About

Sheldon H. Jacobson is a Founder Professor of Computer Science at the University of Illinois Urbana-Champaign, where he also serves as the Director of the Simulation and Optimization Laboratory and the Founding Director of the Bed Time Research Institute. He holds appointments in Industrial & Enterprise Systems Engineering, Electrical & Computer Engineering, Mathematics, and the Carle Illinois College of Medicine. He earned his B.Sc. and M.Sc. in Mathematics from McGill University and his Ph.D. in Operations Research from Cornell University. His academic career includes faculty positions at Case Western Reserve University, Virginia Tech, and the University of Illinois, with a stint as a Program Director at the National Science Foundation. Jacobson's research focuses on applying operations research and advanced analytics to societal problems of national interest. He has made seminal contributions in aviation security analytics, demonstrating how probabilistic models, optimization, and artificial intelligence can improve aviation security systems. His foundational work on multi-level aviation security passenger screening influenced the development of risk-based security measures such as TSA Precheck. His research also spans pediatric immunization, transportation's impact on obesity, and computational redistricting to combat gerrymandering. He has published extensively, delivered numerous presentations worldwide, and directed many Ph.D. dissertations. Recognized with prestigious awards including a Guggenheim Fellowship, the INFORMS Impact Prize, and the George E. Kimball Medal, Jacobson's work has been widely reported in the media and discussed in opinion-editorials. He is an elected Fellow of AAAS, INFORMS, and IISE, and has received multiple awards for research, service, and teaching contributions.

Research topics

• Computer Science
• Political Science
• Law
• Medicine
• Mathematics
• Mathematical optimization
• Engineering
• Operations research
• Mathematical economics
• Internal medicine
• Demography
• Environmental health
• Aeronautics
• Business
• Transport engineering
• Economics
• Telecommunications
• Public administration
• Gerontology

Selected publications

• Simpler Than You Think: The Practical Dynamics of Ranked Choice Voting

  Open MIND · 2026-02-15

  preprintSenior author

  Ranked Choice Voting (RCV) adoption is expanding across U.S. elections, but faces persistent criticism for complexity, strategic manipulation, and ballot exhaustion. We empirically test these concerns on real election data, across three diverse contexts: New York City's 2021 Democratic primaries (54 races), Alaska's 2024 primary-infused statewide elections (52 races), and Portland's 2024 multi-winner City Council elections (4 races). Our algorithmic approach circumvents computational complexity barriers by reducing election instance sizes (via candidate elimination). Our findings reveal that despite its intricate multi-round process and theoretical vulnerabilities, RCV consistently exhibits simple and transparent dynamics in practice, closely mirroring the interpretability of plurality elections. Following RCV adoption, competitiveness increased substantially compared to prior plurality elections, with average margins of victory declining by 9.2 percentage points in NYC and 11.4 points in Alaska. Empirically, complex ballot-addition strategies are not more efficient than simple ones, and ballot exhaustion has minimal impact, altering outcomes in only 3 of 110 elections. These findings demonstrate that RCV delivers measurable democratic benefits while proving robust to ballot-addition manipulation, resilient to ballot exhaustion effects, and maintaining transparent competitive dynamics in practice. The computational framework offers election administrators and researchers tools for immediate election-night analysis and facilitating clearer discourse around election dynamics.

  DOI

• Simpler than you think: the practical dynamics of ranked choice voting

  Journal of Computational Social Science · 2026-04-07

  articleOpen accessSenior author

  Abstract Ranked Choice Voting (RCV) adoption is expanding across U.S. elections, but faces persistent criticism for complexity, strategic manipulation, and ballot exhaustion. We empirically test these concerns on real election data, across three diverse contexts: New York City’s 2021 Democratic primaries (54 races), Alaska’s 2024 primary-infused statewide elections (52 races), and Portland’s 2024 multi-winner City Council elections (4 races). Our algorithmic approach circumvents computational complexity barriers by reducing election instance sizes (via candidate elimination). Our findings reveal that despite its intricate multi-round process and theoretical vulnerabilities, RCV consistently exhibits simple and transparent dynamics in practice, closely mirroring the interpretability of plurality elections. Following RCV adoption, competitiveness increased substantially compared to prior plurality elections, with average margins of victory declining by 9.2 percentage points in NYC and 11.4 points in Alaska. Empirically, complex ballot-addition strategies are not more efficient than simple ones, and ballot exhaustion has minimal impact, altering outcomes in only 3 of 110 elections. These findings demonstrate that RCV delivers measurable democratic benefits while proving robust to ballot-addition manipulation, resilient to ballot exhaustion effects, and maintaining transparent competitive dynamics in practice. The computational framework offers election administrators and researchers tools for immediate election-night analysis and facilitating clearer discourse around election dynamics.

