Welcome back to Scaling Theory. My guest today is Cristina Bicchieri, Professor of Social Thought and Comparative Ethics at the University of Pennsylvania, Director of the Center for Social Norms and Behavioral Dynamics, and one of the most influential scholars working on norm formation and collective behaviour. Her work is widely cited and, as we will talk about, has led to many field experiments and changes across the world.

In our conversation, Cristina and I talk about how norms emerge, scale, and sometimes collapse. We look at tipping signals, self-reinforcing equilibria, and why some norms spread through a population while others fail beyond small groups.

We then move to applied dimensions. Cristina takes us through the field experiments she has conducted and the patterns she has observed. Her work shows how norms and legal rules evolve together, which offers a fresh perspective on the forces that regulate and constrain much of what we do. We also talk about the challenges that appear when behaviours on digital platforms and AI ecosystems evolve faster than regulation. Finally, Cristina offers concrete guidance for policymakers and firms that want to design interventions grounded in norm theory.

I hope you will enjoy the conversation. Keep scaling, keep skating on thin ice.

You can follow me on X (@⁠⁠⁠ProfSchrepel⁠⁠⁠) and BlueSky (@⁠⁠⁠ProfSchrepel⁠⁠⁠).

Welcome back to Scaling Theory. My guest today is Robin Hanson, Associate Professor of economics at George Mason University. Robin has long been one of the most original thinkers on institutional design, collective intelligence, as explored in his books The Age of Em and The Elephant in the Brain. Across his career, he has pushed the boundaries of how societies can aggregate knowledge and make collective decisions when complexity scales faster than comprehension.

In this episode, Robin and I discuss how futarchy could scale that logic across our societies? As societies grow larger, representation, information, and incentives all begin to break down, and futarchy is one possible way to rebuild them. Robin and I talk about where this idea has been tested so far, what a real-world implementation might look like in a city or company, and why, despite its promise, futarchy hasn’t yet scaled.

Finally, we explore how new technologies like blockchain and AI might change the picture, whether they’ll make futarchy more viable, or perhaps even replace parts of it. And we look ahead to Robin’s vision from The Age of Em. When societies become unimaginably fast and complex, which human institutions survive, and which ones don’t?

You can follow me on X (@⁠⁠ProfSchrepel⁠⁠) and BlueSky (@⁠⁠ProfSchrepel⁠⁠).

Welcome back to Scaling Theory. My guest today is Robin Hanson, Associate Professor of economics at George Mason University. Robin has long been one of the most original thinkers on institutional design, collective intelligence, as explored in his books The Age of Em and The Elephant in the Brain. Across his career, he has pushed the boundaries of how societies can aggregate knowledge and make collective decisions when complexity scales faster than comprehension.

In this episode, Robin and I discuss how futarchy could scale that logic across our societies? As societies grow larger, representation, information, and incentives all begin to break down, and futarchy is one possible way to rebuild them. Robin and I talk about where this idea has been tested so far, what a real-world implementation might look like in a city or company, and why, despite its promise, futarchy hasn’t yet scaled.

Finally, we explore how new technologies like blockchain and AI might change the picture, whether they’ll make futarchy more viable, or perhaps even replace parts of it. And we look ahead to Robin’s vision from The Age of Em. When societies become unimaginably fast and complex, which human institutions survive, and which ones don’t?

You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠).

This is the first solo episode of Scaling Theory, where I take a deep dive into the literature. Building on a working paper titled “Adaptive Regulation,” I explore why “future-proof” laws so often fail in the face of rapid technological change, and how complexity science can guide us toward rules that adapt to the things they regulate. Drawing on recent EU digital acts and voices from law, economics, and complexity theory, I sketch the contours of a regulatory system that scales.

You can follow me on X (@⁠⁠ProfSchrepel⁠⁠) and BlueSky (@⁠⁠ProfSchrepel⁠⁠).

