COVID-19 U.S. employment shocks likely larger than Great Depression

The U.S. is likely to see a near-term 24% drop in employment, 17% percent drop in wages, and 22% drop in economic activity as a result of the COVID-19 crisis according to a new study co-authored by SFI External Professor Doyne Farmer at the University of Oxford. These impacts will be very unevenly distributed, with the bottom quarter of earners at risk of a 42% loss in employment and bearing a 30% share of total wage losses. In contrast, the study estimates the top quarter of earners only risk a 7% drop in employment and an 18% share of wage losses.

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What is an individual? Information Theory may provide the answer

Despite the near-universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. A new approach may solve the problem by defining individuals in terms of informational processes.

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STAT: Misinformation is important public health data

In their op-ed for STAT, former SFI postdoctoral fellow Laurent Hébert-Dufresne (University of Vermont) and current postdoc Vicky Chuqiao Yang, Complexity Postdoctoral Fellow and Peters Hurst Scholar, argue that if scientists hope to develop better epidemiological models, they must grasp the complex interplay between social behavior and disease.

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Decarbonizing the energy supply

Shifting from carbon-emitting energy sources to renewable ones will be an essential part of addressing climate change, but the path to a renewable power grid is uncharted. A February 26-28 working group explores how New Mexico might best approach the transition to renewable energy sources, and what lessons could be useful for other regions.

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Wealth inequality and social network structure

An NSF-funded research project is exploring the effects of network structure on wealth inequality. In February over 40 anthropologists, economists, and others will review their research so far and chart new directions.

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If cancer were easy, every cell would do it

A new Scientific Reports paper puts an evolutionary twist on a classic question. Instead of asking why we get cancer, Leonardo Oña of Osnabrück University and Michael Lachmann of the Santa Fe Institute use signaling theory to explore how our bodies have evolved to keep us from getting more cancer.  

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Video: Copying vs. Transforming Information

New research by SFI Postdoctoral Fellow Artemy Kolchinsky and Bernat Corominas-Murtra presents an important distinction for information theory — copying vs. transforming. Watch the video explainer.

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Learning by omission

What would happen if neural networks were explicitly trained to discard useless information, and how to tell them to do so, is the subject of recent research by SFI's Artemy Kolchinsky, Brendan Tracey, and David Wolpert.

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