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.

The civilizing process in London’s Old Bailey

Abstract: The jury trial is a critical point where the state and its citizens come together to define the limits of acceptable behavior. Here we present a large-scale quantitative analysis of trial transcripts from the Old Bailey that reveal a major transition in the nature of this defining moment. By coarse-graining the spoken word testimony into synonym sets and dividing the trials based on indictment, we demonstrate the emergence of semantically distinct violent and nonviolent trial genres. We show that although in the late 18th century the semantic content of trials for violent offenses is functionally indistinguishable from that for nonviolent ones, a long-term, secular trend drives the system toward increasingly clear distinctions between violent and nonviolent acts. We separate this process into the shifting patterns that drive it, determine the relative effects of bureaucratic change and broader cultural shifts, and identify the synonym sets most responsible for the eventual genre distinguishability. This work provides a new window onto the cultural and institutional changes that accompany the monopolization of violence by the state, described in qualitative historical analysis as the civilizing process.

A common scaling rule for abundance, energetics, and production of parasitic and free-living species.

Abstract: The metabolic theory of ecology uses the scaling of metabolism with body size and temperature to explain the causes and consequences of species abundance. However, the theory and its empirical tests have never simultaneously examined parasites alongside free-living species. This is unfortunate because parasites represent at least half of species diversity. We show that metabolic scaling theory could not account for the abundance of parasitic or free-living species in three estuarine food webs until accounting for trophic dynamics. Analyses then revealed that the abundance of all species uniformly scaled with body mass to the –¾ power. This result indicates “production equivalence,” where biomass production within trophic levels is invariant of body size across all species and functional groups: invertebrate or vertebrate, ectothermic or endothermic, and free-living or parasitic.

FRANz: reconstruction of wild multi-generation pedigrees

Abstract: Summary: We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatellites and single nucleotide polymorphisms (SNPs). If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov Chain Monte Carlo (MCMC) sampling. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical dataset with known pedigree. The parentage inference is robust even in the presence of genotyping errors. Availability: The C source code of FRANz can be obtained under the GPL from http://www.bioinf.uni-leipzig.de/Software/FRANz/.

Random intersection graphs with tunable degree distribution and clustering

Abstract: A random intersection graph is constructed by assigning independently to each vertex a subset of a given set and drawing an edge between two vertices if and only if their respective subsets intersect. In this paper a model is developed in which each vertex is given a random weight, and vertices with larger weights are more likely to be assigned large subsets. The distribution of the degree of a given vertex is characterized and is shown to depend on the weight of the vertex. In particular, if the weight distribution is a power law, the degree distribution will be so as well. Furthermore, an asymptotic expression for the clustering in the graph is derived. By tuning the parameters of the model, it is possible to generate a graph with arbitrary clustering, expected degree and -- in the power law case -- tail exponent.