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I'm a professor at Carnegie Mellon University, where I run the Laboratory for Social Minds, and external faculty at the Santa Fe Institute.
We study cultural flourishing. How do people organize themselves to do great things: build nations, create art, start businesses, or discover natural and moral truths? We know a lot about the microevolution of culture, but much less about the big macroevoutionary phenomena that alter the very basis of human life. Simple stories about "memes" are totally inadequate to capture the complex, transformational effects of participatory democracy or the scientific method.
More formally, we ask questions about cultural evolution and the emergence of complex societies and practices through large-scale, computational analysis of archives. At the same time, we build models of cognition, often with a basis in information theory or machine learning, and we then test our theories of culture through laboratory experiments.
Our data science work spans the last few centuries of the human species. We've worked on speeches from the French Revolution (1789–1791), court trials from the British Common Law system (1780-1913), the information foraging habits of Charles Darwin (1836–1859), the emergence of scientific innovation in the Royal Society (1665–1996), finite state machines for cooperation and conflict and norms on Wikipedia (2001–), emergent civil society in China (1980–), empathy and relating on Mumsnet (2001–), online radicalization and the incels movement in Reddit (2017–), the Socratic dialogues (-399–-387), Poetry Magazine (1913–2001), the Serbian Parliament (1996–), and the US Congressional Record (1996–). And probably a few others that we've completely forgotten about (sorry!)
Our theoretical work began with analogies from statistical mechanics, but we're primarily interested now in the phenomena of unsupervised clustering, lossy compression, rate distortion theory, sparse coding and compressed sensing, and the information bottleneck. Also, one of our members is writing code to evolve Python programs but he promises not to trigger the AI singularity. Another is interested in applying theories from the thermodynamics of computation to economics. Sometimes we get punchy. We really, really love Kullback-Leibler divergence.
Our psychological work tries to connect our data science findings to lived human experience. We are now running parallel tests on our information-theoretic tools to see the extent to which they capture human judgement. With Liz Hobson (SFI) and Dan Mønster (Aarhus), we are using online games to study loop closure, downwards causation, and the use of global and local information in decision-making. And with Fritz Breithaupt (IU, Experimental Humanities Lab), we are starting our first tests using his Serial Narratives system on MTurk.
We're always looking for new collaborators, and cultural analysis is a young field with an enormous amount to play with. I've collaborated with a number of CSSS students over the years; many of them are now faculty, and some have tenure!
I built my transdisciplinary career as an Omidyar Fellow at SFI; my early career, as a Ph.D. at Princeton University and a Kavli Fellow at the University of Chicago, was as a theoretical physicist and cosmologist. I'm happy to talk about the transition.