About

My research focuses on understanding social behavior using mathematics, especially using game theory and computer simulation models. Use these techniques to understand all types of social behavior from bacteria to communities of scientists.

Research topics

• Computer Science
• Mathematics
• Thermodynamics
• Epistemology
• Mechanical engineering
• Engineering
• Physics
• Sociology
• Information Retrieval
• Meteorology
• Geography
• Social Science
• Artificial Intelligence
• Political Science
• Data science
• Quantum mechanics
• World Wide Web
• Biology
• Psychology
• Law
• Economics
• Cognitive psychology
• Philosophy
• Management science

Selected publications

• Against theory-motivated experimentation: Can random experimental choice lead to better theories?

  Collective Intelligence · 2026-01-01

  articleOpen accessSenior author

  Scientists must choose which among many experiments to perform. We study the epistemic success of experimental choice strategies proposed by philosophers of science or executed by scientists themselves. We develop a multi-agent model of the scientific process that jointly formalizes its core aspects: active experimentation, theorizing, and social learning. We find that agents who choose new experiments at random develop the most informative and predictive theories of the world. The agents aiming to confirm, falsify theories, or resolve theoretical disagreements end up with an illusion of epistemic success: they develop promising accounts for the data they collected, while misrepresenting the ground truth that they intended to learn about. Agents experimenting in these theory-motivated ways acquire less diverse or less representative samples from the ground truth that also turn out to be easier to account for. Random data collection, on the other hand, combines virtues of diverse and representative sampling from a target scientific domain which enables cumulative development of the successful theoretical accounts of it. We suggest that randomization, already a gold standard within experiments, is also beneficial at the level of experiments themselves.

  PublisherDOI

• Reflection, introspection, and book

  Philosophy and Phenomenological Research · 2025-09-05

  articleOpen access1st authorCorresponding

  Abstract The much‐debated Reflection principle states that a coherent agent's credences must match their estimates for their future credences. Defenders claim that there are Dutch‐book arguments in its favor, putting it on the same normative footing as probabilistic coherence. Critics claim that those arguments rely on the implicit, implausible assumption that the agent is introspective : that they are certain what their own credences are. In this paper, we clarify this debate by surveying several different conceptions of the book scenario. We show that the crucial disagreement hinges on whether agents who are not introspective are known to reliably act on their credences: if they are , then coherent Reflection failures are (at best) ephemeral; if they are not , then Reflection failures can be robust—and perhaps rational and coherent. We argue that the crucial question for future debates is which notion of coherence makes sense for such unreliable agents and sketch a few avenues to explore.

  PublisherOA PDFDOI

• Supplementary information from The Evolution of Scientific Credit: When Authorship Norms Impede Collaboration

  Open MIND · 2025-01-01

  articleSenior author

  Detailed information on mathematical methods

  DOI

• The Evolution of Scientific Credit: When Authorship Norms Impede Collaboration

  PhilSci-Archive (University of Pittsburgh) · 2025-07-10

  preprintOpen accessSenior author

  Scientific authorship norms vary dramatically across disciplines, from contribution-sensitive systems where first author is the greatest contributor and subsequent author order reflects relative input, to contribution-insensitive conventions like alphabetical ordering or senior-author-last. We develop evolutionary game-theoretic models to examine both how these divergent norms emerge and their subsequent effects on collaborative behavior. Our first model reveals that contribution-insensitive norms evolve when researchers who sacrifice positional advantage face the strongest adaptive pressure -- for example senior authors managing larger collaboration portfolios or bearing heavier reputational stakes. This "Red King" dynamic potentially explains why fields in which senior researchers command large labs, major grants, and extensive collaboration portfolios may paradoxically evolve conventions that favour junior-author positioning. Our second model demonstrates that established norms influence researchers' willingness to collaborate, with contribution-sensitive norms consistently outperforming insensitive alternatives in fostering successful partnerships. Contribution-insensitive norms create systematic coordination failures through two mechanisms: "main contributor resentment" when exceptional work goes unrecognized, and "second contributor resentment" when comparable efforts receive unequal credit. These findings suggest that widely adopted practices like senior-last positioning and alphabetical ordering may function as institutional frictions that impede valuable scientific collaborations rather than neutral organizational conventions, potentially reducing overall scientific productivity across affected disciplines.