References:

• Schrepel, T., Adaptive Regulation (2025) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5416454
• Ranchordás, S., & Van‘t Schip, M. (2020). Future-Proofing Legislation for the Digital Age. In Time, Law, and Change: An Interdisciplinary Study.
• Colomo, P. I. (2022). Future-Proof Regulation against the Test of Time: The Evolution of European Telecommunications Regulation. Oxford Journal of Legal Studies, 42(4).
• Chander, A. (2017). Future-proofing law. UC Davis Law Review.
• Powell, W. W., & Snellman, K. (2004). The Knowledge Economy. Annual Review of Sociology, 30.
• Perez, C. (2009). The Double Bubble at the Turn of the Century: Technological Roots and Structural Implications. Cambridge Journal of Economics, 33(4), 779–805.
• Allen, D. W., Berg, C., & Potts, J. (2025). Institutional Acceleration: The Consequences of Technological Change in a Digital Economy. Cambridge University Press.
• Colander, D., Holt, R. P. F., & Rosser, J. B. (2004). The Changing Face of Mainstream Economics. Review of Political Economy, 16(4).
• Arthur, W. B. (2009). The Nature of Technology: What It Is and How It Evolves. New York: Free Press.

• Buchanan, J. M., & Tullock, G. (1962). The Calculus of Consent: Logical Foundations of Constitutional Democracy. University of Michigan Press.
• Sowell, T. (2007). A Conflict of Visions: Ideological Origins of Political Struggles.
• West, G. (2017). Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies. Penguin Press.

My guest today is Vinton G. Cerf, widely regarded as a “father of the Internet.” In the 1970s, Vint co-developed the TCP/IP protocols that define how data is formatted, transmitted, and received across devices. In essence, his work enabled networks to communicate, thus laying the foundation for the Internet as a unified global system. He has received honorary degrees and awards that include the National Medal of Technology, the Turing Award, the Presidential Medal of Freedom, the Marconi Prize, and membership in the National Academy of Engineering. He is currently Chief Internet Evangelist at Google.

In this episode, Vint reflects on the Internet’s path from ARPANET and TCP/IP to the scaling choices that made global connectivity possible. He explains why decentralization was key, and how fiber optics and data centers underwrote explosive growth. Vint also addresses today’s policy anxieties (fragmentation, sovereignty walls, and fragile infrastructures…) before looking upward to the interplanetary Internet now linking spacecraft. Finally, we turn to AI: how LLMs are reshaping learning and software, and why the next leap may be systems that question us back. I hope you enjoy our discussion.

You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠).

My guest today is Melanie Moses, a Professor of Computer Science at the University of New Mexico, an External Faculty at the Santa Fe Institute, and Chair of the New Mexico AI Consortium. Melanie's work spans a wide range of disciplines all unified by her deep understanding of complexity theory.

In our conversation, Melanie and I explore how scaling theory reveals surprising patterns across nature, technology, and society. We discuss what decentralized systems like ant colonies can teach us about building more robust AI, and what the immune system tells us about information networks. We also delve into the costs of building scalable infrastructure, and why we might need new approaches to governance that can scale with our global challenges. Finally, we explore whether there could ever be a universal scaling law and what young researchers should know about pursuing interdisciplinary paths. I hope you enjoy our discussion.

You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠).

References:

• Melanie Moses’ Biological Computation Lab https://moseslab.cs.unm.edu
• Metabolic Scaling From Individuals to Societies (PhD, 1993) https://www.unm.edu/~melaniem/DISSERTATION_MEM.pdf
• Cities as Organisms: Allometric Scaling of Urban Road Networks (2008) https://www.jtlu.org/index.php/jtlu/article/view/29
• Biologically inspired design principles for Scalable, Robust, Adaptive, Decentralized search and automated response (RADAR) (2011) https://ieeexplore.ieee.org/document/5954663

My guest today is Melanie Mitchell, a Professor at the Santa Fe Institute, author of "Complexity: A Guided Tour" and "Artificial Intelligence: A Guide for Thinking Humans." Melanie studied under the legendary John Holland and has become one of the leading voices bridging complexity science with research in artificial intelligence.

In our conversation, Melanie and I explore the fundamental nature of intelligence and why today's AI systems might not be as intelligent as they appear. We discuss the persistent misunderstandings around modern AI, the concept of "jagged intelligence," and why the Turing Test is misleading us. We also talk about embodiment, metacognition, and how complexity science principles like emergence could reshape our approach to building truly intelligent machines. Finally, we delve into what biology can teach us about creating more sustainable and genuinely intelligent artificial systems. I hope you enjoy our discussion.

