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Simon DeDeo - Carnegie Mellon University. Pittsburgh, PA, US

Simon DeDeo

Associate Professor | Carnegie Mellon University


Simon DeDeo conducts empirical investigations, and builds mathematical theories, of both historical and contemporary phenomena.


Simon DeDeo is an associate professor in Social and Decision Sciences at Carnegie Mellon University, and External Professor at the Santa Fe Institute. He was previously affiliated with Complex Systems and the Cognitive Science Program at Indiana University. He has also held post-doctoral fellowships at the Institute for Physics and Mathematics of the Universe at the University of Tokyo and at the Kavli Institute for Cosmological Physics at the University of Chicago.

At the Laboratory for Social Minds Simon DeDeo undertakes empirical investigations, and builds mathematical theories, of both historical and contemporary phenomena. They range from the centuries-long timescales of cultural evolution to the second-by-second emergence of social hierarchy in the non-human animals, from the editors of Wikipedia to the French Revolution to the gas stations of Indiana. DeDeo's lab creates synthetic, deep-time accounts of major transitions in political order, with the goal of the predicting and understanding our species’ future.

Areas of Expertise (5)

Theoretical Physics

Social and Decision Sciences

Artificial Intelligence


Applied Mathematics

Media Appearances (5)

‘We risk being ruled by dangerous binaries’ – Mohsin Hamid on our increasing polarisation

The Guardian  online


In 2017, I published my fourth novel, Exit West, and bought a small notebook to jot down ideas for the next one. I thought it would be about technology. I came across an article by Simon DeDeo, an assistant professor at Carnegie Mellon University, discussing an experiment he and his colleague John Miller had conducted in that same year. They simulated cooperation and competition by machines over many generations, building these machines as computer models and setting them playing a game together. An interesting pattern emerged.

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What makes an explanation good enough?

Phys.org  online


"If you look at the biggest and most divisive arguments we're having right now," says Simon DeDeo, SFI External Professor and Carnegie Mellon University Professor, "we often agree on the facts. We disagree on the explanations."

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Neuroscientist: The Mind Is More Than a Machine — Or Is It?

Mind Matters  online


Could self-reference be the missing puzzle piece that allows for truly intelligent AIs, and maybe even someday sentient machines? Only time will tell, but Simon DeDeo, a complexity scientist at Carnegie Mellon University and the Santa Fe Institute, seems to think so: “Great progress in physics came from taking relativity seriously. We ought to expect something similar here: Success in the project of general artificial intelligence may require we take seriously the relativity implied by self-reference.”

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Computing Crime and Punishment

The New York Times  online


Scientists have now carried out a computational analysis of those words showing how the British justice system created new practices for controlling violence. The study, “The Civilizing Process in London’s Old Bailey,” in The Proceedings of the National Academy of Sciences, is a collaboration between two computer scientists, Simon DeDeo of Indiana University and Sara Klingenstein of the Santa Fe Institute, and a historian, Tim Hitchcock of the University of Sussex in England.

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Machine learning can offer new tools, fresh insights for the humanities

Ars Technica  online


Specifically, rhetorical innovations by key influential figures (like Robespierre) played a critical role in persuading others to accept what were, at the time, audacious principles of governance, according to co-author Simon DeDeo, a former physicist who now applies mathematical techniques to the study of historical and current cultural phenomena. And the cutting-edge machine learning methods he developed to reach that conclusion are now being employed by other scholars of history and literature.

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Simon DeDeo: Satisfying Explanations and Meaningful Arguments AppSciComm Scientist Talks: Cultural Landscapes with Simon DeDeo Mindscape 150 | Simon DeDeo on How Explanations Work and Why They Sometimes Fail Simon DeDeo on Good Explanations & Diseases of Epistemology Sense-making at Scale! | Simon DeDeo


307: The Science Behind the Formation and Future of Human Societies - Dr. Simon DeDeo EP1 Simon DeDeo – The Evolution of Consciousness

Industry Expertise (2)



Accomplishments (2)

Foundational Questions Institute Essay Prize (professional)


Cozzarelli Prize for best Behavioral Sciences paper (professional)

2018 Proceedings of the National Academy of Sciences

Education (3)

Cambridge University: M.A., Applied Mathematics and Theoretical Physics 2001

Harvard University: A.B., Astrophysics 2000

Princeton University: Ph.D., Astrophysics 2006

Event Appearances (3)

Tacit Knowledge

Brunel Centre for Culture & Evolution  Brunel University, London, UK


Consilience and the Cognitive Science of Scientific Explanation

Center for the Philosophy of Science  University of Pittsburgh


The Cognitive Science of Conspiracy

Bavarian Academy of Sciences  


Research Grants (3)

Statistical Inference of Online Radicalization in Extremist Communities

Dietrich College Senior Honors Program $6,500


Foundations and Applications of Cultural Analytics in the Humanities

National Endowment for the Humanities $229,639


The Role of Information in Structured Conflict

Army Research Office $350,698


Articles (5)

Inferring Cultural Landscapes with the Inverse Ising Model


2023 The space of possible human cultures is vast, but some cultural configurations are more consistent with cognitive and social constraints than others. This leads to a “landscape” of possibilities that our species has explored over millennia of cultural evolution. However, what does this fitness landscape, which constrains and guides cultural evolution, look like? The machine-learning algorithms that can answer these questions are typically developed for large-scale datasets.

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Cognitive Attractors and the Cultural Evolution of Religion

Proceedings of the Annual Meeting of the Cognitive Science Society

2023 We use data on a cultural fitness landscape, recently inferred from a large-scale cross-cultural survey of religious practices (6000+ years, 407 cultures), to provide new insights into the dynamics of cultural macroevolution. We report three main results. First, we observe an emergent distinction between the long-run fitness of a religious practice, and its short-term stability: in particular, some low-fitness practices are nonetheless highly stable.

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The cultural transmission of tacit knowledge

Journal of the Royal Society Interface

2022 A wide variety of cultural practices have a ‘tacit’ dimension, whose principles are neither obvious to an observer, nor known explicitly by experts. This poses a problem for cultural evolution: if beginners cannot spot the principles to imitate, and experts cannot say what they are doing, how can tacit knowledge pass from generation to generation? We present a domain-general model of ‘tacit teaching’, drawn from statistical physics, that shows how high-accuracy transmission of tacit knowledge is possible. It applies when the practice’s underlying features are subject to interacting and competing constraints.

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One Fee, Two Fees; Red Fee, Blue Fee: People Use the Valence of Others’ Speech in Social Relational Judgments

Social Cognition

2022 We present an empirical demonstration that people rely on linguistic valence as a direct cue to a speaker’s group membership. Members of the U.S. voting public judge positive words as more likely to be spoken by members of their political in-group, and negative words as more likely to be spoken by members of their political out-group (three studies with 655 participants). We further find that participants perceive pluralized forms of nouns as more extremely valenced than singular forms (one study with 280 participants).

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Learning Communicative Acts in Children's Conversations: A Hidden Topic Markov Model Analysis of the CHILDES Corpora

Topics in Cognitive Science

2021 Over their first years of life, children learn not just the words of their native languages, but how to use them to communicate. Because manual annotation of communicative intent does not scale to large corpora, our understanding of communicative act development is limited to case studies of a few children at a few time points. We present an approach to automatic identification of communicative acts using a hidden topic Markov model, applying it to the conversations of English-learning children in the CHILDES database.

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