  OA PDFDOI

• Supplementary information from The Evolution of Scientific Credit: When Authorship Norms Impede Collaboration

  Figshare · 2025-01-01

  articleOpen accessSenior author

  Detailed information on mathematical methods

  PublisherDOI

• How Explanations Evolve: Social Learning and the Explore–Exploit Trade-off

  2025-08-05

  preprintOpen access

  Explanations are often social objects, and when people try to explain something, they usually have on hand the explanations that others have tried before. We present a simple theory of how people use the explanations they encounter as clues to the broader landscape of possible explanations, which informs decisions to either exploit what has already been done or, conversely, to strike out and explore new possibilities. The challenge of coming up with novel explanations draws people to exploit or imitate appealing ones they have already encountered; this draw increases as less appealing alternatives become more distant (the "strawman" effect). Conversely, when presented with distinct, high-quality explanations, people can merge them, recombining the appealing features in new ways. We test and confirm these predictions using a two-stage transmission chain experiment. In Stage One, 101 participants were asked to explain an unusual scenario. In Stage Two, a separate group of 983 participants were asked to explain the same scenario after reading pairs of explanations drawn from Stage One. We found that participants in Stage Two were more likely to exploit or copy explanations when at least one was high in quality and particularly when it was paired with a weaker alternative. In contrast, participants were more likely to explore or deviate from prior explanations when the two explanations were both high-quality and conceptually distinct. Our work provides new insight into how collective exploration can be promoted, or stalled, by implicit information about what is yet to be discovered.

  PublisherDOI

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

  Animal Behaviour · 2025-02-19 · 3 citations

  article
  PublisherDOI

• Against theory-motivated experimentation in science

  2025-10-29

  articleOpen accessSenior author

  Scientists must choose which among many experiments to perform. We study the epistemic success of experimental choice strategies proposed by philosophers of science or executed by scientists themselves. We develop a multi-agent model of the scientific process that jointly formalizes its core aspects: active experimentation, theorizing, and social learning. We find that agents who choose new experiments at random develop the most accurate theories of the world. The agents aiming to confirm, falsify theories, or resolve theoretical disagreements end up with an illusion of epistemic success: they develop promising accounts for the data they collected, while completely misrepresenting the ground truth that they intended to learn about. Agents experimenting in theory-motivated ways acquire less diverse or less representative samples from the ground truth that also turn out to be easier to account for. Random data collection, on the other hand, combines virtues of diverse and representative sampling from a target scientific domain which enables cumulative development of the successful theoretical accounts of it. We suggest that randomization, already a gold standard within experiments, is also beneficial at the level of experiments themselves.

  PublisherDOI

• Exploring the importance of stochasticity to hybrid equilibria in a discrete signaling game

  Journal of Evolutionary Biology · 2024-11-11 · 1 citations

  article

  Communication via evolved signals is ubiquitous (both within and between species) in the natural world. However, how honest we should expect signals to be remains an open question. Hybrid equilibria are a form of equilibria predicted by discrete signaling games in which signalers are sometimes dishonest and signals do not completely reliably convey information on signaler quality. While these equilibria have been theoretically demonstrated in several signaling games, their dynamics in a stochastic simulation of evolutionary trajectories (that include representation of the inherent noise expected in evolution in the natural world) have not previously been studied. In this paper, we present an agent-based simulation of a discrete signaling game which exhibits hybrid equilibria. We find that while hybrid equilibria are evolutionarily attractive where they exist, populations exhibit variable and often drastic oscillating behavior around the predicted equilibrium values. We discuss how these dynamics might offer valuable opportunity for detecting hybrid equilibria in natural populations.

  PublisherDOI

• Cascades, Leaps, and Strawmen: How Explanations Evolve

  2024-03-12 · 1 citations

  preprintOpen access

  Explanations are social, and when people try to explain something, they usually seek input from others. We present a simple theory of how people use the explanations they encounter as both aids to sense-making and clues to the broader landscape of possible explanations. The computational challenge of coming up with novel explanations draws people to imitate appealing ones (information cascades); this draw increases as less appealing alternatives become more distant (the ``strawman'' effect). Conversely, pairs of divergent high-quality explanations can lead to merging and syncretism, and pairs of low-quality explanations to long-leap exploration. We then use a transmission-chain experiment to test, and confirm, these predictions, although, intriguingly, we find little evidence that the imitation that good explanations can trigger actually leads to better outcomes. Our work provides new insight into how collective exploration can be promoted, or stalled, both by what has been found, and by implicit information about what is yet to be discovered.

  PublisherOA PDFDOI

Recent grants

• CAREER: Incentives, Diversity, and Scientific Problem Choice

  NSF · $406k · 2013–2019

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

  NSF · $191k · 2010–2014

• Scholars Award: Simulating Science

  NSF · $165k · 2010–2012

Frequent coauthors

• Brian Skyrms

  370 shared

• Michela Massimi

  369 shared

• John Dupré

  University of Exeter

  369 shared

• Marcel Weber

  Cambridge University Press

  364 shared

• Chris Smeenk

  University of Edinburgh

  364 shared

• Stephan Hartmann

  Ludwig-Maximilians-Universität München

  364 shared

• Mckenzie Alexander

  University of California, San Diego

  364 shared

• Doreen Fraser

  364 shared

Labs

• Institute for Complex Social DynamicsPI

  Specializing in game theory, social dynamics, and more.