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Institution

Santa Fe Institute

NonprofitSanta 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
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Journal ArticleDOI
TL;DR: A data set consisting of practically all actions of all players over a period of 3 years from a MMOG played by 300,000 people is compiled, demonstrating the feasibility for establishing a ‘socio-economic laboratory’ which allows to operate at levels of precision approaching those of the natural sciences.

272 citations

Journal ArticleDOI
TL;DR: Key directions for future work include linking mechanistic models to field transmission, combining temperature effects with control measures, incorporating trait variation and temperature variation, and investigating climate adaptation and migration.
Abstract: Mosquito-borne diseases cause a major burden of disease worldwide. The vital rates of these ectothermic vectors and parasites respond strongly and nonlinearly to temperature and therefore to climate change. Here, we review how trait-based approaches can synthesise and mechanistically predict the temperature dependence of transmission across vectors, pathogens, and environments. We present 11 pathogens transmitted by 15 different mosquito species - including globally important diseases like malaria, dengue, and Zika - synthesised from previously published studies. Transmission varied strongly and unimodally with temperature, peaking at 23-29oC and declining to zero below 9-23oC and above 32-38oC. Different traits restricted transmission at low versus high temperatures, and temperature effects on transmission varied by both mosquito and parasite species. Temperate pathogens exhibit broader thermal ranges and cooler thermal minima and optima than tropical pathogens. Among tropical pathogens, malaria and Ross River virus had lower thermal optima (25-26oC) while dengue and Zika viruses had the highest (29oC) thermal optima. We expect warming to increase transmission below thermal optima but decrease transmission above optima. Key directions for future work include linking mechanistic models to field transmission, combining temperature effects with control measures, incorporating trait variation and temperature variation, and investigating climate adaptation and migration.

271 citations

Journal ArticleDOI
TL;DR: It is shown that coincident interests are not a prerequisite for linguistic communication, and many of the results derived previously can be expected also under more realistic models of society.
Abstract: The “costly signaling” hypothesis proposes that animal signals are kept honest by appropriate signal costs. We show that to the contrary, signal cost is unnecessary for honest signaling even when interests conflict. We illustrate this principle by constructing examples of cost-free signaling equilibria for the two paradigmatic signaling games of Grafen (1990) and Godfray (1991). Our findings may explain why some animal signals use cost to ensure honesty whereas others do not and suggest that empirical tests of the signaling hypothesis should focus not on equilibrium cost but, rather, on the cost of deviation from equilibrium. We use these results to apply costly signaling theory to the low-cost signals that make up human language. Recent game theoretic models have shown that several key features of language could plausibly arise and be maintained by natural selection when individuals have coincident interests. In real societies, however, individuals do not have fully coincident interests. We show that coincident interests are not a prerequisite for linguistic communication, and find that many of the results derived previously can be expected also under more realistic models of society.

271 citations

Journal ArticleDOI
17 Nov 2011-Nature
TL;DR: Here statistical methods that automatically detect temporal shifts in the rate of evolution through time are applied to a comprehensive mammalian phylogeny and data set of body sizes of 3,185 extant species, suggesting that the processes that give rise to the morphological diversity of a class of animals are far more free to vary than previously considered.
Abstract: The radiation of the mammals provides a 165-million-year test case for evolutionary theories of how species occupy and then fill ecological niches. It is widely assumed that species often diverge rapidly early in their evolution, and that this is followed by a longer, drawn-out period of slower evolutionary fine-tuning as natural selection fits organisms into an increasingly occupied niche space. But recent studies have hinted that the process may not be so simple. Here we apply statistical methods that automatically detect temporal shifts in the rate of evolution through time to a comprehensive mammalian phylogeny and data set of body sizes of 3,185 extant species. Unexpectedly, the majority of mammal species, including two of the most speciose orders (Rodentia and Chiroptera), have no history of substantial and sustained increases in the rates of evolution. Instead, a subset of the mammals has experienced an explosive increase (between 10- and 52-fold) in the rate of evolution along the single branch leading to the common ancestor of their monophyletic group (for example Chiroptera), followed by a quick return to lower or background levels. The remaining species are a taxonomically diverse assemblage showing a significant, sustained increase or decrease in their rates of evolution. These results necessarily decouple morphological diversification from speciation and suggest that the processes that give rise to the morphological diversity of a class of animals are far more free to vary than previously considered. Niches do not seem to fill up, and diversity seems to arise whenever, wherever and at whatever rate it is advantageous.

270 citations

Journal ArticleDOI
TL;DR: In this paper, the authors define and obtain hierarchical trees, correlation based trees and networks from a correlation matrix, and associate a hierarchically nested factor model to a hierarchical tree obtained from the correlation matrix.
Abstract: We discuss some methods to quantitatively investigate the properties of correlation matrices. Correlation matrices play an important role in portfolio optimization and in several other quantitative descriptions of asset price dynamics in financial markets. Here, we discuss how to define and obtain hierarchical trees, correlation based trees and networks from a correlation matrix. The hierarchical clustering and other procedures performed on the correlation matrix to detect statistically reliable aspects of it are seen as filtering procedures of the correlation matrix. We also discuss a method to associate a hierarchically nested factor model to a hierarchical tree obtained from a correlation matrix. The information retained in filtering procedures and its stability with respect to statistical fluctuations is quantified by using the Kullback–Leibler distance.

267 citations


Authors

Showing all 606 results

NameH-indexPapersCitations
James Hone127637108193
James H. Brown12542372040
Alan S. Perelson11863266767
Mark Newman117348168598
Bette T. Korber11739249526
Marten Scheffer11135073789
Peter F. Stadler10390156813
Sanjay Jain10388146880
Henrik Jeldtoft Jensen102128648138
Dirk Helbing10164256810
Oliver G. Pybus10044745313
Andrew P. Dobson9832244211
Carel P. van Schaik9432926908
Seth Lloyd9249050159
Andrew W. Lo8537851440
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202341
202241
2021297
2020309
2019263
2018231