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 & Context (language use). 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: In this article, the authors used the three money's worth measures to measure the benefit-to-tax ratio, the internal rate of return, and the net present value of the U.S. social security system, including systems with privately managed individual accounts invested in equities.
Abstract: This paper describes how three money's worth measures the benefit-to-tax ratio, the internal rate of return, and the net present value are calculated and used in analyses of social security reforms, including systems with privately managed individual accounts invested in equities. Declining returns from the U.S. social security system prove to be the inevitable result of having instituted an unfunded (pay-as-you-go) retirement system that delivered $7.9 trillion of net transfers (in 1997 present value dollars) to people born before 1917, and will deliver another $1.8 trillion to people born between 1918 and 1937. But young and future workers cannot necessarily do better by investing their payroll taxes in capital markets. If the old system were closed down, massive unfunded liabilities of $9-10 trillion would still have to be paid unless already accrued benefits were cut. Alternative methods of calculating these accrued benefits yield somewhat different numbers: the straight line calculation is $800 billion less than the constant benefit calculation we propose as the benchmark. Using this benchmark in a world with no uncertainty, we show that privatization without prefunding would not increase returns at all, net of the new taxes needed to pay for unfunded liabilities. These new taxes would amount to 3.6 percent of payroll, or about 29 percent of social security contributions. Prefunding, implemented by reducing accrued benefits or by raising taxes, would eventually increase money's worth for later generations, but at the cost of lower money's worth for today's workers and/or retirees. Computing money's worth when there is uncertainty is much more difficult unless four conditions hold prices into stocks and out of bonds has no effect whatsoever on money's worth when it i adjusted for risk: a dollar of stock is worth no more than a dollar of bonds. diversification can raise welfare for constrained households, but the exact money's worth must depend on specific assumptions about household attitudes toward risk. Calculations lik the Social Security Advisory Council that attribute over $2.85 of net present va $1 shifted from bonds to stocks completely overlook the disutility of risk. By estimate that a 2 percent of payroll equity fund carved out of social security w present value by about 59 cents per dollar of bonds switched into equities When the likely reductions in income and longevity insurance are factored in privatization and diversification is substantially less than popularly perceived
148 citations
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TL;DR: This work studies the scaling of the average number of dynamically relevant nodes and the median number of distinct attractors in random Boolean networks and indicates that the correct asymptotic scalings emerge only for very large systems.
Abstract: Random Boolean networks, originally invented as models of genetic regulatory networks, are simple models for a broad class of complex systems that show rich dynamical structures. From a biological perspective, the most interesting networks lie at or near a critical point in parameter space that divides "ordered" from "chaotic" attractor dynamics. We study the scaling of the average number of dynamically relevant nodes and the median number of distinct attractors in such networks. Our calculations indicate that the correct asymptotic scalings emerge only for very large systems.
148 citations
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TL;DR: This paper analyzed the role played by the concept of rationality in economic theory, and demonstrated that it is necessarily constrained to be an essentially contentless notion, and argued that the main body of economic theory is firmly grounded and that some contrasting approaches to rationality, although leading to heated debates and vivid confusion, have no fundamental significance for economics.
Abstract: Nowadays, it seems almost universally presumed that the fundamental characteristic of homo oeconomicus is his rationality. We analyze the role played by the concept of rationality in economic theory, and demonstrate that it is necessarily constrained to be an essentially contentless notion. We show that the main body of economic theory is firmly grounded, and that some contrasting approaches to rationality, although leading to heated debates and vivid confusion, have no fundamental significance for economics. With a refereshed view on the essence of economics, we argue that the principles of economic theory form an essential methodological guide for the emergent line of research based on the use of so-called ‘evolutive’ models.
148 citations
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TL;DR: The vast literature on explosive phenomena in networked systems is reviewed to provide a coherent overview and perspective for future research to address the many vital questions that remained unanswered and to classify explosive phenomena based on underlying mechanisms.
Abstract: The emergence of large-scale connectivity and synchronization are crucial to the structure, function and failure of many complex socio-technical networks. Thus, there is great interest in analyzing...
148 citations
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TL;DR: A large class of statistical hypotheses testing procedures are exhibited that solve the problem of whether the players themselves can learn to play equilibrium strategies without assuming that they have prior knowledge of their opponents' strategies and/or payoffs.
148 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 |