Showing papers by "Santa Fe Institute published in 2001"
TL;DR: It is shown that these collaboration networks form "small worlds," in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances.
Abstract: The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE (biomedical research), the Los Alamos e-Print Archive (physics), and NCSTRL (computer science). I show that these collaboration networks form “small worlds,” in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.
4,529 citations
TL;DR: It is demonstrated that in some cases random graphs with appropriate distributions of vertex degree predict with surprising accuracy the behavior of the real world, while in others there is a measurable discrepancy between theory and reality, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.
Abstract: Recent work on the structure of social networks and the internet has focused attention on graphs with distributions of vertex degree that are significantly different from the Poisson degree distributions that have been widely studied in the past. In this paper we develop in detail the theory of random graphs with arbitrary degree distributions. In addition to simple undirected, unipartite graphs, we examine the properties of directed and bipartite graphs. Among other results, we derive exact expressions for the position of the phase transition at which a giant component first forms, the mean component size, the size of the giant component if there is one, the mean number of vertices a certain distance away from a randomly chosen vertex, and the average vertex-vertex distance within a graph. We apply our theory to some real-world graphs, including the worldwide web and collaboration graphs of scientists and Fortune 1000 company directors. We demonstrate that in some cases random graphs with appropriate distributions of vertex degree predict with surprising accuracy the behavior of the real world, while in others there is a measurable discrepancy between theory and reality, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.
3,655 citations
TL;DR: A general model is derived, based on principles of biochemical kinetics and allometry, that characterizes the effects of temperature and body mass on metabolic rate of microbes, ectotherms, endotherms (including those in hibernation), and plants in temperatures ranging from 0° to 40°C.
Abstract: We derive a general model, based on principles of biochemical kinetics and allometry, that characterizes the effects of temperature and body mass on metabolic rate. The model fits metabolic rates of microbes, ectotherms, endotherms (including those in hibernation), and plants in temperatures ranging from 0° to 40°C. Mass- and temperature-compensated resting metabolic rates of all organisms are similar: The lowest (for unicellular organisms and plants) is separated from the highest (for endothermic vertebrates) by a factor of about 20. Temperature and body size are primary determinants of biological time and ecological roles.
3,165 citations
TL;DR: In this article, the authors constructed networks of collaboration between scientists in each of these disciplines and proposed a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and the number other scientists with whom they coauthored those papers.
Abstract: Using computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. Here we study a variety of nonlocal statistics for these networks, such as typical distances between scientists through the network, and measures of centrality such as closeness and betweenness. We further argue that simple networks such as these cannot capture variation in the strength of collaborative ties and propose a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they coauthored those papers.
2,528 citations
TL;DR: Using computer databases of scientific papers in physics, biomedical research, and computer science, a network of collaboration between scientists in each of these disciplines is constructed, and a number of measures of centrality and connectedness in the same networks are studied.
Abstract: Using computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. We study a variety of statistical properties of our networks, including numbers of papers written by authors, numbers of authors per paper, numbers of collaborators that scientists have, existence and size of a giant component of connected scientists, and degree of clustering in the networks. We also highlight some apparent differences in collaboration patterns between the subjects studied. In the following paper, we study a number of measures of centrality and connectedness in the same networks.
2,031 citations
TL;DR: This article found that the canonical model is not supported in any society studied, and that group-level differences in economic organization and the degree of market integration explain a substantial portion of the behavioral variation across societies.
Abstract: We can summarize our results as follows. First, the canonical model is not supported in any society studied. Second, there is considerably more behavioral variability across groups than had been found in previous cross-cultural research, and the canonical model fails in a wider variety of ways than in previous experiments. Third, group-level differences in economic organization and the degree of market integration explain a substantial portion of the behavioral variation across societies: the higher the degree of market integration and the higher the payoffs to cooperation, the greater the level of cooperation in experimental games. Fourth, individual-level economic and demographic variables do not explain behavior either within or across groups. Fifth, behavior in the experiments is generally consistent with economic patterns of everyday life in these societies.
2,019 citations
TL;DR: It is shown that the probability of a pair of scientists collaborating increases with the number of other collaborators they have in common, and that the probabilities of a particular scientist acquiring new collaborators increases withThe number of his or her past collaborators.
