News + Events

Introduction to the Theory of Complex Systems synthesizes hundreds of disparate findings in complexity and articulates a single, underlying characteristic of complex systems.

Identifying meaningful information is a key challenge to disciplines from biology to artificial intelligence. In a new paper, SFI's Artemy Kolchinsky and David Wolpert propose a broadly applicable, fully formal definition for this kind of semantic information.

Sending instantaneous messages across long distances, or quickly computing over ungodly amounts of data are just two possibilities that arise if we can design computers to exploit quantum uncertainty, entanglement, and measurement. In this SFI Community Lecture, scientist Christopher Monroe describes the architecture of a quantum computer based on individual atoms, suspended and isolated with electric fields, and individually addressed with laser beams.

Frances Arnold, who served on the Santa Fe Institute’s Science Board from 1995-2000, received a Nobel Prize in Chemistry this year.

October 13-16, graduate students can meet with leading scientists to learn about modeling and evaluating the future of human populations and their environments. Free tuition for accepted students. Apply before July 11, 2018.

An SFI workshop examines the key impediments to building machines that understand meaning, and how much understanding is necessary for artificially intelligent machines to approach human-level abilities in language, perception, and reasoning.

Two October meetings at SFI aim to dig into some of the trickiest questions about life, both here on Earth, and how we might recognize it elsewhere in the universe.

The autumn Applied Complexity Network meeting “Risk: Retrospective Lessons and Prospective Strategies,” explores what we have learned since the financial crisis of 2008.

Quantitative tools developed in math and physics to understand bifurcations in dynamical systems could help ecologists and biologists better understand — and predict — tipping points in animal societies.

Watch The Majesty of Music & Math, a multi-media collaboration between SFI's Cristopher Moore and The Santa Fe Symphony, and produced by New Mexico PBS.  

One of the first-movers in game theory, SFI External Professor Martin Shubik died August 22, 2018. He was 92 years old.

It may seem that there isn't much cross-discussion between theoretical and empirical scientists, but a new cross-citation network analysis shows there is more overlap than many believe. 

SFI External Professor Mark Newman has updated his classic textbook on networks. Oxford University Press publishes Networks, second edition, in early September, 2018.

The notion that an attractive person is “out of your league” doesn’t often dissuade dating hopefuls – at least online. In fact, the majority of online daters seek out partners who are more desirable than themselves, suggests a new large-scale analysis published in Science Advances.

In this SFI Community Lecture, science writer Sabine Hossenfelder explains what physicists mean when they say a theory is beautiful, what went wrong with their reliance on it, and how the field can move on. Watch her talk.

SFI External Professor Steen Rasmussen was presented with a Lifetime Achievement Award at the 2018 ALIFE conference in Tokyo, Japan.

SFI welcomes ten new professors to our external faculty, a cohort of academics who enrich our networks of interactions, help us push the boundaries of complex systems science, and connect us to over 70 institutions around the globe.

A small cadre of scientists and entrepreneurs convened a two-week long SFI working group to address the growing gap between our physical and social technologies.

Neuroscientists and complexity scientists meet to develop new tools for studying the brain as a complex network. Their working group, titled “Cognitive Regime Shift: When the Brain Breaks,” is part of SFI’s Aging, Adaptation, and the Arrow of Time research theme, funded by the James S. McDonnell Foundation.

In a paper published in Science Advances, researchers from the Santa Fe Institute describe a new algorithm called SpringRank that uses wins and losses to quickly find rankings lurking in large networks. When tested on a wide range of synthetic and real-world datasets, ranging from teams in an NCAA college basketball tournament to the social behavior of animals, SpringRank outperformed other ranking algorithms in predicting outcomes and in efficiency.