About

Douglas Richard Guilbeault is an Assistant Professor of Organizational Behavior at the Stanford Graduate School of Business. His academic appointment is within the field of Organizational Behavior, and he is involved in concentration advising in Computational Social Science. His research focus, background, and key contributions are not detailed in the provided page text.

Research topics

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

Selected publications

• A simple threshold captures the social learning of conventions

  Proceedings of the National Academy of Sciences · 2026-04-22

  articleOpen access1st authorCorresponding

  A persistent puzzle throughout the cognitive and social sciences is how people manage to learn social conventions from the sparse and noisy behavioral data of diverse actors, without explicit instruction. Here, we show that the dominant theories of social learning perform poorly at capturing how individuals learn conventions in coordination experiments that task them with matching their behaviors while interacting in social networks. Across experiments, participants' choices systematically deviate from both imitation and optimization. Instead, they follow a categorical, two-stage learning process: they behave probabilistically until they acquire enough information about each other to trigger a mental threshold and then their choices stabilize. We effectively estimate this threshold using the tolerance principle (TP), a parameter-free equation developed to model how children learn rules in language. We show that threshold-based agents produce social learning that is more accurate than imitating and optimizing agents, while also providing a better model of how a critical mass of dissenters can overturn conventions. The superior performance of our model holds when comparing against a variety of optimization approaches, including Bayesian inference. Furthermore, in a preregistered dyadic experiment requiring people to infer nonlinguistic behavioral patterns amid controlled levels of noise in observed signals, TP outperforms all other models at reproducing learning rates among human participants. These findings offer compelling evidence that a simple, mathematical threshold underlies individual and social learning, from grammatical rules to behavioral conventions.

  PublisherDOI

• A Simple Threshold Captures the Social Learning of Conventions

  2025-11-04

  articleOpen access1st authorCorresponding

  A persistent puzzle throughout the cognitive and social sciences is how people manage to learn social conventions from the sparse and noisy behavioral data of diverse actors, without explicit instruction. Here, we show that the dominant theories of social learning perform poorly at capturing how individuals learn conventions in coordination experiments that task them with matching their behaviors while interacting in social networks. Across experiments, participants' choices systematically deviate from both imitation and optimization. Instead, they follow a categorical, two-stage learning process: they behave probabilistically until they acquire enough information about each other to trigger a mental threshold and then their choices stabilize. We effectively estimate this threshold using the Tolerance Principle (TP), a parameter-free equation first developed to model how children learn rules in language. We show that threshold-based agents produce social learning that is more accurate than imitating and optimizing agents, while also providing a better model of how a critical mass of dissenters can overturn conventions. The superior performance of our model holds when comparing against a variety of optimization approaches, including Bayesian inference. Furthermore, in a preregistered dyadic experiment requiring people to infer non-linguistic behavioral patterns amid controlled levels of noise in observed signals, TP outperforms all other models at reproducing learning rates among human participants. These findings offer compelling evidence that a simple, mathematical threshold underlies individual and social learning, from grammatical rules to behavioral conventions.

  PublisherDOI

• Age and gender distortion in online media and large language models

  Nature · 2025-10-08 · 12 citations

  articleOpen access1st authorCorresponding

  Are widespread stereotypes accurate1–3 or socially distorted4–6? This continuing debate is limited by the lack of large-scale multimodal data on stereotypical associations and the inability to compare these to ground truth indicators. Here we overcame these challenges in the analysis of age-related gender bias7–9, for which age provides an objective anchor for evaluating stereotype accuracy. Despite there being no systematic age differences between women and men in the workforce according to the US Census, we found that women are represented as younger than men across occupations and social roles in nearly 1.4 million images and videos from Google, Wikipedia, IMDb, Flickr and YouTube, as well as in nine language models trained on billions of words from the internet. This age gap is the starkest for content depicting occupations with higher status and earnings. We demonstrate how mainstream algorithms amplify this bias. A nationally representative pre-registered experiment (n = 459) found that Googling images of occupations amplifies age-related gender bias in participants’ beliefs and hiring preferences. Furthermore, when generating and evaluating resumes, ChatGPT assumes that women are younger and less experienced, rating older male applicants as of higher quality. Our study shows how gender and age are jointly distorted throughout the internet and its mediating algorithms, thereby revealing critical challenges and opportunities in the fight against inequality. Stereotypes of age-related gender bias are socially distorted, as evidenced by the age gap in the representations of women and men across various media and algorithms, despite no systematic age differences in the workforce.

