Scaling Theory

Welcome back to Scaling Theory. In this episode, I speak with Matthew O. Jackson, the William D. Eberle Professor of Economics at Stanford University and an external faculty member at the Santa Fe Institute. Matthew is one of the founders of the modern economics of networks and the author of The Human Network and Social and Economic Networks.
We talk about the friendship paradox, why homophily slows how fast a society learns the truth but helps niche ideas catch fire, and the gossip study where villagers in southern India proved remarkably good at naming the most central spreaders in their community. We then turn to AI agents as a different species: Turing tests on LLMs, the steerability of agent personas through system prompts, and what to make of Moltbook, the social network for AI agents.
By the end, you will know why telling students how much their peers actually drink reduces binge drinking more than warning them about the dangers of alcohol, why the same network can spread a virus quickly and a belief slowly, and why AI agents change their behavior when asked to explain it.
Papers and works referenced in the conversation
Books
The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors — Matthew O. Jackson (Pantheon, 2019). https://web.stanford.edu/~jacksonm/books.html
Social and Economic Networks — Matthew O. Jackson (Princeton University Press, 2008). https://web.stanford.edu/~jacksonm/books.html
Part I — The scaling of human networks
"Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily" — Matthew O. Jackson and Dunia López-Pintado, Network Science 1(1), 2013. https://arxiv.org/abs/1111.0073
"How Homophily Affects the Speed of Learning and Best-Response Dynamics" — Benjamin Golub and Matthew O. Jackson, Quarterly Journal of Economics 127(3), 2012. https://web.stanford.edu/~jacksonm/homophily.pdf
"Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials" — Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, and Matthew O. Jackson, Review of Economic Studies 86(6), 2019. https://academic.oup.com/restud/article/86/6/2453/5345571
"Empathy and Well-Being Correlate with Centrality in Different Social Networks" — Sylvia A. Morelli, Desmond C. Ong, Rucha Makati, Matthew O. Jackson, and Jamil Zaki, PNAS 114(37), 2017. https://www.pnas.org/doi/10.1073/pnas.1702155114
Part II — The scaling of AI agents
"Inequality's Economic and Social Roots: The Role of Social Networks and Homophily" — Matthew O. Jackson, in Advances in Economics and Econometrics: Twelfth World Congress of the Econometric Society (Cambridge University Press, 2025). https://arxiv.org/abs/2506.13016
"AI Behavioral Science" — Jackson, Mei, Wang, Xie, Yuan, Benzell, Brynjolfsson, Camerer, Evans, Jabarian, Kleinberg, Meng, Mullainathan, Ozdaglar, Pfeiffer, Tennenholtz, Willer, Yang, and Ye, arXiv 2509.13323, 2025. https://arxiv.org/abs/2509.13323
"A Turing Test of Whether AI Chatbots Are Behaviorally Similar to Humans" — Qiaozhu Mei, Yutong Xie, Walter Yuan, and Matthew O. Jackson, PNAS 121(9), 2024. https://www.pnas.org/doi/10.1073/pnas.2313925121

Welcome back to Scaling Theory. My guest today is Albert-László Barabási, Professor of Network Science at Northeastern University and one of the most cited scientists alive with over 320 000 citations. His books include Linked, The Formula, and Network Science.
In 1999, Albert-László Barabási published a paper that changed how we understand networks. The finding was this: real-world networks are not random. They are dominated by hubs. A few nodes collect most of the links, and they do so because they already have them. In this episode, he explains the details of what he actually found. We then move to the scaling of networks, and the temptation to control them. We conclude with a discussion about art, ballet dancers, architecture, and what mapping careers across disciplines reveals about how networks really work.
You can follow me on X (@ProfSchrepel) and BlueSky (@ProfSchrepel).
References:
➝ Papers
Barabási, A.-L. & Albert, R. "Emergence of Scaling in Random Networks." Science 286, no. 5439 (1999): 509–512. https://doi.org/10.1126/science.286.5439.509
Albert, R., Jeong, H. & Barabási, A.-L. "Diameter of the World-Wide Web." Nature 401 (1999): 130–131. https://doi.org/10.1038/43601
Watts, D.J. & Strogatz, S.H. "Collective Dynamics of 'Small-World' Networks." Nature 393 (1998): 440–442. https://doi.org/10.1038/30918
Erdős, P. & Rényi, A. "On Random Graphs." Publicationes Mathematicae 6 (1959): 290–297. https://snap.stanford.edu/class/cs224w-readings/erdos59random.pdf
➝ Books
Barabási, A.-L. Linked: The New Science of Networks. Cambridge, MA: Perseus Publishing, 2002. https://en.wikipedia.org/wiki/Linked:_The_New_Science_of_Networks
Barabási, A.-L. Network Science. Cambridge: Cambridge University Press, 2016. https://networksciencebook.com (open access)
Barabási, A.-L. The Formula: The Universal Laws of Success. New York: Little, Brown and Company, 2018. https://www.hachettebookgroup.com/titles/albert-laszlo-barabasi/the-formula/9780316505499

