About

Ozan Candogan is the Nicholson Family Professor of Operations Management in the Wallman Society of Fellows at the University of Chicago Booth School of Business. Before joining Chicago Booth, he was an Assistant Professor at the Fuqua School of Business, where he was part of the Decision Sciences area. He earned his Ph.D. and M.S. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. Professor Candogan's primary research area is social and economic networks, focusing on two complementary themes. First, he investigates the impact of networks on operational decisions by studying how to leverage network data—such as social networks, mobility networks, and trading networks—to improve operational decisions including pricing, inventory management, information disclosure, and facility location. He also explores the value of such data in various operational settings. Second, he develops novel approaches and tools for analyzing complex social and economic systems, applying these to characterize equilibria and dynamics in games, study equilibria and comparative statics in trading networks, and design information disclosure policies. His research has practical applications in the operations of online social networks, ride-sharing platforms, delivery platforms, two-sided marketplaces, supply chains, and online advertising platforms. His work has been published in leading journals such as Management Science, Operations Research, Mathematics of Operations Research, Manufacturing & Service Operations Management, and Games and Economic Behavior. Professor Candogan has been recognized as a finalist for the 2013 George Nicholson Student Paper Competition and the 2021 M&SOM Service Management SIG Prize, and he is a recipient of the 2009 Siebel Scholarship and the 2012 Microsoft Research Ph.D. Fellowship. At Chicago Booth, he teaches Operations Management and two PhD courses on networks titled Introduction to Networks and Networks and Markets.

Research topics

• Computer Science
• Computer Security
• Internet privacy
• Mathematics

Selected publications

• Information Design in Supply Chains with Priority Suppliers

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Mobility Data, Competition and Strategic Location

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Taming Spatial Imbalances in Freight Networks: Time-Based Mechanisms

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• <div> Network Rewiring and Spatial Targeting: Optimal Disease <span>Mitigation in Multilayer Social Networks</span></div>

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Endogenous Entry in Networked Markets with Production and Edge Capacity Constraints

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Managing Resources for Shared Micromobility: Approximate Optimality in Large-Scale Systems

  Management Science · 2024-10-14 · 7 citations

  article

  We consider the problem of managing resources in shared micromobility systems (bike sharing and scooter sharing). An important task in managing such systems is periodic repositioning/recharging/sourcing of units to avoid stockouts or excess inventory at nodes with unbalanced flows. We consider a discrete-time model; each period begins with an initial inventory at each node in the network, and then, customers (demand) materialize at the nodes. Each customer picks up a unit at the origin node and drops it off at a randomly sampled destination node with an origin-specific probability distribution. We model the above network inventory management problem as an infinite horizon discrete-time discounted Markov decision process (MDP) and prove the asymptotic optimality of a novel mean-field approximation to the original MDP as the number of stations becomes large. To compute an approximately optimal policy for the mean-field dynamics, we provide an algorithm with a running time that is logarithmic in the desired optimality gap. Lastly, we compare the performance of our mean field-based policy with state-of-the-art heuristics via numerical experiments, including experiments using Austin scooter-sharing data. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02023 .

  PublisherDOI

• Mobility Data in Operations: Multi-Location Facility Location Problem

  2024-07-08 · 3 citations

  article1st authorCorresponding

  Individual mobility patterns have a first-order impact on many operational decisions, ranging from facility location decisions to the optimization of transit systems. In the past, obtaining data on individual mobility patterns was a challenging task. However, in recent years, such data have been extensively collected via mobile phones. Moreover, various data providers have made these data available at scale, enabling decision makers to leverage them for large-scale analysis.

  PublisherDOI

• The Value of Information Design in Supply Chain Management

  Management Science · 2024-11-11 · 8 citations

  article1st authorCorresponding

  This paper studies an information design problem of a retailer in a two-tier supply chain that procures a single type of product from a supplier. The supplier needs to decide on a production quantity by balancing the shortage cost and the excess inventory holding cost with respect to the retailer’s demand. The retailer’s demand is random, but the retailer receives an informative signal about the demand before the supplier sets the production quantity and places orders after learning the demand realization. The retailer wants to reduce the shortage cost, and to this end, the retailer can disclose information about the retailer’s signal to persuade the supplier to increase production levels. For this setup, we characterize the optimal information disclosure policy of the retailer and shed light on settings in which the retailer strictly benefits from carefully designed information disclosure policies relative to a full- or a no-disclosure policy. This paper was accepted by Jeannette Song, operations management. Funding: O. Candogan acknowledges the NSF (National Science Foundation) [Grant 2216912] for “Institute for Data, Econometrics, Algorithms and Learning.” Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03004 .

  PublisherDOI

• Mobility Data in Operations: The Facility Location Problem

  SSRN Electronic Journal · 2023-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Achieving Fairness and Accuracy in Regressive Property Taxation

  arXiv (Cornell University) · 2023-12-10

  preprintOpen access1st authorCorresponding

  Regressivity in property taxation, or the disproportionate overassessment of lower-valued properties compared to higher-valued ones, results in an unfair taxation burden for Americans living in poverty. To address regressivity and enhance both the accuracy and fairness of property assessments, we introduce a scalable property valuation model called the $K$-segment model. Our study formulates a mathematical framework for the $K$-segment model, which divides a single model into $K$ segments and employs submodels for each segment. Smoothing methods are incorporated to balance and smooth the multiple submodels within the overall model. To assess the fairness of our proposed model, we introduce two innovative fairness measures for property evaluation and taxation, focusing on group-level fairness and extreme sales price portions where unfairness typically arises. Compared to the model employed currently in practice, our study demonstrates that the $K$-segment model effectively improves fairness based on the proposed measures. Furthermore, we investigate the accuracy--fairness trade-off in property assessments and illustrate how the $K$-segment model balances high accuracy with fairness for all properties. Our work uncovers the practical impacts of the $K$-segment models in addressing regressivity in property taxation, offering a tangible solution for policymakers and property owners. By implementing this model, we pave the way for a fairer taxation system, ensuring a more equitable distribution of tax burdens.

  PublisherOA PDFDOI

Frequent coauthors

• Asuman Ozdaglar

  20 shared

• Pablo A. Parrilo

  15 shared

• Kostas Bimpikis

  Stanford University

  13 shared

• Yiding Feng

  University of Chicago

  12 shared

• John R. Birge

  University of Chicago

  11 shared

• Daniela Sabán

  Palo Alto University

  11 shared

• Philipp Strack

  Yale University

  10 shared

• Santiago Balseiro

  8 shared

Education

• Ph.D., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

• M.S., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

Awards & honors

• MSOM Young Scholar Prize (2023)
• Finalist for the MSOM Best OM Paper in Operations Research A…
• INFORMS Revenue Management and Pricing Section Prize (2022)
• Finalist for the M&SOM Service Management SIG Prize (2021)
• Winner of the LinkedIn Economic Graph Challenge (2015)