  PublisherOA PDFDOI

• The Power of Meta-Prediction Accuracy: Leveraging Predictions of Others' Predictions to Enhance Collective and Individual Intelligence

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Emergent Directedness in Social Contagion

  ArXiv.org · 2025-10-07

  preprintOpen accessSenior author

  An enduring challenge in contagion theory is that the pathways contagions follow through social networks exhibit emergent complexities that are difficult to predict using network structure. Here, we address this challenge by developing a causal modeling framework that (i) simulates the possible network pathways that emerge as contagions spread and (ii) identifies which edges and nodes are most impactful on diffusion across these possible pathways. This yields a surprising discovery. If people require exposure to multiple peers to adopt a contagion (a.k.a., 'complex contagions'), the pathways that emerge often only work in one direction. In fact, the more complex a contagion is, the more asymmetric its paths become. This emergent directedness problematizes canonical theories of how networks mediate contagion. Weak ties spanning network regions - widely thought to facilitate mutual influence and integration - prove to privilege the spread contagions from one community to the other. Emergent directedness also disproportionately channels complex contagions from the network periphery to the core, inverting standard centrality models. We demonstrate two practical applications. We show that emergent directedness accounts for unexplained nonlinearity in the effects of tie strength in a recent study of job diffusion over LinkedIn. Lastly, we show that network evolution is biased toward growing directed paths, but that cultural factors (e.g., triadic closure) can curtail this bias, with strategic implications for network building and behavioral interventions.

  PublisherOA PDFDOI

• Statistical or Embodied? Comparing Colorseeing, Colorblind, Painters, and Large Language Models in their Processing of Color Metaphors

  2025-06-11

  preprintOpen access

  Can metaphorical reasoning involving embodied experience—such as color perception—be learned from the statistics of language alone? Recent work finds that colorblind individuals robustly understand and reason abstractly about color, implying that color associations in everyday language might contribute to metaphorical understanding of color. However, it is unclear how much colorblind individuals’ understanding of color is driven by language versus their limited (but no less embodied) visual experience. A more direct test of whether language supports the acquisition of humans’ understanding of color is whether large language models (LLMs)—those trained purely on text with no visual experience—can nevertheless learn to generate consistent and coherent metaphorical responses about color. Here, we conduct pre-registered surveys that compare colorseeing adults, colorblind adults, and LLMs in how they (i) associate colors to words that lack established color associations and (ii) interpret conventional and novel color metaphors. Colorblind and colorseeing adults exhibited highly similar and replicable color associations with novel words and abstract concepts. Yet, while GPT (a popular LLM) also generated replicable color associations with impressive consistency, its associations departed considerably from colorseeing and colorblind participants. Moreover, GPT frequently failed to generate coherent responses about its own metaphorical color associations when asked to invert its color associations or explain novel color metaphors in context. Consistent with this view, painters who regularly work with color pigments were more likely than all other groups to understand novel color metaphors using embodied reasoning. Thus, embodied experience may play an important role in metaphorical reasoning about color and the generation of conceptual connections between embodied associations.

  PublisherOA PDFDOI

• Statistical or Embodied? Comparing Colorseeing, Colorblind, Painters, and Large Language Models in their Processing of Color Metaphors

  2025-07-07

  preprintOpen access

  Can metaphorical reasoning involving embodied experience—such as color perception—be learned from the statistics of language alone? Recent work finds that colorblind individuals robustly understand and reason abstractly about color, implying that color associations in everyday language might contribute to metaphorical understanding of color. However, it is unclear how much colorblind individuals’ understanding of color is driven by language versus their limited (but no less embodied) visual experience. A more direct test of whether language supports the acquisition of humans’ understanding of color is whether large language models (LLMs)—those trained purely on text with no visual experience—can nevertheless learn to generate consistent and coherent metaphorical responses about color. Here, we conduct pre-registered surveys that compare colorseeing adults, colorblind adults, and LLMs in how they (i) associate colors to words that lack established color associations and (ii) interpret conventional and novel color metaphors. Colorblind and colorseeing adults exhibited highly similar and replicable color associations with novel words and abstract concepts. Yet, while GPT (a popular LLM) also generated replicable color associations with impressive consistency, its associations departed considerably from colorseeing and colorblind participants. Moreover, GPT frequently failed to generate coherent responses about its own metaphorical color associations when asked to invert its color associations or explain novel color metaphors in context. Consistent with this view, painters who regularly work with color pigments were more likely than all other groups to understand novel color metaphors using embodied reasoning. Thus, embodied experience may play an important role in metaphorical reasoning about color and the generation of conceptual connections between embodied associations.

