About

Damon Centola, Ph.D., is the Elihu Katz Professor of Communication, Sociology, and Engineering at the University of Pennsylvania. He serves as the Director of the Network Dynamics Group and is a Senior Fellow at the Leonard Davis Institute of Health Economics. His research centers on social networks and behavior change, exploring how ideas, behaviors, and social norms spread within populations. Centola's work has contributed to understanding the mechanisms of diffusion in online and offline networks, and he has developed methods to promote diffusion in online networks, including inventing a patented approach. He has received numerous scientific awards, such as the Goodman Prize for Outstanding Contributions to Sociological Methodology, the James Coleman Award for Outstanding Research in Rationality and Society, and the Harrison White Award for Outstanding Scholarly Book. Centola was a developer of the NetLogo agent-based modeling environment and is a member of the Sci Foo community and a fellow of the Center for Advanced Study in the Behavioral Sciences at Stanford University. His research has been widely covered in major media outlets, and he has worked with various organizations including Amazon, Apple, Cigna, General Motors, Microsoft, and the U.S. Army. He is also a series editor for Princeton University Press and author of books on social contagions and behavior change.

Research topics

• Computer Science
• Data science
• Data Mining
• Social psychology
• World Wide Web
• Psychology
• Engineering
• Management science
• Internet privacy
• Statistics
• Theoretical computer science
• Computer network
• Physics
• Combinatorics
• Biology
• Mathematics

Selected publications

• Experimental Evidence that Social Learning in Structured Information-Sharing Networks Corrects Anchoring Bias

  Journal of Social Computing · 2026-03-01

  articleOpen accessSenior author

  The anchoring effect is a cognitive bias in which exposure to arbitrary initial values disproportionately influences estimates. While this effect has been widely documented, its role in social contexts remains unclear. Laboratory studies of isolated individuals often show stronger anchoring effects than naturalistic settings where people interact in social networks. We propose that social networks can amplify or attenuate anchoring depending on whether anchors are “helpful” or “harmful” for accuracy. In a preregistered experiment with 1600 participants across 40 groups, we compared individuals working independently to those embedded in peer-to-peer information-sharing networks. Networked groups improved overall belief accuracy, showing truth-seeking reductions in bias from harmful anchors, while also exhibiting truth-seeking increases in anchoring bias from helpful anchors. By contrast, isolated individuals showed no endogenous change in anchoring in either case. Our analyses identify a psychological mechanism that may underlie these effects: “confidence in others”, rather than self-confidence.

  PublisherDOI

• Physician clinical decision modification and bias assessment in a randomized controlled trial of AI assistance

  Communications Medicine · 2025-03-04 · 28 citations

  articleOpen access

  BACKGROUND: Artificial intelligence assistance in clinical decision making shows promise, but concerns exist about potential exacerbation of demographic biases in healthcare. This study aims to evaluate how physician clinical decisions and biases are influenced by AI assistance in a chest pain triage scenario. METHODS: A randomized, pre post-intervention study was conducted with 50 US-licensed physicians who reviewed standardized chest pain video vignettes featuring either a white male or Black female patient. Participants answered clinical questions about triage, risk assessment, and treatment before and after receiving GPT-4 generated recommendations. Clinical decision accuracy was evaluated against evidence-based guidelines. RESULTS: Here we show that physicians are willing to modify their clinical decisions based on GPT-4 assistance, leading to improved accuracy scores from 47% to 65% in the white male patient group and 63% to 80% in the Black female patient group. The accuracy improvement occurs without introducing or exacerbating demographic biases, with both groups showing similar magnitudes of improvement (18%). A post-study survey indicates that 90% of physicians expect AI tools to play a significant role in future clinical decision making. CONCLUSIONS: Physician clinical decision making can be augmented by AI assistance while maintaining equitable care across patient demographics. These findings suggest a path forward for AI clinical decision support that improves medical care without amplifying healthcare disparities.

