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Santa Fe, NM, United States

The Santa Fe Institute is an independent, nonprofit theoretical research institute located in Santa Fe and dedicated to the multidisciplinary study of the fundamental principles of complex adaptive systems, including physical, computational, biological, and social systems.The Institute consists of a small number of resident faculty, a large group of "external" faculty, whose primary appointments are at other institutions, and a number of visiting scholars. The Institute is advised by a group of eminent scholars, including several Nobel Prize–winning scientists. Although theoretical scientific research is the Institute's primary focus, it also runs several popular summer schools on complex systems, along with other educational and outreach programs aimed at students ranging from middle school up through graduate school.The Institute's annual funding comes from a combination of private donors, grant-making foundations, government science agencies, and companies affiliated with its business network. The 2011 budget was just over $10 million. Wikipedia.

DeDeo S.,Santa Fe Institute
PLoS computational biology | Year: 2010

Conflict destabilizes social interactions and impedes cooperation at multiple scales of biological organization. Of fundamental interest are the causes of turbulent periods of conflict. We analyze conflict dynamics in an monkey society model system. We develop a technique, Inductive Game Theory, to extract directly from time-series data the decision-making strategies used by individuals and groups. This technique uses Monte Carlo simulation to test alternative causal models of conflict dynamics. We find individuals base their decision to fight on memory of social factors, not on short timescale ecological resource competition. Furthermore, the social assessments on which these decisions are based are triadic (self in relation to another pair of individuals), not pairwise. We show that this triadic decision making causes long conflict cascades and that there is a high population cost of the large fights associated with these cascades. These results suggest that individual agency has been over-emphasized in the social evolution of complex aggregates, and that pair-wise formalisms are inadequate. An appreciation of the empirical foundations of the collective dynamics of conflict is a crucial step towards its effective management. Source

Halifax J.,Santa Fe Institute
Current Opinion in Supportive and Palliative Care | Year: 2012

Purpose of review: This article is an investigation of the possibility that compassion is not a discrete feature but an emergent and contingent process that is at its base enactive. Compassion must be primed through the cultivation of various factors. This article endeavors to identify interdependent components of compassion. This is particularly relevant for those in the end-of-life care professions, wherein compassion is an essential factor in the care of those suffering from a catastrophic illness or injury. The Halifax Model of Compassion is presented here as a new vision of compassion with particular relevance for the training of compassion in clinicians. Recent findings: Compassion is generally valued as a prosocial mental quality. The factors that foster compassion are not well understood, and the essential components of compassion have not been sufficiently delineated. Neuroscience research on compassion has only recently begun, and there is little clinical research on the role of compassion in end-of-life care. Summary: Compassion is in general seen as having two main components: the affective feeling of caring for one who is suffering and the motivation to relieve suffering. This definition of compassion might impose limitations and will, therefore, have consequences on how one trains compassion in clinicians and others. It is the author's premise that compassion is dispositionally enactive (the interactions between living organisms and their environments, i.e., the propensity toward perception-action in relation to one's surrounds), and it is a process that is contingent and emergent. © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins. Source

Bowles S.,Santa Fe Institute | Choi J.-K.,Kyungpook National University
Proceedings of the National Academy of Sciences of the United States of America | Year: 2013

The advent of farming around 12 millennia ago was a cultural as well as technological revolution, requiring a new systemof property rights. Among mobile hunter-gatherers during the late Pleistocene, food was almost certainly widely shared as it was acquired. If a harvested crop or the meat of a domesticated animal were to have been distributed to other group members, a late Pleistocene would-be farmer would have had little incentive to engage in the required investments in clearing, cultivation, animal tending, and storage. However, the new property rights that farming required-secure individual claims to the products of one's labor-were infeasible because most of the mobile and dispersed resources of a forager economy could not cost-effectively be delimited and defended. The resulting chicken-and-egg puzzle might be resolved if farming had been much more productive than foraging, but initially it was not. Our model and simulations explain how, despite being an unlikely event, farming and a new system of farming-friendly property rights nonetheless jointly emerged when they did. This Holocene revolution was not sparked by a superior technology. It occurred because possession of the wealth of farmers-crops, dwellings, and animals-could be unambiguously demarcated and defended. This facilitated the spread of new property rights that were advantageous to the groups adopting them. Our results thus challenge unicausal models of historical dynamics driven by advances in technology, population pressure, or other exogenous changes. Our approach may be applied to other technological and institutional revolutions such as the 18th- and 19th-century industrial revolution and the information revolution today. Source

