W. Brian Arthur on Complexity Economics – Sean Carroll’s Mindscape podcast

Economies in the modern world are incredibly complex systems. But when we sit down to think about them in quantitative ways, it’s natural to keep things simple at first. We look for reliable relations between small numbers of variables, seek equilibrium configurations, and so forth. But those approaches don’t always work in complex systems, and sometimes we have to use methods that are specifically adapted to the challenges of complexity. That’s the perspective of W. Brian Arthur, a pioneer in the field of complexity economics, according to which economies are typically not in equilibrium, not made of homogeneous agents, and are being constantly updated. We talk about the basic ideas of complexity economics, how it differs from more standard approaches, and what it teaches us about the operation of real economies.

Listen at: www.preposterousuniverse.com

The 2021 Conference on Artificial Life Proceedings

Edited by Jitka Čejková, Silvia Holler, Lisa Soros, Olaf Witkowski

MIT Press

This volume contains the proceedings of the 2021 Conference on Artificial Life (ALIFE 2021) which was originally scheduled to be held in Prague (Czech Republic) 19 – 23 July 2021, but because of the covid-19 pandemic and its repercussions, is being held virtually only. (https://2021.alife.org/). The International Conference on the Synthesis and Simulation of Living Systems (ALIFE) and the European Conference on Artificial Life (ECAL) have been the major meetings of the artificial life (ALife) research community since 1987 and 1991, respectively. Currently, these scientific gatherings are supported by the International Society for Artificial Life (ISAL) – a democratic, international, professional society dedicated to promoting scientific research and education relating to artificial life, including sponsoring this conference annually, publishing scientific journals and proceedings, and maintaining web sites related to artificial life.

Read the full proceedings at: direct.mit.edu

Unified treatment of synchronization patterns in generalized networks with higher-order, multilayer, and temporal interactions

Yuanzhao Zhang, Vito Latora & Adilson E. Motter
Communications Physics volume 4, Article number: 195 (2021)

When describing complex interconnected systems, one often has to go beyond the standard network description to account for generalized interactions. Here, we establish a unified framework to simplify the stability analysis of cluster synchronization patterns for a wide range of generalized networks, including hypergraphs, multilayer networks, and temporal networks. The framework is based on finding a simultaneous block diagonalization of the matrices encoding the synchronization pattern and the network topology. As an application, we use simultaneous block diagonalization to unveil an intriguing type of chimera states that appear only in the presence of higher-order interactions. The unified framework established here can be extended to other dynamical processes and can facilitate the discovery of emergent phenomena in complex systems with generalized interactions. Recent studies have shown that complex systems are often best represented by generalized networks such as hypergraphs, multilayer networks, and temporal networks. Here, the authors propose a unified framework to investigate cluster synchronization patterns in generalized networks and demonstrate the existence of chimera states that emerge exclusively in the presence of higher-order interactions.

Read the full article at: www.nature.com

Unmasking the mask studies: Why the effectiveness of surgical masks in preventing respiratory infections has been underestimated

Pratyush K Kollepara, M.Sc, Alexander F Siegenfeld, S.B, Nassim Nicholas Taleb, Ph.D, Yaneer Bar-Yam, Ph.D

Journal of Travel Medicine, taab144

Background: Pre-pandemic empirical studies have produced mixed statistical results on the effectiveness of masks against respiratory viruses, leading to confusion that may have contributed to organizations such as the WHO and CDC initially not recommending that the general public wear masks during the COVID-19 pandemic.

Methods: A threshold-based dose–response curve framework is used to analyse the effects of interventions on infection probabilities for both single and repeated exposure events. Empirical studies on mask effectiveness are evaluated with a statistical power analysis that includes the effect of adherence to mask usage protocols.

Results: When the adherence to mask-usage guidelines is taken into account, the empirical evidence indicates that masks prevent disease transmission: all studies we analysed that did not find surgical masks to be effective were under-powered to such an extent that even if masks were 100% effective, the studies in question would still have been unlikely to find a statistically significant effect. We also provide a framework for understanding the effect of masks on the probability of infection for single and repeated exposures. The framework demonstrates that masks can have a disproportionately large protective effect and that more frequently wearing a mask provides super-linearly compounding protection.

Conclusions: This work shows (1) that both theoretical and empirical evidence is consistent with masks protecting against respiratory infections and (2) that nonlinear effects and statistical considerations regarding the percentage of exposures for which masks are worn must be taken into account when designing empirical studies and interpreting their results.

Read the full article at: academic.oup.com