Abstract: We study empirically the time evolution of scientific collaboration networks in physics and biology. In these networks, two scientists are considered connected if they have coauthored one or more papers together. We show that the probability of a pair of scientists collaborating increases with the number of other collaborators they have in common, and that the probability of a particular scientist acquiring new collaborators increases with the number of his or her past collaborators. These results provide experimental evidence in favor of previously conjectured mechanisms for clustering and power-law degree distributions in networks.
1,867 citations
TL;DR: The authors found that the advantages of the children of successful parents go beyond the benefits of superior education, the inheritance of wealth, or the genetic inheritance of cognitive ability, and suggested that noncognitive personality variables such as attitudes towards risk, ability to adapt to new economic conditions, hard work, and the rate of time preference affect both earning and the transmission of economic status across generations.
Abstract: We survey the determinants of earnings and propose a framework for understanding labor market success. We suggest that the advantages of the children of successful parents go considerably beyond the benefits of superior education, the inheritance of wealth, or the genetic inheritance of cognitive ability. We suggest that noncognitive personality variables, such as attitudes towards risk, ability to adapt to new economic conditions, hard work, and the rate of time preference affect both earning and the transmission of economic status across generations.
1,124 citations
TL;DR: It is shown that graphs of words in sentences display two important features recently found in a disparate number of complex systems, the so called small–world effect and a scale–free distribution of degrees.
Abstract: Words in human language interact in sentences in non-random ways, and allow humans to construct an astronomic variety of sentences from a limited number of discrete units. This construction process is extremely fast and robust. The co-occurrence of words in sentences reflects language organization in a subtle manner that can be described in terms of a graph of word interactions. Here, we show that such graphs display two important features recently found in a disparate number of complex systems. (i) The so called small-world effect. In particular, the average distance between two words, d (i.e. the average minimum number of links to be crossed from an arbitrary word to another), is shown to be d approximately equal to 2-3, even though the human brain can store many thousands. (ii) A scale-free distribution of degrees. The known pronounced effects of disconnecting the most connected vertices in such networks can be identified in some language disorders. These observations indicate some unexpected features of language organization that might reflect the evolutionary and social history of lexicons and the origins of their flexibility and combinatorial nature.
1,026 citations
TL;DR: A general quantitative model based on fundamental principles for the allocation of metabolic energy between maintenance of existing tissue and the production of new biomass is derived to predict the parameters governing growth curves from basic cellular properties and derive a single parameterless universal curve that describes the growth of many diverse species.
Abstract: Several equations have been proposed to describe ontogenetic growth trajectories for organisms justified primarily on the goodness of fit rather than on any biological mechanism. Here, we derive a general quantitative model based on fundamental principles for the allocation of metabolic energy between maintenance of existing tissue and the production of new biomass. We thus predict the parameters governing growth curves from basic cellular properties and derive a single parameterless universal curve that describes the growth of many diverse species. The model provides the basis for deriving allometric relationships for growth rates and the timing of life history events.
1,021 citations
TL;DR: Using computer simulations, it is found that models that incorporate all of these features reproduce many of the features of real social networks, including high levels of clustering or network transitivity and strong community structure in which individuals have more links to others within their community than to individuals from other communities.
Abstract: We propose some simple models of the growth of social networks, based on three general principles: (1). meetings take place between pairs of individuals at a rate that is high if a pair has one or more mutual friends and low otherwise; (2). acquaintances between pairs of individuals who rarely meet decay over time; (3). there is an upper limit on the number of friendships an individual can maintain. Using computer simulations, we find that models that incorporate all of these features reproduce many of the features of real social networks, including high levels of clustering or network transitivity and strong community structure in which individuals have more links to others within their community than to individuals from other communities.
TL;DR: This paper showed that the causal-state representation of ∈-machine is the minimal one consistent with accurate prediction and established several results on ∈machine optimality and uniqueness and on how ∆-machines compare to alternative representations.
Abstract: Computational mechanics, an approach to structural complexity, defines a process's causal states and gives a procedure for finding them. We show that the causal-state representation—an ∈-machine—is the minimal one consistent with accurate prediction. We establish several results on ∈-machine optimality and uniqueness and on how ∈-machines compare to alternative representations. Further results relate measures of randomness and structural complexity obtained from ∈-machines to those from ergodic and information theories.