Beach T.G.,Santa Fe Institute
Journal of Alzheimer's Disease | Year: 2013

Medical science is currently perceived as underperforming. This is because of the relatively slow recent rate of development of new disease treatments. This has been blamed on cultural, regulatory, and economic factors that generate a so-called 'Valley of Death', hindering new drug candidates from being moved into clinical trials and eventually approved for use. We propose, however, that for neurodegenerative diseases, a relative decline of human brain tissue research is also a contributor. The present pharmacological agents for treating Alzheimer's disease (AD) were identified through direct examination of postmortem human brain tissue more than 30 years ago. Since that time the percentage of research grants awarded to human brain tissue-using projects has dropped precipitously and publication rates have stagnated. As human brain tissue research has played a central and often initiating role in identifying most of the targets that have gone to AD clinical trials, it is proposed that the rate of discovery of new targets has been curtailed. Additionally, the continued rejection of cortical biopsy as a diagnostic method for AD has most probably depressed the perceived effect sizes of new medications and contributed to the high Phase II clinical trial failure rates. Despite the relative lack of funding, human brain discovery research has continued to make important contributions to our understanding of neurodegenerative disease, and brain banks have played an essential role. It is likely that the pace of discovery will dramatically accelerate over the coming decades as increasingly powerful tools including genomics, epigenetics, transcriptomics, regulatory RNA, gene expression profiling, proteomics, and metabolomics are applied. To optimize the promise of these new technologies, however, it is critical that brain banks are rejuvenated by enhanced governmental and/or private support. © 2013 - IOS Press and the authors. All rights reserved. Source

Katzgraber H.G.,Texas A&M University | Hamze F.,D-Wave Systems | Andrist R.S.,Santa Fe Institute
Physical Review X | Year: 2014

Recently, a programmable quantum annealing machine has been built that minimizes the cost function of hard optimization problems by, in principle, adiabatically quenching quantum fluctuations. Tests performed by different research teams have shown that, indeed, the machine seems to exploit quantum effects. However, experiments on a class of random-bond instances have not yet demonstrated an advantage over classical optimization algorithms on traditional computer hardware. Here, we present evidence as towhy this might be the case. These engineered quantum annealing machines effectively operate coupled to a decohering thermal bath. Therefore, we study the finite-temperature critical behavior of the standard benchmark problem used to assess the computational capabilities of these complex machines. We simulate both random-bond Ising models and spin glasses with bimodal and Gaussian disorder on the D-Wave Chimera topology. Our results show that while the worst-case complexity of finding a ground state of an Ising spin glass on the Chimera graph is not polynomial, the finite-temperature phase space is likely rather simple because spin glasses on Chimera have only a zero-temperature transition. This means that benchmarking optimization methods using spin glasses on the Chimera graph might not be the best benchmark problems to test quantum speedup. We propose alternative benchmarks by embedding potentially harder problems on the Chimera topology. Finally, we also study the (reentrant) disordertemperature phase diagram of the random-bond Ising model on the Chimera graph and show that a finitetemperature ferromagnetic phase is stable up to 19.85(15)% antiferromagnetic bonds. Beyond this threshold, the system only displays a zero-temperature spin-glass phase. Our results therefore show that a careful design of the hardware architecture and benchmark problems is key when building quantum annealing machines. Source

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