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
More filters
••
Catalan Institution for Research and Advanced Studies1, University of Michigan2, Howard Hughes Medical Institute3, Scripps Institution of Oceanography4, Lawrence Berkeley National Laboratory5, International Centre for Theoretical Physics6, Pennsylvania State University7, University of Oslo8, Spanish National Research Council9, Princeton University10, Santa Fe Institute11
Abstract: The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.
88 citations
••
TL;DR: In this paper, the authors argue that the exchange paradigm of economics has experienced diminishing marginal returns for some time and suggest a new perspective that gives more import to economic processes and less to end states, one that bases behavior less on axioms and more on laboratory experiments.
Abstract: Kohn (The Cato Journal, 24(3):303–339, 2004) has argued that the neoclassical conception of economics—what he terms the “value paradigm”—has experienced diminishing marginal returns for some time. He suggests a new perspective is emerging—one that gives more import to economic processes and less to end states, one that bases behavior less on axioms and more on laboratory experiments. He calls this the “exchange paradigm”. He further asserts that it is the mathematization of economics that is partially at fault for leading the profession down a methodological path that has become something of a dead end. Here I suggest that the nascent research program Kohn has rightly spotted is better understood as distinct from its precursors because it is intrinsically dynamic, permits agent actions out of equilibrium, and treats such actions as occurring within networks. Analyzing economic processes having these characteristics is mathematically very difficult and I concur with Kohn’s appeal to computational approaches. However, I claim it is so-called multi-agent systems and agent-based models that are the way forward within the “exchange paradigm,” and not the cellular automata (Wolfram, A new kind of science, 2002) that Kohn seems to promote. Agent systems are generalizations of cellular automata and support the natural abstraction of individual economic agents as software agents.
88 citations
••
TL;DR: In this paper, the authors introduce an ambidextrous view of stochastic dynamical systems, comparing their forward-time and reverse-time representations and then integrating them into a single time-symmetric representation.
Abstract: We introduce an ambidextrous view of stochastic dynamical systems, comparing their forward-time and reverse-time representations and then integrating them into a single time-symmetric representation. The perspective is useful theoretically, computationally, and conceptually. Mathematically, we prove that the excess entropy--a familiar measure of organization in complex systems--is the mutual information not only between the past and future, but also between the predictive and retrodictive causal states. Practically, we exploit the connection between prediction and retrodiction to directly calculate the excess entropy. Conceptually, these lead one to discover new system invariants for stochastic dynamical systems: crypticity (information accessibility) and causal irreversibility. Ultimately, we introduce a time-symmetric representation that unifies all these quantities, compressing the two directional representations into one. The resulting compression offers a new conception of the amount of information stored in the present.
88 citations
••
TL;DR: A parsimonious network-based mathematical model provides a simple demographic explanation for the age bias observed for H1N1/09 attack rates, and suggests that this bias may shift in coming months.
Abstract: Shweta Bansal is with Pennsylvania State University and NIH, Babak Pourbohloul is with British Columbia Centre for Disease Control and University of British Columbia, Nathaniel Hupert is with Weill Cornell Medical College and CDC, Bryan Grenfell is with Princeton University, Lauren Ancel Meyers is with UT Austin and Santa Fe Institute.
88 citations
••
TL;DR: This paper investigated whether signal receivers actually perceive religious signalers as such and found that people are attending to the full suite of religious acts carried out by their peers, using these signals to discern multiple aspects of their character and intentions.
88 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 |