About

Carl Bergstrom is a Professor of Biology at the University of Washington and a member of the External Faculty at the Santa Fe Institute. His training is in evolutionary biology and mathematical population genetics, and he enjoys working across disciplines to integrate ideas from the natural and social sciences. His unifying research theme is the concept of information, focusing on how communication evolves and how evolution encodes information in genomes. Bergstrom uses mathematical models and computer simulations to study a wide range of problems in population biology, animal behavior, and evolutionary theory. His research efforts are concentrated in several areas, including the science of science, where he investigates how norms and institutions shape scientific knowledge and research strategies; the flow of information in biological systems, exploring how living organisms acquire, store, and use information, and the strategic aspects of communication; and the field of evolution and medicine, which seeks evolutionary explanations for human vulnerability to disease and the rapid evolution of pathogens and parasites. Bergstrom's work emphasizes the importance of information in understanding biological processes and the influence of social and scientific norms on knowledge production.

Research topics

• Computer Science
• Environmental health
• Medicine
• Data science
• Virology
• Sociology
• Telecommunications
• Artificial Intelligence
• Pathology
• Social Science
• Political Science
• Psychology
• Computer Security
• Biology
• Knowledge management
• Public relations
• Geography
• Business
• Pediatrics
• Internet privacy
• Emergency medicine
• Econometrics
• Economics
• Economic geography

Selected publications

• Humbled by Evolution

  American Scientist · 2026-01-01

  articleSenior author
  PublisherDOI

• Screening, sorting, and the feedback cycles that imperil peer review

  PLoS Biology · 2026-02-24 · 1 citations

  articleOpen access1st authorCorresponding

  Scholarly journals rely on peer review to identify the science most worthy of publication. Yet finding willing and qualified reviewers to evaluate manuscripts has become an increasingly challenging task, possibly even threatening the long-term viability of peer review as an institution. What can or should be done to salvage it? Here, we develop mathematical models to reveal the intricate interactions among incentives faced by authors, reviewers, and readers in their endeavors to identify the best science. Two facets are particularly salient. First, peer review partially reveals authors' private sense of their work's quality through their decisions of where to send their manuscripts. Second, journals' reliance on traditionally unpaid and largely unrewarded review labor deprives them of a standard market mechanism-wages-to recruit additional reviewers when review labor is in short supply. We highlight a resulting feedback loop that threatens to overwhelm the peer review system: (1) an increase in submissions overtaxes the pool of suitable peer reviewers; (2) the accuracy of review drops because journals must either solicit assistance from less qualified reviewers or ask current reviewers to do more; (3) as review accuracy drops, submissions further increase as more authors try their luck at venues that might otherwise be a stretch. We illustrate how this cycle is propelled by the increasing emphasis on high-impact publications, the proliferation of journals, and competition among these journals for peer reviews. Finally, we suggest interventions that could slow or even reverse this cycle of peer-review meltdown.

  PublisherDOI

• Industry Influence in High-Profile Social Media Research

  arXiv (Cornell University) · 2026-01-16

  preprintOpen accessSenior author

  To what extent is social media research independent from industry influence? Leveraging openly available data, we show that half of the research published in top journals has disclosable ties to industry in the form of prior funding, collaboration, or employment. However, the majority of these ties go undisclosed in the published research. These trends do not arise from broad scientific engagement with industry, but rather from a select group of scientists who maintain long-lasting relationships with industry. Undisclosed ties to industry are common not just among authors, but among reviewers and academic editors during manuscript evaluation. Further, industry-tied research garners more attention within the academy, among policymakers, on social media, and in the news. Finally, we find evidence that industry ties are associated with a topical focus away from impacts of platform-scale features. Together, these findings suggest industry influence in social media research is extensive, impactful, and often opaque. Going forward there is a need to strengthen disclosure norms and implement policies to ensure the visibility of independent research, and the integrity of industry supported research.

  PublisherDOI

• Industry Influence in High-Profile Social Media Research

  ArXiv.org · 2026-01-16

  articleOpen accessSenior author

  To what extent is social media research independent from industry influence? Leveraging openly available data, we show that half of the research published in top journals has disclosable ties to industry in the form of prior funding, collaboration, or employment. However, the majority of these ties go undisclosed in the published research. These trends do not arise from broad scientific engagement with industry, but rather from a select group of scientists who maintain long-lasting relationships with industry. Undisclosed ties to industry are common not just among authors, but among reviewers and academic editors during manuscript evaluation. Further, industry-tied research garners more attention within the academy, among policymakers, on social media, and in the news. Finally, we find evidence that industry ties are associated with a topical focus away from impacts of platform-scale features. Together, these findings suggest industry influence in social media research is extensive, impactful, and often opaque. Going forward there is a need to strengthen disclosure norms and implement policies to ensure the visibility of independent research, and the integrity of industry supported research.