TL;DR: An efficient algorithm is described that can measure an observable quantity in a percolation system for all values of the site or bond occupation probability from zero to one in an amount of time that scales linearly with the size of the system.
Abstract: We describe in detail an efficient algorithm for studying site or bond percolation on any lattice. The algorithm can measure an observable quantity in a percolation system for all values of the site or bond occupation probability from zero to one in an amount of time that scales linearly with the size of the system. We demonstrate our algorithm by using it to investigate a number of issues in percolation theory, including the position of the percolation transition for site percolation on the square lattice, the stretched exponential behavior of spanning probabilities away from the critical point, and the size of the giant component for site percolation on random graphs.
TL;DR: It is concluded that grown graphs, however randomly they are constructed, are fundamentally different from their static random graph counterparts.
Abstract: We analyze a minimal model of a growing network. At each time step, a new vertex is added; then, with probability $\ensuremath{\delta},$ two vertices are chosen uniformly at random and joined by an undirected edge. This process is repeated for t time steps. In the limit of large t, the resulting graph displays surprisingly rich characteristics. In particular, a giant component emerges in an infinite-order phase transition at $\ensuremath{\delta}=1/8.$ At the transition, the average component size jumps discontinuously but remains finite. In contrast, a static random graph with the same degree distribution exhibits a second-order phase transition at $\ensuremath{\delta}=1/4,$ and the average component size diverges there. These dramatic differences between grown and static random graphs stem from a positive correlation between the degrees of connected vertices in the grown graph---older vertices tend to have higher degree, and to link with other high-degree vertices, merely by virtue of their age. We conclude that grown graphs, however randomly they are constructed, are fundamentally different from their static random graph counterparts.
University of Ioannina1, Tufts University2, Columbia University3, Harlem Hospital Center4, Rafael Advanced Defense Systems5, Centers for Disease Control and Prevention6, Santa Fe Institute7, Los Alamos National Laboratory8, French Institute of Health and Medical Research9, National Institutes of Health10, Harvard University11, Duke University12
TL;DR: Perinatal HIV-1 transmission occurs in only 1% of treated women with RNA virus loads <1000 copies/mL and may be almost eliminated with antiretroviral prophylaxis accompanied by suppression of maternal viremia.
Abstract: In a collaboration of 7 European and United States prospective studies, 44 cases of vertical human immunodeficiency virus type 1 (HIV-1) transmission were identified among 1202 women with RNA virus loads 500 copies/mL. Perinatal HIV-1 transmission occurs in only 1% of treated women with RNA virus loads <1000 copies/mL and may be almost eliminated with antiretroviral prophylaxis accompanied by suppression of maternal viremia.
TL;DR: In this paper, a principal-agent relationship between an employer and an employee is considered, where effort is not contractible, but is elicited through employer incentive mechanisms and preferences that allow the employer to elicit effort at lower cost incentive enhancing.
Abstract: Suppose there is a principal-agent relationship between employer and employee in which effort is not contractible, but is elicited through employer incentive mechanisms. We term preferences that allow the employer to elicit effort at lower cost incentive enhancing. We analyze how such preferences affect earnings, nd then provide evidence that one of the relevant behavioral traits, efficacy, as well as other psychological aspects of individuals, are significant influences on earnings. We conclude that measures of cognitive performance are not sufficient indicators of the effectiveness of schools in promoting student labor market success, incentive enhancing preferences are irreducibly heterogeneous, incentive enhancing preferences help explain the persistence of poverty over generations within families and, unlike cognitive skills, incentive-enhancing traits need not be welfare increasing for their bearers.
TL;DR: This paper proposes to extend the explanatory level for phenotypic evolution from fitness considerations alone to include the topological structure of phenotype space as induced by the genotype-phenotype map, and introduces the mathematical concepts and tools necessary to formalize the notion of accessibility pre-topology relative to which the authors can speak of continuity in the genotypes-phenotypes map and in evolutionary trajectories.
TL;DR: The quadratic map is used for the site dynamics with different coupling schemes such as global coupling, nearest neighbor coupling, intermediate range coupling, random coupling, small world coupling and scale free coupling.