  PublisherOA PDFDOI

• The Paradox of Behaviour Change and the Science of Network Diffusion

  2024-01-01 · 2 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• ChatGPT Influence on Medical Decision-Making, Bias, and Equity: A Randomized Study of Clinicians Evaluating Clinical Vignettes

  medRxiv · 2023-11-27 · 33 citations

  preprintOpen access

  In a randomized, pre-post intervention study, we evaluated the influence of a large language model (LLM) generative AI system on accuracy of physician decision-making and bias in healthcare. 50 US-licensed physicians reviewed a video clinical vignette, featuring actors representing different demographics (a White male or a Black female) with chest pain. Participants were asked to answer clinical questions around triage, risk, and treatment based on these vignettes, then asked to reconsider after receiving advice generated by ChatGPT+ (GPT4). The primary outcome was the accuracy of clinical decisions based on pre-established evidence-based guidelines. Results showed that physicians are willing to change their initial clinical impressions given AI assistance, and that this led to a significant improvement in clinical decision-making accuracy in a chest pain evaluation scenario without introducing or exacerbating existing race or gender biases. A survey of physician participants indicates that the majority expect LLM tools to play a significant role in clinical decision making.

  PublisherOA PDFDOI

• Experimental evidence for structured information–sharing networks reducing medical errors

  Proceedings of the National Academy of Sciences · 2023-07-24 · 10 citations

  articleOpen access1st authorCorresponding

  Errors in clinical decision-making are disturbingly common. Recent studies have found that 10 to 15% of all clinical decisions regarding diagnoses and treatment are inaccurate. Here, we experimentally study the ability of structured information-sharing networks among clinicians to improve clinicians' diagnostic accuracy and treatment decisions. We use a pool of 2,941 practicing clinicians recruited from around the United States to conduct 84 independent group-level trials, ranging across seven different clinical vignettes for topics known to exhibit high rates of diagnostic or treatment error (e.g., acute cardiac events, geriatric care, low back pain, and diabetes-related cardiovascular illness prevention). We compare collective performance in structured information-sharing networks to collective performance in independent control groups, and find that networks significantly reduce clinical errors, and improve treatment recommendations, as compared to control groups of independent clinicians engaged in isolated reflection. Our results show that these improvements are not a result of simple regression to the group mean. Instead, we find that within structured information-sharing networks, the worst clinicians improved significantly while the best clinicians did not decrease in quality. These findings offer implications for the use of social network technologies to reduce errors among clinicians.

  PublisherDOI

• The network science of collective intelligence

  Trends in Cognitive Sciences · 2022-09-27 · 72 citations

  review1st authorCorresponding
  PublisherDOI

• Scaling Up Change: A Critical Review and Practical Guide to Harnessing Social Norms for Climate Action

  Psychological Science in the Public Interest · 2022 · 267 citations

  • Psychology
  • Management science
  • Social psychology

  Anthropogenic carbon emissions have the potential to trigger changes in climate and ecosystems that would be catastrophic for the well-being of humans and other species. Widespread shifts in production and consumption patterns are urgently needed to address climate change. Although transnational agreements and national policy are necessary for a transition to a fully decarbonized global economy, fluctuating political priorities and lobbying by vested interests have slowed these efforts. Against this backdrop, bottom-up pressure from social movements and shifting social norms may offer a complementary path to a more sustainable economy. Furthermore, norm change may be an important component of decarbonization policies by accelerating or strengthening the impacts of other demand-side measures. Individual actions and policy support are social processes-they are intimately linked to expectations about the actions and beliefs of others. Although prevailing social norms often reinforce the status quo and unsustainable development pathways, social dynamics can also create widespread and rapid shifts in cultural values and practices, including increasing pressure on politicians to enact ambitious policy. We synthesize literature on social-norm influence, measurement, and change from the perspectives of psychology, anthropology, sociology, and economics. We discuss the opportunities and challenges for the use of social-norm and social-tipping interventions to promote climate action. Social-norm interventions aimed at addressing climate change or other social dilemmas are promising but no panacea. They require in-depth contextual knowledge, ethical consideration, and situation-specific tailoring and testing to understand whether they can be effectively implemented at scale. Our review aims to provide practitioners with insights and tools to reflect on the promises and pitfalls of such interventions in diverse contexts.

