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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: It is demonstrated that sample-space-reducing (SSR) processes necessarily lead to Zipf's law in the rank distributions of their outcomes, and several applications showing how SSR processes can be used to understand Zipf’s law in word frequencies are discussed.
Abstract: History-dependent processes are ubiquitous in natural and social systems. Many such stochastic processes, especially those that are associated with complex systems, become more constrained as they unfold, meaning that their sample space, or their set of possible outcomes, reduces as they age. We demonstrate that these sample-space-reducing (SSR) processes necessarily lead to Zipf’s law in the rank distributions of their outcomes. We show that by adding noise to SSR processes the corresponding rank distributions remain exact power laws, p(x)∼x−λ, where the exponent directly corresponds to the mixing ratio of the SSR process and noise. This allows us to give a precise meaning to the scaling exponent in terms of the degree to which a given process reduces its sample space as it unfolds. Noisy SSR processes further allow us to explain a wide range of scaling exponents in frequency distributions ranging from α=2 to ∞. We discuss several applications showing how SSR processes can be used to understand Zipf’s law in word frequencies, and how they are related to diffusion processes in directed networks, or aging processes such as in fragmentation processes. SSR processes provide a new alternative to understand the origin of scaling in complex systems without the recourse to multiplicative, preferential, or self-organized critical processes.

82 citations

Journal ArticleDOI
TL;DR: The purpose of this White Paper of the EU Support Action “Visioneer” is to address the following goals: develop strategies to quickly increase the objective knowledge about social and economic systems and establish ethical standards regarding the storage, processing, evaluation, and publication of social andEconomic data.
Abstract: The purpose of this White Paper of the EU Support Action “Visioneer”(see www.visioneer.ethz.ch) is to address the following goals: 1. Develop strategies to quickly increase the objective knowledge about social and economic systems. 2. Describe requirements for efficient large-scale scientific data mining of anonymized social and economic data. 3. Formulate strategies how to collect stylized facts extracted from large data set. 4. Sketch ways how to successfully build up centers for computational social science. 5. Propose plans how to create centers for risk analysis and crisis forecasting. 6. Elaborate ethical standards regarding the storage, processing, evaluation, and publication of social and economic data.

82 citations

Journal ArticleDOI
TL;DR: A Bayesian model in which both individual and social learning arise from a single inferential process is presented, indicating that natural selection favors individuals who place heavy weight on social cues when the environment changes slowly or when its state cannot be well predicted using nonsocial cues.

82 citations

Journal ArticleDOI
12 Mar 2013-PeerJ
TL;DR: This study conducted a study during a flat track roller derby tournament and found that teammates shared distinct skin microbial communities before and after playing against another team, but that opposing teams’ bacterial communities converged during the course of a roller derby bout.
Abstract: Diverse bacterial communities live on and in human skin. These complex communities vary by skin location on the body, over time, between individuals, and between geographic regions. Culture-based studies have shown that human to human and human to surface contact mediates the dispersal of pathogens, yet little is currently known about the drivers of bacterial community assembly patterns on human skin. We hypothesized that participation in a sport involving skin to skin contact would result in detectable shifts in skin bacterial community composition. We conducted a study during a flat track roller derby tournament, and found that teammates shared distinct skin microbial communities before and after playing against another team, but that opposing teams’ bacterial communities converged during the course of a roller derby bout. Our results are consistent with the hypothesis that the human skin microbiome shifts in composition during activities involving human to human contact, and that contact sports provide an ideal setting in which to evaluate dispersal of microorganisms between people.

81 citations

Journal ArticleDOI
TL;DR: A new order parameter approximation to random boolean networks (RBN) is introduced, based on the concept of Boolean derivative, allowing to provide the onset of damage spreading through the network and how sensitive it is to minimal perturbations.
Abstract: A new order parameter approximation to random boolean networks (RBN) is introduced, based on the concept of Boolean derivative. A statistical argument involving an annealed approximation is used, allowing to measure the order parameter in terms of the statistical properties of a random matrix. Using the same formalism, a Lyapunov exponent is calculated, allowing to provide the onset of damage spreading through the network and how sensitive it is to minimal perturbations. Finally, the Lyapunov exponents are obtained by means of dierent approximations: through distance method and a discrete variant of the Wolf’s method for continuous systems. c 2000 Elsevier Science B.V. All rights reserved.

81 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