Welcome back to scaling theory. My guest today is ⁠Scott E. Page⁠, Distinguished University Professor of Complexity, Social Science, and Management at the University of Michigan, and an external faculty member at the Santa Fe Institute. He is an elected member of the National Academy of Sciences and the American Academy of Arts and Sciences, and a recipient of the Guggenheim Fellowship. His books include The Difference, Diversity and Complexity, The Diversity Bonus, and The Model Thinker.
In this episode of Scaling Theory, Scott walks us through what complexity actually is. He unpacks the difference between complicated and genuinely complex systems, explains why cognitively diverse teams systematically outperform homogeneous ones on complex tasks, and what that means for how organizations scale. We also take up path dependence, the spillover effects of overlapping games across platform ecosystems, and where complexity tools have changed real decisions in practice. We close on the single open problem whose resolution would most reshape our understanding of social systems. As you will hear, Scott’s thinking is exceptionally clear. It is always a pleasure to talk with him and to listen to his insights. I hope you enjoy our discussion.
You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠).
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Books
Page, S.E. (2007). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton University Press.
Page, S.E. (2011). Diversity and Complexity. Princeton University Press (Primers in Complex Systems).
Page, S.E. (2018). The Model Thinker: What You Need to Know to Make Data Work for You. Basic Books.
Miller, J.H. and Page, S.E. (2007). Complex Adaptive Social Systems: An Introduction to Computational Models of Social Life. Princeton University Press.
Peer-reviewed articles
Hong, L. and Page, S.E. (2004). "Groups of diverse problem solvers can outperform groups of high-ability problem solvers." Proceedings of the National Academy of Sciences, 101(46): 16385–16389.
Page, S.E. (2006). "Path Dependence." Quarterly Journal of Political Science, 1(1): 87–115.
Page, S.E. (2007). "Type Interactions and the Rule of Six." Economic Theory, 30(2): 223–241.
Bednar, J. and Page, S.E. (2007). "Can Game(s) Theory Explain Culture? The Emergence of Cultural Behavior Within Multiple Games." Rationality and Society, 19(1): 65–97.
Bednar, J., Bramson, A., Jones-Rooy, A. and Page, S.E. (2010). "Emergent Cultural Signatures and Persistent Diversity: A Model of Conformity and Consistency." Rationality and Society, 22(4): 407–444.

My guest today is Cass R. Sunstein, University Professor at Harvard and one of the most influential legal and political thinkers of our time. A prolific author of dozens of books and hundreds of academic articles, Cass has shaped debates in constitutional law, administrative law, behavioral economics, and public policy. He is regularly ranked amongst the very top of the most cited legal scholars alive. Cass also served as Administrator of the White House Office of Information and Regulatory Affairs under President Obama. He has advised governments and international organizations around the world, and was awarded the Holberg Prize, the equivalent of a Nobel in law and the humanities.
His latest book, On Liberalism: In Defense of Freedom, is a systematic defense of the liberal tradition at a moment when it is, as he shows, under unprecedented pressure. Our conversation is centered around his book. We begin with the urgency at the heart of the book: how liberalism confronts critiques from moral conservatives and egalitarian progressives alike, what it means to defend the liberal framework in an era of fragmentation, etc. We then turn to questions of scaling: does liberalism have internal patterns or institutional mechanisms that allow it to scale across diverse societies. We grapple with how the liberal tradition’s “big tent” of thinkers (from Mill and Hayek to Roosevelt’s Second Bill of Rights) impact liberalism ability to scale. We also explore how liberalism navigates technological change, expertise versus public accountability, and the pretence of knowledge. I hope you enjoy our discussion.
You can follow me on X (@⁠⁠ProfSchrepel⁠⁠) and BlueSky (@⁠⁠ProfSchrepel⁠⁠).
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References:
On Liberalism (MIT Press, 2025) https://mitpress.mit.edu/9780262049771/on-liberalism/

In the very first episode of Scaling Theory, I mentioned a few scientists who have shaped my understanding of the world. At the very top of that list is today’s guest: W. Brian Arthur.
Brian was born and raised in Belfast, Northern Ireland, and went on to become one of the most important figures of complexity science. Today, he is widely known as the father of complexity economics, a field that has transformed how we think about the evolution of modern economies.
His influence is remarkable. Brian’s work has been cited more than 58,000 times according to Google Scholar. He received numerous awards and recognition, such as being the inaugural laureate of the Lagrange Prize in Complexity Science, an award that many have described as complexity’s equivalent of the Nobel Prize. Brian has been, at age 37, the youngest endowed chair holder at Stanford University. He went on to work for my institutions, including the Santa Fe Institute, as we will talk about. On a personal note, I consider Brian a friend.
Now, what makes me especially happy to have Brian on the podcast is the unique perspective he brings on how economies form and evolve. His understanding of technology, how it emerges and scales, offers a lens that none others have developed. It is a way of seeing economic life as something alive. Be ready to be blown away.
You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠).
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References:
W. Brian Arthur, Competing Technologies, Increasing Returns, and Lock-In by Historical Events (1989) https://www.rochelleterman.com/ir/sites/default/files/arthur 1989.pdf
W. Brian Arthur, Foundations of Complexity Economics (2021) https://pmc.ncbi.nlm.nih.gov/articles/PMC7844781/pdf/42254_2020_Article_273.pdf
W. Brian Arthur, The Nature of Technology: What It Is and How It Evolves (2009)
W. Brian Arthur, Economics in Nouns and Verbs (2023) https://www.sciencedirect.com/science/article/pii/S0167268122003936
Thibault Schrepel, The Evolution of Economies, Technologies, and Other Institutions: Exploring W. Brian Arthur's Insights (2024) https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8809341E2E94D76B8CCAB4A4DDACBC4C/S1744137424000067a.pdf/evolution_of_economies_technologies_and_other_institutions_exploring_w_brian_arthurs_insights.pdf