Institution
Santa Fe Institute
Nonprofit•Santa Fe, New Mexico, United States•
About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Complex network. The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.
Papers published on a yearly basis
Papers
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TL;DR: This work used vocabulary data from three of the world's major language groups to show that 10 to 33% of the overall vocabulary differences among these languages arose from rapid bursts of change associated with language-splitting events.
Abstract: Linguists speculate that human languages often evolve in rapid or punctuational bursts, sometimes associated with their emergence from other languages, but this phenomenon has never been demonstrated. We used vocabulary data from three of the world9s major language groups—Bantu, Indo-European, and Austronesian—to show that 10 to 33% of the overall vocabulary differences among these languages arose from rapid bursts of change associated with language-splitting events. Our findings identify a general tendency for increased rates of linguistic evolution in fledgling languages, perhaps arising from a linguistic founder effect or a desire to establish a distinct social identity.
212 citations
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TL;DR: The demonstration of geographic and niche range changes offers a novel means of assessing the downfall of Ediacara-type taxa at the hands of emerging metazoans, which is hypothesize to be most likely due to the indirect ecological impact metazans had upon the Ediacarans.
212 citations
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TL;DR: This work argues that oneOs acquaintances, oneOs immediate neighbors in the acquaintance network, are far from being a random sample of the population, and that this biases the numbers of neighbors two and more steps away, and presents an improved theoretical model which gives significantly better results.
211 citations
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TL;DR: A new model can explain cultural clustering in human societies by combining the integrative tendencies of social influence with the disintegrative effects of individualization, and is robust to “noise”—randomness is actually the central mechanism that sustains pluralism and clustering.
Abstract: One of the most intriguing dynamics in biological systems is the emergence of clustering, in the sense that individuals self-organize into separate agglomerations in physical or behavioral space. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is the clustering of opinions in human populations, particularly when opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing continuous opinion formation models predict “monoculture” in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness has not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution to the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct computer simulation experiments demonstrating that with this kind of noise a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting. In summary, the new model can explain cultural clustering in human societies. Strikingly, model predictions are not only robust to “noise”—randomness is actually the central mechanism that sustains pluralism and clustering.
211 citations
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TL;DR: It is argued here that robustness is a measure of feature persistence in systems that compels us to focus on perturbations, and often assemblages of perturbation, qualitatively different in nature from those addressed by stability theory.
Abstract: Exploring the difference between “stable” and “robust” touches on essentially every aspect of what we instinctively find interesting about robustness in natural, engineering, and social systems. It is argued here that robustness is a measure of feature persistence in systems that compels us to focus on perturbations, and often assemblages of perturbations, qualitatively different in nature from those addressed by stability theory. Moreover, to address feature persistence under these sorts of perturbations, we are naturally led to study issues including: the coupling of dynamics with organizational architecture, implicit assumptions of the environment, the role of a system’s evolutionary history in determining its current state and thereby its future state, the sense in which robustness characterizes the fitness of the set of “strategic options” open to the system; the capability of the system to switch among multiple functionalities; and the incorporation of mechanisms for learning, problem-solving, and creativity.
211 citations
Authors
Showing all 606 results
Name | H-index | Papers | Citations |
---|---|---|---|
James Hone | 127 | 637 | 108193 |
James H. Brown | 125 | 423 | 72040 |
Alan S. Perelson | 118 | 632 | 66767 |
Mark Newman | 117 | 348 | 168598 |
Bette T. Korber | 117 | 392 | 49526 |
Marten Scheffer | 111 | 350 | 73789 |
Peter F. Stadler | 103 | 901 | 56813 |
Sanjay Jain | 103 | 881 | 46880 |
Henrik Jeldtoft Jensen | 102 | 1286 | 48138 |
Dirk Helbing | 101 | 642 | 56810 |
Oliver G. Pybus | 100 | 447 | 45313 |
Andrew P. Dobson | 98 | 322 | 44211 |
Carel P. van Schaik | 94 | 329 | 26908 |
Seth Lloyd | 92 | 490 | 50159 |
Andrew W. Lo | 85 | 378 | 51440 |