Scaling Theory

Peter Howitt won the 2025 Nobel Prize in Economic Sciences. The award was for the theory of sustained growth through creative destruction. He and Philippe Aghion gave that theory a mathematical form in 1992.
Competition and innovation do not move together in a straight line. What decides the direction is where a firm sits relative to the technological frontier. Peter also draws the line between a merger that buys innovation and one that removes it, and he describes the route by which an incumbent turns market power into political power. We then turn to agent-based modeling. He is candid about why a method he still defends took thirty years to find readers.
Follow me on X (https://x.com/profschrepel) and BlueSky (@ProfSchrepel).
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References:
"A Model of Growth Through Creative Destruction" (with Aghion), Econometrica 60(2), March 1992, 323–351. https://www.jstor.org/stable/2951599.
Endogenous Growth Theory (with Aghion), MIT Press, 1998. https://mitpress.mit.edu/9780262528467/endogenous-growth-theory/
"Competition and Innovation: An Inverted-U Relationship" (with Aghion, Bloom, Blundell, Griffith), Quarterly Journal of Economics 120(2), May 2005, 701–728. https://academic.oup.com/qje/article-abstract/120/2/701/1933966.
"The Effects of Entry on Incumbent Innovation and Productivity" (with Aghion, Blundell, Griffith, Prantl), Review of Economics and Statistics 91(1), February 2009, 20–32. https://direct.mit.edu/rest/article/91/1/20/57753/The-Effects-of-Entry-on-Incumbent-Innovation-and.
"The Emergence of Economic Organization" (with Robert Clower), Journal of Economic Behavior & Organization 41(1), January 2000, 55–84. https://www.sciencedirect.com/science/article/abs/pii/S0167268199000876.
"Banks, Market Organization, and Macroeconomic Performance: An Agent-Based Computational Analysis" (with Ashraf and Gershman), JEBO 135, March 2017, 143–180. DOI 10.1016/j.jebo.2016.12.023.
"How Inflation Affects Macroeconomic Performance: An Agent-Based Computational Investigation" (with Ashraf and Gershman), Macroeconomic Dynamics 20(2), March 2016, 558–581. https://www.cambridge.org/core/journals/macroeconomic-dynamics/article/abs/how-inflation-affects-macroeconomic-performance-an-agentbased-computational-investigation/DC3AF3CA750F82FDE20164B36935FA1F.
"Getting at Systemic Risk via an Agent-Based Model of the Housing Market" (with Geanakoplos, Axtell, Farmer and others), American Economic Review 102(3), May 2012, 53–58. https://www.aeaweb.org/articles?id=10.1257/aer.102.3.53.

Some things change the world not because they are new, but because everyone learns them at once. That is the difference between mutual knowledge, where each of us knows something, and common knowledge, where each of us knows that the other knows, without end. It is the hidden machinery behind money, language, authority, and revolution.
Steven Pinker, professor of psychology at Harvard, joins Scaling Theory to discuss his latest book, “When Everyone Knows That Everyone Knows.” We move from a folk tale to game theory, from the evolution of altruism to the future of artificial agents. I read the book as a theory of scaling. A single mind can hold only a few layers of who knows what about whom, yet we coordinate in the millions. How we bridge that gap, and what happens to it in an age of fragmented media and machines that can model one another, is what I wanted to understand.

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.