  PublisherOA PDFDOI

• A Simple Threshold Captures the Social Learning of Conventions

  2025-04-04

  preprintOpen access1st authorCorresponding

  A persistent puzzle throughout the cognitive and social sciences is how people manage to learn social conventions from the sparse and noisy behavioral data of diverse actors, without explicit instruction. Here, we show that the dominant theories of social learning perform poorly at capturing how individuals learn conventions in coordination experiments that task them with matching their behaviors while interacting in social networks. Across experiments, participants' choices systematically deviate from both imitation and optimization. Instead, they follow a categorical, two-stage learning process: they behave probabilistically until they acquire enough information about each other to trigger a mental threshold and then their choices stabilize. We effectively estimate this threshold using the Tolerance Principle (TP), a parameter-free equation first developed to model how children learn rules in language. We show that threshold-based agents produce social learning that is more accurate than imitating and optimizing agents, while also providing a better model of how a critical mass of dissenters can overturn conventions. The superior performance of our model holds when comparing against a variety of optimization approaches, including Bayesian inference. Furthermore, in a preregistered dyadic experiment requiring people to infer non-linguistic behavioral patterns amid controlled levels of noise in observed signals, TP outperforms all other models at reproducing learning rates among human participants. These findings offer compelling evidence that a simple, mathematical threshold underlies individual and social learning, from grammatical rules to behavioral conventions.

  PublisherOA PDFDOI

• Information Architectures: A Framework for Understanding Socio-Technical Systems

  2025-04-05

  preprintOpen access

  A sequence of technological inventions over several centuries has dramatically lowered the cost of producing and distributing information. Because societies ride on a substrate of information, these changes have profoundly impacted how we live, work, and interact. This paper explores the nature of information architectures (IAs)—the features that govern how information flows within human populations. IAs include physical and digital infrastructures, norms and institutions, and algorithmic technologies for filtering, producing, and disseminating information. IAs can reinforce societal biases and lead to prosocial outcomes as well as social ills. IAs have culturally evolved rapidly with human usage, creating new affordances and new problems for the dynamics of social interaction. We explore societal outcomes instigated by shifts in IAs and call for an enhanced understanding of the social implications of increasing IA complexity, the nature of competition among IAs, and the creation of mechanisms for the beneficial use of IAs.

  PublisherDOI

• The Managerialization of Everyday Life from 1950 to 2020

  Academy of Management Proceedings · 2025-07-01

  articleSenior author

  Market expansion is a fundamental characteristic of capitalist economies, with its cultural consequences often examined through the lens of commodification. This paper introduces managerialization—a novel channel through which market expansion shapes perceptions of everyday life. Managerialization is using management principles of control and optimization to interpret social domains traditionally outside business and organization. Using two computational approaches, we trace the historical evolution of managerialization through management metaphors in public discourse from 1950 to 2020. Our analysis reveals a consistent increase in managerialization across diverse sources, including newspapers, films, fiction, congressional speeches, and judicial decisions. We identify the 1980s as a critical turning point in the expansion of managerialization, coinciding with the rise of neoliberalism. We also find that managerialization is particularly prominent in domains such as emotion, body, and social relationships. Additionally, based on analysis of a contemporary interview dataset, we find that privileged social groups—young, educated, high-income white males—tend to adopt managerialization more frequently. Business education is also associated with a higher likelihood of individuals engaging in managerialization discourse. Our study provides empirical evidence of how economic institutions profoundly influence cultural discourse.

  PublisherDOI

Frequent coauthors

• Bhargav Srinivasa Desikan

  Institute for Public Policy Research

  109 shared

• Ethan O. Nadler

  109 shared

• Mark Chu

  Columbia University

  108 shared

• Elise Darragh-Ford

  SLAC National Accelerator Laboratory

  67 shared

• Donald Ruggiero Lo Sardo

  49 shared

• Tasker Hull

  41 shared

• Aabir Abubakar Kar

  Columbia University

  36 shared

• Joshua Becker

  10 shared

Awards & honors

• Stanford Honors Thesis Prizes - Symbolic Systems
• Glushko Prize for Excellence in Undergraduate Research in Sy…
• Barwise Award for Distinguished Contributions to Symbolic Sy…
• Symbolic Systems Distinguished Teaching Award