Simon DeDeo

Associate Professor Carnegie Mellon University

  • Pittsburgh PA

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

Contact

Carnegie Mellon University

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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

Theoretical Physics
Social and Decision Sciences
Artificial Intelligence
Astrophysics
Applied Mathematics

Media Appearances

‘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.

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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."

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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.”

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Industry Expertise

Research
Education/Learning

Accomplishments

Foundational Questions Institute Essay Prize

2018

Cozzarelli Prize for best Behavioral Sciences paper

2018

Proceedings of the National Academy of Sciences

Education

Cambridge University

M.A.

Applied Mathematics and Theoretical Physics

2001

Harvard University

A.B.

Astrophysics

2000

Princeton University

Ph.D.

Astrophysics

2006

Event Appearances

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

Statistical Inference of Online Radicalization in Extremist Communities

Dietrich College Senior Honors Program

2021

Foundations and Applications of Cultural Analytics in the Humanities

National Endowment for the Humanities

2020

The Role of Information in Structured Conflict

Army Research Office

2017

Articles

Inferring Cultural Landscapes with the Inverse Ising Model

Entropy

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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