  PublisherOA PDF

• Screening, sorting, and the feedback cycles that imperil peer review

  ArXiv.org · 2025-07-14

  preprintOpen access1st authorCorresponding

  Scholarly journals rely on peer review to identify the science most worthy of publication. Yet finding willing and qualified reviewers to evaluate manuscripts has become an increasingly challenging task, possibly even threatening the long-term viability of peer review as an institution. What can or should be done to salvage it? Here, we develop mathematical models to reveal the intricate interactions among incentives faced by authors, reviewers, and readers in their endeavors to identify the best science. Two facets are particularly salient. First, peer review partially reveals authors' private sense of their work's quality through their decisions of where to send their manuscripts. Second, journals' reliance on traditionally unpaid and largely unrewarded review labor deprives them of a standard market mechanism -- wages -- to recruit additional reviewers when review labor is in short supply. We highlight a resulting feedback loop that threatens to overwhelm the peer review system: (1) an increase in submissions overtaxes the pool of suitable peer reviewers; (2) the accuracy of review drops because journals either must either solicit assistance from less qualified reviewers or ask current reviewers to do more; (3) as review accuracy drops, submissions further increase as more authors try their luck at venues that might otherwise be a stretch. We illustrate how this cycle is propelled by the increasing emphasis on high-impact publications, the proliferation of journals, and competition among these journals for peer reviews. Finally, we suggest interventions that could slow or even reverse this cycle of peer-review meltdown.

  PublisherOA PDFDOI

• AI, peer review and the human activity of science

  Nature · 2025-06-25 · 15 citations

  article1st authorCorresponding
  PublisherDOI

• How should the advancement of large language models affect the practice of science?

  Universität Zürich, ZORA · 2025-01-27

  articleOpen access

  Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advancement of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate. Schulz et al. make the argument that working with LLMs is not fundamentally different from working with human collaborators, while Bender et al. argue that LLMs are often misused and overhyped, and that their limitations warrant a focus on more specialized, easily interpretable tools. Marelli et al. emphasize the importance of transparent attribution and responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans should retain responsibility for determining the scientific roadmap. To facilitate the discussion, the four perspectives are complemented with a response from each group. By putting these different perspectives in conversation, we aim to bring attention to important considerations within the academic community regarding the adoption of LLMs and their impact on both current and future scientific practices.

  PublisherDOI

• How competition propels scientific risk-taking

  ArXiv.org · 2025-09-08

  preprintOpen accessSenior author

  In science as elsewhere, attention is a limited resource and scientists compete with one another to produce the most exciting, novel and impactful results. We develop a game-theoretic model to explore how such competition influences the degree of risk that scientists are willing to embrace in their research endeavors. We find that competition for scarce resources -- for example, publications in elite journals, prestigious prizes, and faculty jobs -- motivates scientific risk-taking and may be important in counterbalancing other incentives that favor cautious, incremental science. Even small amounts of competition induce substantial risk-taking. Moreover, we find that in an ``opt-in'' contest, increasing the stakes induces increased participation -- which crowds the contest and further impels entrants to pursue higher-risk, higher-return investigations. The model also illuminates a source of tension in academic training and collaboration. Researchers at different career stages differ in their need to amass accomplishments that distinguish them from their peers, and therefore may not agree on what degree of risk to accept.

  PublisherOA PDFDOI

• How should the advancement of large language models affect the practice of science?

  Proceedings of the National Academy of Sciences · 2025-01-27 · 47 citations

  articleOpen access

  Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advancement of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate. Schulz et al. make the argument that working with LLMs is not fundamentally different from working with human collaborators, while Bender et al. argue that LLMs are often misused and overhyped, and that their limitations warrant a focus on more specialized, easily interpretable tools. Marelli et al. emphasize the importance of transparent attribution and responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans should retain responsibility for determining the scientific roadmap. To facilitate the discussion, the four perspectives are complemented with a response from each group. By putting these different perspectives in conversation, we aim to bring attention to important considerations within the academic community regarding the adoption of LLMs and their impact on both current and future scientific practices.

  PublisherOA PDFDOI

• Partial honesty in a hummingbird polymorphism provides evidence for a hybrid equilibrium

  Animal Behaviour · 2025-02-19 · 3 citations

  article
  PublisherDOI

Recent grants

• Collaborative Research: How do publication and funding filters shape the science that we do, and how we learn from it?

  NSF · $75k · 2020–2022

• TLS: COLLABORATIVE RESEARCH: Tracking Scientific Innovation from Usage Data: Models and Tools to Support a Science of Science

  NSF · $217k · 2009–2013

• Collaborative Research: Dynamic Perspectives on Costs and Conflict in Signaling Interactions

  NSF · $230k · 2010–2015

Frequent coauthors

• Michael Lachmann

  Santa Fe Institute

  46 shared

• Jevin D. West

  44 shared

• Martin Rosvall

  27 shared

• Rustom Antia

  Emory University

  23 shared

• Théodore C. Bergstrom

  20 shared

• Kevin Gross

  North Carolina State University

  18 shared

• Tali Magidson

  University of Washington

  16 shared

• Marc Lipsitch

  13 shared

Education

• Ph.D., Evolutionary Biology

  University of California, Berkeley

  1996

• M.S., Evolutionary Biology

  University of California, Berkeley

  1992

• B.A., Zoology

  University of California, Santa Barbara

  1989