The bane of the language-learner is a goldmine for linguists, cultural evolutionists, and computer scientists, a group of whom will meet at SFI Aug. 27–28, 2018. Given the messy state of linguistic affairs, they ask, is it possible to quantitatively encode “meaning” independent of any particular language?
An SFI Working Group examines the evidence of low-density Maya settlements and the challenge this poses to the idea that density increases with population.
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.
A-list for ALIFE: Steen Rasmussen receives Lifetime Achievement Award from International Society for Artificial Life
July 26-28, an interdisciplinary group of researchers gathers at SFI to explore the relationship between aging and infectious disease.
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.
Parakeet pecking orders, basketball match-ups, and the tenure-track: How analyzing winners and losers can reveal rank within networks
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.
Polygyny has been more common among relatively egalitarian low-tech horticulturalists than in highly unequal, capital-intensive agricultural societies. This surprising fact is known as the "polygyny paradox," and a new study from SFI's Dynamics of Wealth Inequality Project provides a possible resolution of the puzzle.
This question of how the collective influences individual performance is central to the work of SFI’s investigation into the limits of human performance. In a workshop that takes place June 25-27, experts from a range of disciplines, including physiology, organizational behavior, sports analytics and applied mathematics, explore how the collective affects the individual.
On June 11, the SFI Press released the second volume in its Seminar Series, The Emergence of Premodern States, edited by Jeremy A. Sabloff and Paula L.W. Sabloff. This project tackles one of the most deceptively simple inquiries in archaeology: How did humans transition from hunter-gatherer societies into states — collective entities that are the movers and shakers of the modern world?
An SFI team led by Professor Mirta Galesic has received a nearly $500,000 grant from the US Department of Agriculture’s National Institute of Food and Agriculture to study how people form beliefs about genetically modified crops.
We humans make social judgments about ourselves and others that can appear contradictory. A new Social Sampling Model, presented by Professor Mirta Galesic and External Professor Henrik Olsson, suggest these apparently conflicting judgments can be explained by a single quantitative theory.
Complexity scientists meet at SFI to examine how collective decisions get made in biological systems and to what degree those systems share a mechanism from one system to the next.
A new proof by SFI Professor David Wolpert sends a humbling message to would-be super intelligences: you can’t know everything all the time.
Researchers analyzed new data on the Chilean elections of the 1970s to understand how economies react to institutional change.
"Algorithmic Information Dynamics: From Networks to Cells," is a new online course that will introduce students to tools that allow them to explore causal relationships in complex datasets. Register online through Complexity Explorer.