  PublisherOA PDFDOI

• Simpler Than You Think: The Practical Dynamics of Ranked Choice Voting

  arXiv (Cornell University) · 2026-02-15

  articleOpen accessSenior author

  Ranked Choice Voting (RCV) adoption is expanding across U.S. elections, but faces persistent criticism for complexity, strategic manipulation, and ballot exhaustion. We empirically test these concerns on real election data, across three diverse contexts: New York City's 2021 Democratic primaries (54 races), Alaska's 2024 primary-infused statewide elections (52 races), and Portland's 2024 multi-winner City Council elections (4 races). Our algorithmic approach circumvents computational complexity barriers by reducing election instance sizes (via candidate elimination). Our findings reveal that despite its intricate multi-round process and theoretical vulnerabilities, RCV consistently exhibits simple and transparent dynamics in practice, closely mirroring the interpretability of plurality elections. Following RCV adoption, competitiveness increased substantially compared to prior plurality elections, with average margins of victory declining by 9.2 percentage points in NYC and 11.4 points in Alaska. Empirically, complex ballot-addition strategies are not more efficient than simple ones, and ballot exhaustion has minimal impact, altering outcomes in only 3 of 110 elections. These findings demonstrate that RCV delivers measurable democratic benefits while proving robust to ballot-addition manipulation, resilient to ballot exhaustion effects, and maintaining transparent competitive dynamics in practice. The computational framework offers election administrators and researchers tools for immediate election-night analysis and facilitating clearer discourse around election dynamics.

  PublisherOA PDF

• Generalizations of the Sock matching problem

  IISE Transactions · 2026-03-05

  articleSenior author
  PublisherDOI

• Strategic AI in Cournot Markets

  arXiv (Cornell University) · 2026-01-24

  preprintOpen accessSenior author

  As artificial intelligence increasingly automates decision-making in competitive markets, understanding the resulting dynamics and ensuring fair market mechanisms is essential. We investigate the multi-faceted decision-making of large language models (LLMs) in oligopolistic Cournot markets, showing that LLMs not only grasp complex market dynamics--demonstrating their potential as effective economic planning agents--but also engage in sustained tacit collusion, driving prices up to 200% above Nash equilibrium levels. Our analysis examines LLM behavior across three dimensions-(1) decision type, (2) opponent strategies, and (3) market composition--revealing how these factors may shape the competitiveness of LLM-based decision-makers. Furthermore, we show that regulating a few dominant agents by enforcing best-response strategies effectively disrupts collusion and helps restore competitive pricing. Our findings identify potential concerns associated with AI integration in competitive market environments and provide regulatory policy recommendations for the era of automation.

  PublisherDOI

• Strategic AI in Cournot Markets

  ArXiv.org · 2026-01-24

  articleOpen accessSenior author

  As artificial intelligence increasingly automates decision-making in competitive markets, understanding the resulting dynamics and ensuring fair market mechanisms is essential. We investigate the multi-faceted decision-making of large language models (LLMs) in oligopolistic Cournot markets, showing that LLMs not only grasp complex market dynamics--demonstrating their potential as effective economic planning agents--but also engage in sustained tacit collusion, driving prices up to 200% above Nash equilibrium levels. Our analysis examines LLM behavior across three dimensions-(1) decision type, (2) opponent strategies, and (3) market composition--revealing how these factors may shape the competitiveness of LLM-based decision-makers. Furthermore, we show that regulating a few dominant agents by enforcing best-response strategies effectively disrupts collusion and helps restore competitive pricing. Our findings identify potential concerns associated with AI integration in competitive market environments and provide regulatory policy recommendations for the era of automation.

