Javier Argota Sánchez-Vaquerizo, Dirk Helbing

Current trends in urban planning aim at the reduction of space for private vehicles to promote alternative mobility, more diverse activities on streets, and reduced pollution for healthier cities. In our study, we evaluate a number of “what-if scenarios” of “city pruning” regarding traffic restrictions for Barcelona by means of realistic, agent-based computer simulations in order to identify their impact on travel performance and the environment. Comparing existing plans designed by the City of Barcelona with variants of those, we find positive counterintuitive effects related to “Braess’ Paradox”, which result in the reduction of emissions (-8% of main pollutants) and traffic congestion (-14% of travel time) solely by closing some streets to motor vehicles. These findings indicate a further potential to improve the quality of life in cities using positive counterintuitive effects of street repurposing and it is an opportunity for participatory and sustainable city-making beyond the ongoing public debate.

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Jessica Flack, Panos Ipeirotis, Thomas W Malone, Geoff Mulgan, Scott E Page

Collective behavior is a universal property of biological, social, and many engineered systems. However, the study of collective intelligence—roughly, the production of adaptive, wise, or clever structures and behaviors by groups—remains nascent. Despite that, it is growing in various disciplines, from biology and psychology to computer science and economics, management, and political science to mathematics, complexity science, and neuroscience.
With the launch of Collective Intelligence, we aim to create a publication that transcends disciplines, methodologies, and traditional formats. We hope to help discover principles that can be useful to both basic and applied science and encourage the emergence of a unified discipline of study.

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Jarrod Shilts, Yannik Severin, Francis Galaway, Nicole Müller-Sienerth, Zheng-Shan Chong, Sophie Pritchard, Sarah Teichmann, Roser Vento-Tormo, Berend Snijder & Gavin J. Wright 
Nature volume 608, pages397–404 (2022)

The human immune system is composed of a distributed network of cells circulating throughout the body, which must dynamically form physical associations and communicate using interactions between their cell-surface proteomes1. Despite their therapeutic potential2, our map of these surface interactions remains incomplete3,4. Here, using a high-throughput surface receptor screening method, we systematically mapped the direct protein interactions across a recombinant library that encompasses most of the surface proteins that are detectable on human leukocytes. We independently validated and determined the biophysical parameters of each novel interaction, resulting in a high-confidence and quantitative view of the receptor wiring that connects human immune cells. By integrating our interactome with expression data, we identified trends in the dynamics of immune interactions and constructed a reductionist mathematical model that predicts cellular connectivity from basic principles. We also developed an interactive multi-tissue single-cell atlas that infers immune interactions throughout the body, revealing potential functional contexts for new interactions and hubs in multicellular networks. Finally, we combined targeted protein stimulation of human leukocytes with multiplex high-content microscopy to link our receptor interactions to functional roles, in terms of both modulating immune responses and maintaining normal patterns of intercellular associations. Together, our work provides a systematic perspective on the intercellular wiring of the human immune system that extends from systems-level principles of immune cell connectivity down to mechanistic characterization of individual receptors, which could offer opportunities for therapeutic intervention.

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Yanbo Zhang, Sara Imari Walker
The high-dimesionality, non-linearity and emergent properties of complex systems pose a challenge to identifying general laws in the same manner that has been so successful in simpler physical systems. In Anderson’s seminal work on why “more is different” he pointed to how emergent, macroscale patterns break symmetries of the underlying microscale laws. Yet, less recognized is that these large-scale, emergent patterns must also retain some symmetries of the microscale rules. Here we introduce a new, relational macrostate theory (RMT) that defines macrostates in terms of symmetries between two mutually predictive observations, and develop a machine learning architecture, MacroNet, that identifies macrostates. Using this framework, we show how macrostates can be identifed across systems ranging in complexity from the simplicity of the simple harmonic oscillator to the much more complex spatial patterning characteristic of Turing instabilities. Furthermore, we show how our framework can be used for the inverse design of microstates consistent with a given macroscopic property — in Turing patterns this allows us to design underlying rule with a given specification of spatial patterning, and to identify which rule parameters most control these patterns. By demonstrating a general theory for how macroscopic properties emerge from conservation of symmetries in the mapping between observations, we provide a machine learning framework that allows a unified approach to identifying macrostates in systems from the simple to complex, and allows the design of new examples consistent with a given macroscopic property.

Read the full article at: arxiv.org