Abstract: Spectral properties of coupled map lattices are described. Conditions for the stability of spatially homogeneous chaotic solutions are derived using linear stability analysis. Global stability analysis results are also presented. The analytical results are supplemented with numerical examples. The quadratic map is used for the site dynamics with different coupling schemes such as global coupling, nearest neighbor coupling, intermediate range coupling, random coupling, small world coupling and scale free coupling.
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TL;DR: The possibility of default limits available liquidity as mentioned in this paper, and if the potential default draws nearer, a liquidity crisis may ensue, causing a crash in asset prices, even if the probability of default barely changes, and even if no defaults subsequently materialize.
Abstract: The possibility of default limits available liquidity. If the potential default draws nearer, a liquidity crisis may ensue, causing a crash in asset prices, even if the probability of default barely changes, and even if no defaults subsequently materialize. Introducing default and limited collateral into general equilibrium theory (GE) allows for a theory of endogenous contracts, including endogenous margin requirements on loans. This in turn allows GE to explain liquidity and liquidity crises in equilibrium. A formal definition of liquidity is presented. When new information raises the probability and shortens the horizon over which a fixed income asset may default, its drop in price may be much greater than its objective drop in value for two reasons: the drop in value reduces the relative wealth of its natural buyers and also endogenously raises the margin required for its purchase. The liquidity premium rises, and there may be spillovers in which other assets crash in price even though their probability of default did not change.
TL;DR: It is shown that coincident interests are not a prerequisite for linguistic communication, and many of the results derived previously can be expected also under more realistic models of society.
Abstract: The “costly signaling” hypothesis proposes that animal signals are kept honest by appropriate signal costs. We show that to the contrary, signal cost is unnecessary for honest signaling even when interests conflict. We illustrate this principle by constructing examples of cost-free signaling equilibria for the two paradigmatic signaling games of Grafen (1990) and Godfray (1991). Our findings may explain why some animal signals use cost to ensure honesty whereas others do not and suggest that empirical tests of the signaling hypothesis should focus not on equilibrium cost but, rather, on the cost of deviation from equilibrium. We use these results to apply costly signaling theory to the low-cost signals that make up human language. Recent game theoretic models have shown that several key features of language could plausibly arise and be maintained by natural selection when individuals have coincident interests. In real societies, however, individuals do not have fully coincident interests. We show that coincident interests are not a prerequisite for linguistic communication, and find that many of the results derived previously can be expected also under more realistic models of society.
TL;DR: It is conjecture that the small world pattern arises from the compact design in which many elements share a small, close physical neighborhood plus the fact that the system must define a single connected component (which requires shortcuts connecting different integrated clusters).
Abstract: Recent theoretical studies and extensive data analyses have revealed a common feature displayed by biological, social, and technological networks: the presence of small world patterns. Here we analyze this problem by using several graphs obtained from one of the most common technological systems: electronic circuits. It is shown that both analogic and digital circuits exhibit small world behavior. We conjecture that the small world pattern arises from the compact design in which many elements share a small, close physical neighborhood plus the fact that the system must define a single connected component (which requires shortcuts connecting different integrated clusters). The degree distributions displayed are consistent with a conjecture concerning the sharp cutoffs associated to the presence of costly connections [Amaral et al., Proc. Natl. Acad. Sci. USA 97, 11 149 (2000)], thus providing a limit case for the classes of universality of small world patterns from real, artificial networks. The consequences for circuit design are outlined.
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TL;DR: In this paper, it was shown that one's acquaintances, one's immediate neighbors in the acquaintance network, are far from being a random sample of the population, and that this biases the numbers of neighbors two and more steps away.
Abstract: Recent work has demonstrated that many social networks, and indeed many networks of other types also, have broad distributions of vertex degree. Here we show that this has a substantial impact on the shape of ego-centered networks, i.e., sets of network vertices that are within a given distance of a specified central vertex, the ego. This in turn affects concepts and methods based on ego-centered networks, such as snowball sampling and the "ripple effect". In particular, we argue that one's acquaintances, one's immediate neighbors in the acquaintance network, are far from being a random sample of the population, and that this biases the numbers of neighbors two and more steps away. We demonstrate this concept using data drawn from academic collaboration networks, for which, as we show, current simple theories for the typical size of ego-centered networks give numbers that differ greatly from those measured in reality. We present an improved theoretical model which gives significantly better results.
