Carlos Gershenson on Balance, Criticality, Antifragility, and The Philosophy of Complex Systems

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.

Listen at: complexity.simplecast.com

Mirta Galesic on Global Collective Behavior

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.

Listen at: jimruttshow.blubrry.net

A Law for Irreversible Thermodynamics? Synergy Increases Free Energy by Decreasing Entropy

Klaus Jaffe

Classical laws of thermodynamics apply only for reversible processes. Most processes however are irreversible and occur in open systems. That is the case of synergy that emerges from synchronized reciprocal positive feedback loops between a network of diverse actors. For this process to proceed, compatible information from different sources synchronically coordinates the actions of the actors resulting in a nonlinear increase in the useful work or potential energy the system can manage. In contrast noise is produced when incompatible information is mixed. This synergy produced from the coordination of different agents achieves non-linear gains in free energy and in information (negentropy). Free energy can be estimated by proxies such as individual autonomy of an organism, emancipation from the environment, productivity, efficiency, capacity for flexibility, self-regulation, and self-control of behavior; whereas entropy, or the lack of it, is revealed by the degree of synchronized division of ever more specialized labor, structural complexity, information, and dissipation of energy. Empirical examples that provide quantitative data for these phenomena are presented. Results show that increases in free energy density are concomitant with decreases in entropy density. This may be a rule for synergistic processes in irreversible thermodynamics, which is consilient with the first and second laws of classical thermodynamics. Under this light, biological evolution is the task of self reproducing irreversible synergistic system to discover empirically (through natural and sexual selection) types of order that increase their free energy.

Read the full article at: www.qeios.com

Economic Fitness and Complexity Spring School. June 5-9, 2023, Rome.

The Enrico Fermi Research Centre – CREF (Rome) and UNU-MERIT (Maastricht), in collaboration with the Young Scholar Initiative of the Institute of New Economic Thinking are now calling for submissions to the first Economic Fitness and Complexity spring school, which will be held on June 5-9, 2023 in Rome, Italy. The school is an extensive introduction to the economic complexity framework, with theoretical and practical lessons. The first three days will focus on theoretical and practical classes covering the following topics: economic complexity measurement, network theory, machine learning, measurement of relatedness. Theoretical lectures will be followed by coding labs, where participants will have the chance to apply the methodologies introduced in class, and to carry out assigned group projects, focusing on the school core themes. For the last two days of the school, many world-wide renowned scholars have been invited to present their frontier research linking economic complexity with economic development, economic geography, labour economics, sustainability, economics of science, and innovation policy. Visit the website to submit an application: https://efc-school.cref.it/apply 

Postdoctoral research associate on Spreading phenomena on geometric networks

Employer: Rényi Institute of Mathematics

Place: Budapest, Hungary

Research theme: epidemic modeling, network science, graph theory, geometric networks, metapopulation models

Scientific directors: Dr. Márton Karsai (karsai.marton@gmail.com ) & Prof. Dr. László Lovász (laszlo.lovasz@ttk.elte.hu )

Network Epidemics Group @ Rényi Insitute

The Network Epidemics Group at the Rényi Institute works on the mathematical, computational, and data-driven modelling of dynamical epidemiological processes on graphs and networks. On one hand, the group plays special focus on the mathematical foundation of geometric network effects on evolving spreading processes, and on the other hand, on the data-driven simulations of epidemic processes to observe and understand real-world spreading phenomena. The group is led by Dr. Márton Karsai and Pr. László Lovász and functions as a member of the Health Security National Laboratory in Hungary.

Mission

It is a fundamental question in disease modeling how the structure and dynamics of social interactions and mobility mixing patterns influence an ongoing epidemic. These behavioral patterns can be effectively represented as networks, that provide effective tools for the mathematical and computational modelling of epidemic phenomena. They contribute to a better approximation that incorporates non-homogeneous mixing patterns within and between populations, which can build up into meta-population networks to describe how epidemics spread in countries or even around the globe.

The geometric structure and spatial organization of interaction and mobility networks play special roles in the emergence of a rich but largely unexplored set of spreading phenomena. One of these phenomena is the commonly observed spatial clustering of infection cases during the sub-sequent waves of the actual COVID-19 pandemic. While these phenomena can be related to the inhomogeneous spatial distribution of susceptible populations, local patterns of herd immunity or the different seeding scenarios of an actual wave, their emergence is substantially depending on the geometric nature of the underlying social and mobility networks.

In this project we aim to tackle this problem from two different directions:

Computational modelling of epidemic processes on geometric networks: to develop a spatially embedded meta-population framework, relying on data from Hungary, that is capable to reproduce rich class of spatially clustered patterns of infected cases in the country.
Mathematical modelling: to develop the mathematical foundation of these observed phenomena by identifying the fundamental graph properties of the underlying network structures that can induce the observed geometric patterns of infection clustering.

Read the full article at: renyi.hu