  PublisherOA PDF

• Finite‐Horizon Behavior of the Periodically Observed Time‐Homogeneous Poisson Process

  Naval Research Logistics (NRL) · 2025-06-13

  articleOpen accessSenior author

  ABSTRACT When evaluating whether system arrivals can be characterized as a Poisson process, researchers draw conclusions from observed data. In practice, many systems are observed periodically (e.g., hourly, daily, monthly), which leads to discretization errors in the observed data. The periodically observed time‐homogeneous Poisson process (PTPP) model is an existing framework for capturing the long‐run effect of periodic observation on a Poisson process. This paper extends the PTPP model to capture the behavior of periodically observed Poisson systems over a finite number of events . This paper formulates the ‐step transition probabilities in an infinite‐state PTPP system and uses them to determine the PTPP's mixing time. The PTPP's interarrival distribution and the quality of its arrival rate moment estimator are evaluated as a function of . After extending the PTPP model for finite , the model's applicability is demonstrated through a case study of search‐and‐rescue operations in New York wilderness regions. The results suggest that five regions behave similarly to daily‐observed Poisson processes, which can inform practical resourcing decisions over extended periods (e.g., days, weeks, quarters).

  PublisherDOI

• Finding Shortest Flip Sequences Between Connected Graph Partitions

  INFORMS Journal on Optimization · 2025-12-12

  articleOpen accessSenior author

  Research story. The idea of finding a “path” between two district plans developed alongside the political redistricting compromise work in Dobbs et al. (2024a). After working with the transfer distance in Dobbs et al. (2024a) and reading about the existence of flip sequences on biconnected graphs in Akitaya et al. (2023), the authors were curious about the relationship between transfer distance and shortest flip sequences. The authors used this relationship to design two algorithms (one exact and one heuristic) that seek a shortest flip sequence between connected graph partitions.

  PublisherDOI

• Bias in the ballot: how votemandering exploits gerrymandering and campaign strategies

  Annals of Operations Research · 2025-08-06

  articleOpen accessSenior author

  Abstract Gerrymandering—the deliberate manipulation of electoral district boundaries for political advantage—is a persistent challenge in U.S. elections. In this work, we introduce and analyze Votemandering, a strategic blend of gerrymandering and targeted political campaigning devised to gain more seats by circumventing fairness measures. Votemandering leverages accurate demographic and socio-political data, bolstered by advancements in technology and data analytics, to influence voter decisions in pursuit of subtle gerrymandering strategies. We formulate votemandering as a Mixed Integer Program (MIP) that performs fairness-constrained gerrymandering over multiple election rounds. We analyze the influence of various redistricting constraints and parameters on votemandering efficacy. We explore the interconnectedness of gerrymandering, substantial campaign budgets, and strategic campaigning, illustrating their collective potential to generate biased electoral maps. A case study of Wisconsin State Senate redistricting reveals significant votemandering potential. Our findings underscore the need for reforms in the redistricting process beyond enforcing thresholds for specific fairness metrics.

  PublisherDOI

• A framework for analyzing the periodically-observed time-homogeneous Poisson process

  Journal of the Operational Research Society · 2025-03-27 · 2 citations

  articleSenior author
  PublisherDOI

Recent grants

• A Game Theoretic Approach to Pediatric Vaccine Pricing

  NSF · $368k · 2012–2017

• Collaborative Research: Pediatric Vaccine Formulary Optimization and Analysis

  NSF · $248k · 2005–2009

• Workshop: Setting a Broader Impact Innovation Roadmap; Arlington, Virginia; May 2016

  NSF · $58k · 2016–2018

• Collaborative Research: Aviation Access Control Security Systems

  NSF · $254k · 2001–2007

Frequent coauthors

• Edward C. Sewell

  Southern Illinois University Edwardsville

  55 shared

• Douglas M. King

  University of Illinois Urbana-Champaign

  30 shared

• Adrian Lee

  Illinois Institute of Technology

  25 shared

• John E. Kobza

  University of Tennessee at Knoxville

  23 shared

• Jason J. Sauppe

  University of Wisconsin–La Crosse

  20 shared

• Laura A. Albert

  Hospital Arnau de Vilanova

  19 shared

• Janet A. Jokela

  University of Illinois Urbana-Champaign

  18 shared

• Alexander Nikolaev

  17 shared

Education

• Ph.D., Computer Science

  University of Illinois at Urbana-Champaign

  1989

• M.S., Computer Science

  University of Illinois at Urbana-Champaign

  1985

• B.S., Mathematics

  University of Illinois at Urbana-Champaign

  1981

Awards & honors

• 2003 Guggenheim Fellowship
• 2018 INFORMS Impact Prize
• 2023 Clayton J. Thomas Award from the Military Operations Re…
• 2024 J. Steinhardt Prize from the INFORMS Military and Secur…
• George E. Kimball Medal (INFORMS) (2020)