Working group to study aging in single-celled organisms

What does it mean to grow old? Many fields have offered answers, but none of them provides a universal theory. According to former SFI Postdoc Jacopo Grilli (International Centre for Theoretical Physics), we understand the when but not the how of aging: when the components of an organism fail, but not the causes of these failures or if the process serves an evolutionary purpose. This February, a diverse international working group will meet at SFI to find a fresh take on the problem.

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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: Crime and Punishment in the Shadows of Doubt

In this SFI Community Lecture, economist Rajiv Sethi shows the depths to which stereotypes are implicated in the most controversial criminal justice issues of our time, and how a clearer understanding of their effects can guide us toward a more just society.

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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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Why Congress cares about Regional Transmission Organizations (and you should too)

For something as ubiquitous in modern life as electrical power, few of us know much about the rules that govern power production, fees, or transmission. SFI External Professor Seth Blumsack, with colleagues from Boise State University and Duke University, are working to better understand them through a recently funded project called RTOGov (short for RTO Governance). Last fall, they shared what they've learned with the U.S. Congress.

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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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