You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠).

Welcome back to Scaling Theory. Today, we are taking on a surprising but deeply relevant topic: religion. We are not entering the realm of theology, but rather looking at religion the way an economist might look at a multinational corporation or a digital platform. Think of it this way: in the U.S. alone, faith-based organizations generate more annual revenue than Apple and Microsoft combined. So when we ask how religions scale, we are really asking how some of the world’s most enduring (and powerful) institutions grow, adapt, and persist.

Our guest is Paul Seabright, Professor of Economics at the Toulouse School of Economics and author of The Divine Economy: How Religions Compete for Wealth, Power, and People.

Paul and I talk about how religions scale, why rituals, doctrines, and compelling narratives matter for growth. We explore how religions act as multi-sided platforms, how they build robust networks that resist churn, and how technologies like the printing press and social media can reshape their reach. Toward the end, we explore whether new movements in the Silicon Valley function like new religions, and what their chances of success might be in today’s competitive market for belief. I hope you enjoy our discussion.

You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠) to receive regular updates.

Today’s episode is different from all the previous ones, as for the first time on Scaling Theory, we focus on research methodology, exploring how AI is reshaping the very process of doing research and what that shift means for science and society at large.

I sat down with James Evans, Professor of Sociology, Computational and Data Science at the University of Chicago, External Professor at the Santa Fe Institute, and Faculty Member at the Complexity Science Hub in Vienna, to explore how AI is transforming the way we simulate, scale, and understand human behavior, and what that shift means for science and society.

We dive into his pioneering work on using large language models to simulate individuals, societies, and entire social systems. James and I explore the strengths and limits of AI agents for both the social and hard sciences before reflecting on the future of social science itself. We talk about research centers entirely run by AI and conferences conducted by AI agents, without any human involvement. We also discuss the role of small research teams in disruptive innovation, and how to cultivate proximity and serendipity in a research world where we increasingly cooperate with machines.

You can follow me on X (@ProfSchrepel) and BlueSky (@ProfSchrepel) to receive regular updates.

References:

- Simulating Subjects: The Promise and Peril of AI Stand-ins for Social Agents and Interactions (2025) https://osf.io/preprints/socarxiv/vp3j2_v3

- LLM Social Simulations Are a Promising Research Method (2025) https://arxiv.org/pdf/2504.02234

- Large teams develop and small teams disrupt science and technology (2019) https://www.nature.com/articles/s41586-019-0941-9?wpisrc=

- AI Expands Scientists' Impact but Contracts Science's Focus (2024) https://arxiv.org/abs/2412.07727

- The Paradox of Collective Certainty in Science (2024) https://arxiv.org/html/2406.05809v1?utm_source=chatgpt.com

- Being Together in Place as a Catalyst for Scientific Advance (Research Policy, 2023) https://www.sciencedirect.com/science/article/pii/S0048733323001956

My guest today is Eric von Hippel, Professor of Technological Innovation at the MIT Sloan School of Management. Eric is the author of numerous academic articles and books, including Free Innovation, Democratizing Innovation, and The Sources of Innovation, all published by MIT Press and available for free. Eric has accumulated over 90,000 citations on Google Scholar and has received many awards, including the Schumpeter School Prize (2017)—a particularly interesting recognition given his work on non-Schumpeterian innovation.

In our conversation, Eric and I explore the role of free innovation in today’s economy. Eric highlights some of his favorite examples of free innovation and discusses how, despite being developed at personal cost, it is scaling at an impressive rate. We explore the mechanisms that best enable this scaling—whether through recognition, institutional support, IP protections, or alternative incentives. By the end of this talk, you will understand what free innovation is, how it develops, and how it interacts with producer innovation.

You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠) to receive regular updates.

References:

Sources of Innovation (1988) https://web.mit.edu/evhippel/www-old/books/sources/SofI.pdf

Democratizing Innovation (2005) https://direct.mit.edu/books/book-pdf/2425023/book_9780262285636.pdf