  PublisherDOI

• Experimental evidence for scale-induced category convergence across populations

  Nature Communications · 2021-01-12 · 41 citations

  articleOpen accessSenior author

  Individuals vary widely in how they categorize novel and ambiguous phenomena. This individual variation has led influential theories in cognitive and social science to suggest that communication in large social groups introduces path dependence in category formation, which is expected to lead separate populations toward divergent cultural trajectories. Yet, anthropological data indicates that large, independent societies consistently arrive at highly similar category systems across a range of topics. How is it possible for diverse populations, consisting of individuals with significant variation in how they categorize the world, to independently construct similar category systems? Here, we investigate this puzzle experimentally by creating an online "Grouping Game" in which we observe how people in small and large populations collaboratively construct category systems for a continuum of ambiguous stimuli. We find that solitary individuals and small groups produce highly divergent category systems; however, across independent trials with unique participants, large populations consistently converge on highly similar category systems. A formal model of critical mass dynamics in social networks accurately predicts this process of scale-induced category convergence. Our findings show how large communication networks can filter lexical diversity among individuals to produce replicable society-level patterns, yielding unexpected implications for cultural evolution.

  PublisherOA PDFDOI

• The reduction of race and gender bias in clinical treatment recommendations using clinician peer networks in an experimental setting

  Nature Communications · 2021-11-15 · 43 citations

  articleOpen access1st authorCorresponding

  Bias in clinical practice, in particular in relation to race and gender, is a persistent cause of healthcare disparities. We investigated the potential of a peer-network approach to reduce bias in medical treatment decisions within an experimental setting. We created "egalitarian" information exchange networks among practicing clinicians who provided recommendations for the clinical management of patient scenarios, presented via standardized patient videos of actors portraying patients with cardiac chest pain. The videos, which were standardized for relevant clinical factors, presented either a white male actor or Black female actor of similar age, wearing the same attire and in the same clinical setting, portraying a patient with clinically significant chest pain symptoms. We found significant disparities in the treatment recommendations given to the white male patient-actor and Black female patient-actor, which when translated into real clinical scenarios would result in the Black female patient being significantly more likely to receive unsafe undertreatment, rather than the guideline-recommended treatment. In the experimental control group, clinicians who were asked to independently reflect on the standardized patient videos did not show any significant reduction in bias. However, clinicians who exchanged real-time information in structured peer networks significantly improved their clinical accuracy and showed no bias in their final recommendations. The findings indicate that clinician network interventions might be used in healthcare settings to reduce significant disparities in patient treatment.

  PublisherOA PDFDOI

• Topological measures for identifying and predicting the spread of complex contagions

  Nature Communications · 2021 · 133 citations

  Senior authorCorresponding
  • Computer Science
  • Data Mining
  • Computer Science

  The standard measure of distance in social networks - average shortest path length - assumes a model of "simple" contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are "complex" contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.

  PublisherOA PDFDOI

Recent grants

• Influencing cervical cancer prevention and detection online through social media

  NIH · $1.3M · 2014–2018

Frequent coauthors

• Jingwen Zhang

  10 shared

• Douglas Guilbeault

  10 shared

• Michael W. Macy

  Cornell University

  8 shared

• Joshua Becker

  8 shared

• Devon Brackbill

  University of Pennsylvania

  7 shared

• Vı́ctor M. Eguı́luz

  7 shared

• Sijia Yang

  North China University of Technology

  6 shared

• M. San Miguel

  5 shared

Awards & honors

• Goodman Prize for Outstanding Contributions to Sociological…
• James Coleman Award for Outstanding Research in Rationality…
• Harrison White Award for Outstanding Scholarly Book (2019)
• Guggenheim Fellow (2026)