News + Events

Researchers who study Earth’s biosphere tend to operate from one of three scientific cultures, each with distinct ways of conducting science, and which have been operating mostly independently from one another, find the authors of a Perspective published in PNAS. Three SFI researchers identify and explain the three cultures, and suggest that reconnecting them could help accelerate biosphere science.

The journal Perspectives on Psychological Science, one of the flagship publications of the Association for Psychological Science, has published two new special issues, both edited by SFI researchers and both taking an unusual approach for disciplinary journals: featuring research from a range of disciplines to address two especially timely topics. One of the special issues dives into the power and perils of human collectives, while the other takes on the rapid advances in AI.

There are many things we don’t know about how history unfolds. The process might be impersonal, even inevitable, as some social scientists have suggested; human societies might be doomed to decline. Or, individual actions and environmental conditions might influence our communities’ trajectories. Social scientists have struggled to find a consensus on such fundamental issues. A new framework by SFI faculty and others suggests a way to unify these perspectives.

Randomness in an environment can make predictions difficult to model, but a new approach helps streamline the process.

Networks can represent changing systems, like the spread of an epidemic or the growth of groups in a population of people. But the structure of these networks can change, too, as links appear or vanish over time. In a new paper, a trio of SFI-affiliated researchers describe a novel way to aggregate static snapshots into smaller clusters of networks while still preserving the dynamic nature of the system.

New simulation model shows that the evolution of sustainable institutions critically depends on clearly defined and enforced access rights.

In a new paper, SFI External Professor Ricard Solé and co-authors propose an experimental setup that will test James Lovelock and Lynn Margulis's hypothesis of planetary self-regulation.

A new study in Nature Communications presents data and a mathematical model to explain why there is more unconscious, or implicit, racial bias in some cities than others. The study, which brings together the math of cities with the psychology of how individuals develop unconscious racial biases, suggests that a city's level of implicit bias depends on how populous, diverse, and segregated that city is.

Researchers introduce a mathematical model that connects innovation and obsolescence to unify insights across diverse fields, from economics and biology, to science itself.

The AI that can write sentences or compose news articles can also accurately predict the unfolding of individual human lives. A new tool called life2vec can predict outcomes, including early death, by leveraging similarities between how sequences of events progress in human lives and sequences of words progress in language, according to a recent study in Nature.

Many complex systems, from microbial communities to mussel beds to drylands, display striking self-organized clusters. According to theoretical models, these groupings play an important role in how an ecosystem works and its ability to respond to environmental changes. A new paper in PNAS focused on the spatial patterns found in drylands offers important empirical evidence validating the models.

Waste is a natural by-product of productive human economies and a problem that plagues human systems. In a new paper, Mingzhen Lu and Chris Kempes explore how three types of waste production — municipal solid waste, wastewater, and greenhouse gas emissions — scale with city size. 

In 2005, three researchers at SFI put forward a model, now known as the LMF Model, explaining a "long-memory" behavior in stock markets around the world. Last November, their model was finally confirmed.

The rules of statistical physics address the uncertainty about the state of a system that arises when that system interacts with its environment. But they’ve long missed another kind. In a new paper published in Physical Review Research, David Wolpert and Jan Korbel argue that uncertainty in the thermodynamic parameters themselves — built into equations that govern the energetic behavior of the system — may also influence the outcome of an experiment.

As consumer interest in electric vehicles rises, the lack of charging stations is a continuing concern to potential customers. A recent paper in PNAS Nexus provides a possible road map for how to optimize the locations for new EV infrastructure.

Societies and political structures, like the humans they serve, appear to become more fragile as they age, according to an analysis of hundreds of pre-modern societies. A new study, which holds implications for the modern world, provides the first quantitative support for the theory that the resilience of political states decreases over time. 

Researchers, including former SFI Postdoctoral Fellow Elizabeth Hobson, have introduced a novel mate choice concept called 'Inferred Attractiveness,' outlined in a recent publication in PLOS Biology. 

As children, we learn categories through visual examples, verbal explanations, or both, and are often guided by “teachers” — perhaps a parent or other adult. In contrast, academic research has primarily studied non-pedagogical learning where there is no active teacher, and learning based on visual examples, omitting verbal-based category learning. A recent paper in Cognition by Arseny Moskvichev and co-authors aims to close this gap.

In the last two decades, researchers have reported success modeling high-dimensional chaotic behaviors with a simple but powerful machine-learning approach called reservoir computing. A new paper in Physical Review Research identifies limitations to reservoir computing and suggests a kind of catch-22 that can prove hard to circumvent, especially for complicated dynamic systems. 

The field of artificial intelligence has long been stymied by the lack of an answer to its most fundamental question: What is intelligence? To address questions about intelligence in AI, we need concrete tasks to pin down and test the notion of intelligence, argue SFI researchers Arseny Moskvichev, Melanie Mitchell, and Victor Vikram Odouard in a new paper in Transactions on Machine Learning Research.