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: A critical overview of the identification of social interactions can be found in this article, where the authors consider linear and discrete choice models as well as social networks structures and also consider experimental and quasi-experimental methods.
Abstract: While interest in social determinants of individual behavior has led to a rich theoretical literature and many efforts to measure these influences, a mature “social econometrics” has yet to emerge. This chapter provides a critical overview of the identification of social interactions. We consider linear and discrete choice models as well as social networks structures. We also consider experimental and quasi-experimental methods. In addition to describing the state of the identification literature, we indicate areas where additional research is especially needed and suggest some directions that appear to be especially promising.
222 citations
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TL;DR: A detailed analysis of the evolving RNA populations in genotype space shows that cryptic variation allows a population to explore new genotypes that become adaptive only in a new environment, highlighting the positive role that robustness and epistasis can have in adaptive evolution.
Abstract: Cryptic variation is caused by the robustness of phenotypes to mutations Cryptic variation has no effect on phenotypes in a given genetic or environmental background, but it can have effects after mutations or environmental change Because evolutionary adaptation by natural selection requires phenotypic variation, phenotypically revealed cryptic genetic variation may facilitate evolutionary adaptation This is possible if the cryptic variation happens to be pre-adapted, or "exapted", to a new environment, and is thus advantageous once revealed However, this facilitating role for cryptic variation has not been proven, partly because most pertinent work focuses on complex phenotypes of whole organisms whose genetic basis is incompletely understood Here we show that populations of RNA enzymes with accumulated cryptic variation adapt more rapidly to a new substrate than a population without cryptic variation A detailed analysis of our evolving RNA populations in genotype space shows that cryptic variation allows a population to explore new genotypes that become adaptive only in a new environment Our observations show that cryptic variation contains new genotypes pre-adapted to a changed environment Our results highlight the positive role that robustness and epistasis can have in adaptive evolution
222 citations
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TL;DR: In this article, the authors find evidence that the unwinding of these portfolios began in July 2007 and continued until the end of 2007, suggesting that the Quant Meltdown of August 2007 was the combined effects of portfolio deleveraging throughout July and the first week of August, and a temporary withdrawal of marketmaking risk capital starting August 8th.
Abstract: During the week of August 6, 2007, a number of quantitative long/short equity hedge funds experienced unprecedented losses. It has been hypothesized that a coordinated deleveraging of similarly constructed portfolios caused this temporary dislocation in the market. Using the simulated returns of long/short equity portfolios based on five specific valuation factors, we find evidence that the unwinding of these portfolios began in July 2007 and continued until the end of 2007. Using transactions data, we find that the simulated returns of a simple marketmaking strategy were significantly negative during the week of August 6, 2007, but positive before and after, suggesting that the Quant Meltdown of August 2007 was the combined effects of portfolio deleveraging throughout July and the first week of August, and a temporary withdrawal of marketmaking risk capital starting August 8th. Our simulations point to two unwinds - a mini-unwind on August 1st starting at 10:45am and ending at 11:30am, and a more sustained unwind starting at the open on August 6th and ending at 1:00pm - that began with stocks in the financial sector and long Book-to-Market and short Earnings Momentum. These conjectures have significant implications for the systemic risks posed by the hedge-fund industry.
222 citations
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TL;DR: It is found that market impact is strongly concave, approximately increasing as the square root of order size, and as a given order is executed, the impact grows in time according to a power law.
Abstract: We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are statistically reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power law; after the order is finished, it reverts to a level of about 0.5–0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.
221 citations
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Santa Fe Institute1, University of Texas at Austin2, San Diego State University3, New Mexico Department of Health4, Harvard University5, McGill University6, Boston Children's Hospital7, Los Alamos National Laboratory8, Johns Hopkins University9, Virginia Bioinformatics Institute10, Centers for Disease Control and Prevention11, Biomedical Advanced Research and Development Authority12, Chatham House13, New York City Department of Health and Mental Hygiene14, University of Queensland15, University of Iowa16, University of Liverpool17, University of Cambridge18, Google19, National Center for Immunization and Respiratory Diseases20, Northeastern University21
TL;DR: This paper outlines a conceptual framework for integrating NDS into current public health surveillance and presents the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical.
Abstract: Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.
220 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 |