Network Coalitions, and the Backfiring Mandat

  ArXiv.org · 2026-04-23

  articleOpen accessSenior author

  Competing firms that share a population of risky customers face a decentralized risk detection problem in which each firm holds fragmentary information whose aggregation would generate social value, but private incentives impede truthful sharing. We develop a dynamic mechanism design framework for this setting and identify three strategic frictions that distinguish it from classical mechanism design with decentralized information: compliance moral hazard, adversarial adaptation, and information destruction through intervention. A temporal value assignment (TVA) mechanism credits firms using a strictly proper scoring rule applied to discounted verified outcomes; under stated assumptions, TVA implements truthful posterior reporting as a Bayes--Nash equilibrium (uniquely optimal at each edge in large federations, with $O(1/m)$ shading in finite systems). A network Shapley characterization shows that under edge-additive coalition value, each firm's marginal contribution is proportional to its weighted cross-firm interaction degree, yielding a sharp prescription for coalition design that prioritizes inter-firm volume over firm size. Embedding TVA in a model of competition among firms, we establish a welfare ordering across four regulatory regimes (autarky, voluntary federation, mandated full sharing, TVA) and identify conditions under which information-sharing mandates without compatible incentive design reduce welfare below autarky: a ``backfiring mandate.'' We illustrate the framework on a 1.4M-transaction synthetic anti-money-laundering benchmark; the same machinery extends to platform fraud, cybersecurity threat intelligence, and supply chain risk detection.

  PublisherOA PDF

• Mechanism Design for Decentralized Risk Detection: Strict Propriety, Network Coalitions, and the Backfiring Mandat

  arXiv (Cornell University) · 2026-04-23

  preprintOpen accessSenior author

  Competing firms that share a population of risky customers face a decentralized risk detection problem in which each firm holds fragmentary information whose aggregation would generate social value, but private incentives impede truthful sharing. We develop a dynamic mechanism design framework for this setting and identify three strategic frictions that distinguish it from classical mechanism design with decentralized information: compliance moral hazard, adversarial adaptation, and information destruction through intervention. A temporal value assignment (TVA) mechanism credits firms using a strictly proper scoring rule applied to discounted verified outcomes; under stated assumptions, TVA implements truthful posterior reporting as a Bayes--Nash equilibrium (uniquely optimal at each edge in large federations, with $O(1/m)$ shading in finite systems). A network Shapley characterization shows that under edge-additive coalition value, each firm's marginal contribution is proportional to its weighted cross-firm interaction degree, yielding a sharp prescription for coalition design that prioritizes inter-firm volume over firm size. Embedding TVA in a model of competition among firms, we establish a welfare ordering across four regulatory regimes (autarky, voluntary federation, mandated full sharing, TVA) and identify conditions under which information-sharing mandates without compatible incentive design reduce welfare below autarky: a ``backfiring mandate.'' We illustrate the framework on a 1.4M-transaction synthetic anti-money-laundering benchmark; the same machinery extends to platform fraud, cybersecurity threat intelligence, and supply chain risk detection.

  PublisherDOI

• Pricing and Capacity Decisions in Platform Competition with Network Externalities

  2025-07-02

  articleOpen access1st authorCorresponding

  The structure of network externalities influences platform competition and can determine whether a two-sided market is winner-takes-all or highly contestable. Yet, because of analytical challenges, relatively little is known about these structures in different markets, raising several research questions: How do network externalities arise in specific markets? What is the structure of network externalities? How does the structure influence the competition between two-sided platforms? What conditions lead to a monopoly or an oligopoly outcome, enabling or discouraging entry? We address these questions in the context of ride-hailing platforms, identifying the externalities that arise from congestion.

  PublisherOA PDFDOI

• Networked Markets, Fragmented Data: Adaptive Graph Learning for Customer Risk Analytics and Policy Design

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Profitability of collusive sandwich attack in automated market maker-based decentralized exchanges

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Integrating Harvest Planning Along the Supply Chain: A Case Study From the European Sugar Beet Industry

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Ding‐Zhu Du

  162 shared

• M Pardalos Panos

  162 shared

• Hanif D. Sherali

  Virginia Tech

  162 shared

• Christodoulos A. Floudas

  162 shared

• M Pardalos

  University of Florida

  162 shared

• V. Jeyakumar

  64 shared

• Zden Ěk

  University of Florida

  49 shared

• Silvia Schwarze

  Universität Hamburg

  49 shared

Education

• B.S.

  Princeton University

  1977

• M.S., operations research

  Stanford University

  1979

• Ph.D., operations research

  Stanford University

  1980

Awards & honors

• Best Paper Award from the Japan Society for Industrial and A…
• Institute for Operations Research and the Management Science…
• Institute of Industrial Engineers Medallion Award
• elected to the National Academy of Engineering