TL;DR: In this paper, the authors give a detailed proof for two discrete analogues of Courant's Nodal Domain Theorem (CNDT) and show that they are equivalent.
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TL;DR: Barbera as discussed by the authors replaced the decisive voter with the weaker notion of a pivotal voter, thereby shortening the first step, but complicating the second step, and gave three brief proofs, all of which turn on replacing the decisive/pivotal voter with an extremely pivotal voter (a voter who by unilaterally changing his vote can move some alternative from the bottom of the social ranking to the top).
Abstract: Arrow's original proof of his impossibility theorem proceeded in two steps: showing the existence of a decisive voter, and then showing that a decisive voter is a dictator. Barbera replaced the decisive voter with the weaker notion of a pivotal voter, thereby shortening the first step, but complicating the second step. I give three brief proofs, all of which turn on replacing the decisive/pivotal voter with an extremely pivotal voter (a voter who by unilaterally changing his vote can move some alternative from the bottom of the social ranking to the top), thereby simplifying both steps in Arrow's proof. My first proof is the most straightforward, and the second uses Condorcet preferences (which are transformed into each other by moving the bottom alternative to the top). The third (and shortest) proof proceeds by reinterpreting Step 1 of the first proof as saying that all social decisions are made the same way (neutrality).
TL;DR: In this paper, Ohira and Sawatari presented a simple model of computer network traffic dynamics and showed that a phase transition point is present separating the low-traffic phase with no congestion from the congestion phase as the packet creation rate increases.
Abstract: In a recent study, Ohira and Sawatari presented a simple model of computer network traffic dynamics. These authors showed that a phase transition point is present separating the low-traffic phase with no congestion from the congestion phase as the packet creation rate increases. We further investigated this model by relaxing the network topology using a random location of routers. It is shown that the model exhibits nontrivial scaling properties close to the critical point, which reproduce some of the observed real Internet features. At criticality, the net shows maximum information transfer and efficiency. It is shown that some of the key properties of this model are shared by highway traffic models, as previously conjectured by some authors. The relevance to Internet dynamics and to the performance of parallel arrays of processors is discussed.
TL;DR: It is argued that both the general properties of the sequence structure map of RNA secondary structures and the ease with which the design tool finds bistable RNAs strongly indicates that RNA switches are easily accessible in evolution.
Abstract: We show that the problem of designing RNA sequences that can fold into multiple stable secondary structures can be transformed into a combinatorial optimization problem that can be solved by means of simple heuristics. Hence it is feasible to design RNA switches with prescribed structural alternatives. We discuss the theoretical background and present an efficient tool that allows the design of various types of switches. We argue that both the general properties of the sequence structure map of RNA secondary structures and the ease with which our design tool finds bistable RNAs strongly indicates that RNA switches are easily accessible in evolution. Thus conformational switches are yet another function for which RNA can be employed.
TL;DR: In this paper, the authors construct a computer simulation of a repeated double-auction market, designed to match those in experimental-market settings with human subjects, to model complex interactions among artificially-intelligent traders endowed with varying degrees of learning capabilities.
Abstract: We construct a computer simulation of a repeated double-auction market, designed to match those in experimental-market settings with human subjects, to model complex interactions among artificially-intelligent traders endowed with varying degrees of learning capabilities. In the course of six different experimental designs, we investigate a number of features of our agent-based model: the price efficiency of the market, the speed at which prices converge to the rational expectations equilibrium price, the dynamics of the distribution of wealth among the different types of AI-agents, trading volume, bid/ask spreads, and other aspects of market dynamics. We are able to replicate several endings of human-based experimental markets, however, we also and intriguing differences between agent-based and human-based experiments.
TL;DR: Algorithms, related to metric and ordinal multidimensional scaling algorithms first developed in the mathematical psychology literature, which construct explicit, quantitative coordinates for points in shape space given experimental data such as hemagglutination inhibition assays, or other general affinity assays are provided.
TL;DR: The predictions of the phenomenological approach on the single-shape landscape are very well reproduced by replication and mutation kinetics of tRNAphe, and the results are in excellent agreement with the results derived from the birth-and-death model.