In this episode we speak with Carlos Gershenson, SFI Sabbatical Visitor and professor of computer science at the Universidad Nacional Autónoma de México, where he leads the Self-organizing Systems Lab, among many other titles you can find in our show notes. For the next hour, we’ll discuss his decades of research and writing on a vast array of core complex systems concepts and their intersections with both Western and Eastern philosophical traditions — a first for this podcast.
Jim talks with Mirta Galesic about the ideas in her co-authored paper “Stewardship of Global Collective Behavior.” They discuss the meaning of collective behavior, a crisis in network structures, the analogy of the printing press, consequences of person-to-person communication, the capacity for collective forgetting, unpredictable developments in chatbots, bottom-up vs top-down influence, advertising-driven information ecosystems, emergent knobs in social media design, ChatGPT’s political bias, the widespread trust in algorithms, suggestions for reforming Twitter, information decay, viscosity, opportunities & dangers of mass surveillance data, the Twitter Files, free speech & cultural evolution, and much more.
Network Science Society Colloquium – January 25, 2023
Nicholas A. Christakis Experiments with Social Network Interventions
Abstract Human beings choose their friends, and often their neighbors and co-workers, and we inherit our relatives; and each of the people to whom we are connected also does the same, such that, in the end, we assemble ourselves into face-to-face social networks that obey particular mathematical and sociological rules. Why do we do this? And how might a deep understanding of human social network structure and function be used to intervene in the world to make it better? Here, I will review recent research from our lab describing three classes of interventions involving both offline and online networks: (1) interventions that rewire the connections between people; (2) interventions that manipulate social contagion, modifying the flow of desirable or undesirable properties; and (3) interventions that manipulate the positions of people within network structures. I will illustrate what can be done using a variety of experiments in settings as diverse as fostering cooperation or the diffusion of innovation in networked groups online, to fostering health behavior change in developing world villages and towns. I will also discuss recent experiments with “hybrid systems” comprised of both humans and artificial intelligence (AI) agents interacting in small groups. Overall, by taking account of people’s structural embeddedness in social networks, and by understanding social influence, it is possible to intervene in social systems to enhance desirable population-level properties as diverse as health, wealth, cooperation, coordination, and learning.
About the Speaker Nicholas A. Christakis, MD, PhD, MPH, is a social scientist and physician at Yale University who conducts research in the fields of network science, biosocial science, and behavior genetics. His current work focuses on how human biology and health affect, and are affected by, social interactions and social networks. He directs the Human Nature Lab and is the Co-Director of the Yale Institute for Network Science. He is the Sterling Professor of Social and Natural Science at Yale University, appointed in the Departments of Sociology; Medicine; Ecology and Evolutionary Biology; Biomedical Engineering; and the School of Management.
One of the things that make complexity science so fascinating is the diversity of the systems that it applies to. In this series so far, you’ve learnt about everything from ecologies to economies, tipping points in ecologies and economies, to power and influence in the 1400s, and even the spread of coronavirus in the lungs and the thing that brings all of these different topics together is complexity. This means that we can study one system to help us understand other systems — including bees.
In today’s episode, Orit Peleg, Faculty at the University of Colorado, Boulder, and External Faculty at the Santa Fe Institute, explains how bees self-organise and produce sophisticated behaviour. In this case, you’ll hear how thousands of bees can work out where their queen is at any given point.
Economic complexity methods have become popular tools in economic geography, international development, and innovation studies. Here, I review economic complexity theory and applications, with a particular focus on two streams of literature: the literature on relatedness, which focuses on the evolution of specialization patterns, and the literature on metrics of economic complexity, which uses dimensionality reduction techniques to create metrics of economic sophistication that are predictive of variations in income, economic growth, emissions, and income inequality.
Complexity Digest 