Simon DeDeo
Associate Professor | Carnegie Mellon University
Pittsburgh, PA, UNITED STATES
Simon DeDeo conducts empirical investigations, and builds mathematical theories, of both historical and contemporary phenomena.
Biography
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
Astrophysics
Applied Mathematics
Media Appearances (5)
‘We risk being ruled by dangerous binaries’ – Mohsin Hamid on our increasing polarisation
The Guardian online
2022-07-30
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.
What makes an explanation good enough?
Phys.org online
2021-01-14
"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."
Neuroscientist: The Mind Is More Than a Machine — Or Is It?
Mind Matters online
2022-06-12
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.”
Computing Crime and Punishment
The New York Times online
2014-06-16
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.
Machine learning can offer new tools, fresh insights for the humanities
Ars Technica online
2019-01-03
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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Industry Expertise (2)
Research
Education/Learning
Accomplishments (2)
Foundational Questions Institute Essay Prize (professional)
2018
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
Links (7)
Event Appearances (3)
Tacit Knowledge
Brunel Centre for Culture & Evolution Brunel University, London, UK
2022-01-26
Consilience and the Cognitive Science of Scientific Explanation
Center for the Philosophy of Science University of Pittsburgh
2021-11-16
The Cognitive Science of Conspiracy
Bavarian Academy of Sciences
2022-04-29
Research Grants (3)
Statistical Inference of Online Radicalization in Extremist Communities
Dietrich College Senior Honors Program $6,500
2021
Foundations and Applications of Cultural Analytics in the Humanities
National Endowment for the Humanities $229,639
2020
The Role of Information in Structured Conflict
Army Research Office $350,698
2017
Articles (5)
Inferring Cultural Landscapes with the Inverse Ising Model
Entropy2023 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.
Cognitive Attractors and the Cultural Evolution of Religion
Proceedings of the Annual Meeting of the Cognitive Science Society2023 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.
The cultural transmission of tacit knowledge
Journal of the Royal Society Interface2022 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.
One Fee, Two Fees; Red Fee, Blue Fee: People Use the Valence of Others’ Speech in Social Relational Judgments
Social Cognition2022 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).
Learning Communicative Acts in Children's Conversations: A Hidden Topic Markov Model Analysis of the CHILDES Corpora
Topics in Cognitive Science2021 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.
