Santa Fe Institute

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.

Self-organized critical traffic in parallel computer networks

Abstract: In a recent paper, we analysed the dynamics of traffic flow in a simple, square lattice architecture. It was shown that a phase transition takes place between a free and a congested phase. The transition point was shown to exhibit optimal information transfer and wide fluctuations in time, with scale-free properties. In this paper, we further extend our analysis by considering a generalization of the previous model in which the rate of packet emission is regulated by the local congestion perceived by each node. As a result of the feedback between traffic congestion and packet release, the system is poised at criticality. Many well-known statistical features displayed by Internet traffic are recovered from our model in a natural way.

Market efficiency and the long-memory of supply and demand: is price impact variable and permanent or fixed and temporary?

Abstract: The article focuses on the market efficiency and the long-memory of supply and demand. The long-memory of supply and demand implies that there are waves of buyer-initiated transactions that are highly foreseeable with the use of simple linear algorithm. The authors stressed that the total price impact can be summed up with bare propagators associated with each transaction.

Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species

Abstract: AimDespite several recent efforts to map plant traits and to identify their climatic drivers, there are still major gaps. Global trait patterns for major functional groups, in particular, the differences between woody and herbaceous plants, have yet to be identified. Here, we take advantage of big data efforts to compile plant species occurrence and trait data to analyse the spatial patterns of assemblage means and variances of key plant traits. We tested whether these patterns and their climatic drivers are similar for woody and herbaceous plants. LocationNew World (North and South America). MethodsUsing the largest currently available database of plant occurrences, we provide maps of 200 × 200 km grid‐cell trait means and variances for both woody and herbaceous species and identify environmental drivers related to these patterns. We focus on six plant traits: maximum plant height, specific leaf area, seed mass, wood density, leaf nitrogen concentration and leaf phosphorus concentration. ResultsFor woody assemblages, we found a strong climate signal for both means and variances of most of the studied traits, consistent with strong environmental filtering. In contrast, for herbaceous assemblages, spatial patterns of trait means and variances were more variable, the climate signal on trait means was often different and weaker. Main conclusionTrait variations for woody versus herbaceous assemblages appear to reflect alternative strategies and differing environmental constraints. Given that most large‐scale trait studies are based on woody species, the strikingly different biogeographic patterns of herbaceous traits suggest that a more synthetic framework is needed that addresses how suites of traits within and across broad functional groups respond to climate.

Author‐level Eigenfactor metrics: Evaluating the influence of authors, institutions, and countries within the social science research network community

Abstract: In this article, we show how the Eigenfactor score, originally designed for ranking scholarly journals, can be adapted to rank the scholarly output of authors, institutions, and countries based on author-level citation data. Using the methods described in this article, we provide Eigenfactor rankings for 84,808 disambiguated authors of 240,804 papers in the Social Science Research Network (SSRN)—a preprint and postprint archive devoted to the rapid dissemination of scholarly research in the social sciences and humanities. As an additive metric, the Eigenfactor scores are readily computed for collectives such as departments or institutions as well. We show that a collective's Eigenfactor score can be computed either by summing the Eigenfactor scores of its members or by working directly with a collective-level cross-citation matrix. We provide Eigenfactor rankings for institutions and countries in the SSRN repository. With a network-wide comparison of Eigenfactor scores and download tallies, we demonstrate that Eigenfactor scores provide information that is both different from and complementary to that provided by download counts. We see author-level ranking as one filter for navigating the scholarly literature, and note that such rankings generate incentives for more open scholarship, because authors are rewarded for making their work available to the community as early as possible and before formal publication.

A 2,000-year reconstruction of the rain-fed maize agricultural niche in the US Southwest.

Abstract: R. Kyle Bocinsky, Timothy Kohler. (2014). A 2,000-year reconstruction of the rain-fed maize agricultural niche in the US Southwest. Nature Communications 5:5618. DOI: 10.1038/ncomms6618.