Santa Fe Institute

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.

Networks in the Understanding of Economic Behaviors

Abstract: In this paper I discuss what we have learned about how the structure of social networks impacts economic behaviors, and why it is important to include network information in many economic studies. I also discuss some issues of estimating models of network formation, and some of the challenges of accounting for endogenous networks in analyzing interactions.

Googling Food Webs: Can an Eigenvector Measure Species' Importance for Coextinctions?

Abstract: A major challenge in ecology is forecasting the effects of species' extinctions, a pressing problem given current human impacts on the planet. Consequences of species losses such as secondary extinctions are difficult to forecast because species are not isolated, but interact instead in a complex network of ecological relationships. Because of their mutual dependence, the loss of a single species can cascade in multiple coextinctions. Here we show that an algorithm adapted from the one Google uses to rank web-pages can order species according to their importance for coextinctions, providing the sequence of losses that results in the fastest collapse of the network. Moreover, we use the algorithm to bridge the gap between qualitative (who eats whom) and quantitative (at what rate) descriptions of food webs. We show that our simple algorithm finds the best possible solution for the problem of assigning importance from the perspective of secondary extinctions in all analyzed networks. Our approach relies on network structure, but applies regardless of the specific dynamical model of species' interactions, because it identifies the subset of coextinctions common to all possible models, those that will happen with certainty given the complete loss of prey of a given predator. Results show that previous measures of importance based on the concept of “hubs” or number of connections, as well as centrality measures, do not identify the most effective extinction sequence. The proposed algorithm provides a basis for further developments in the analysis of extinction risk in ecosystems.

An empirical behavioral model of liquidity and volatility

Abstract: We develop a behavioral model for liquidity and volatility based on empirical regularities in trading order flow in the London Stock Exchange. This can be viewed as a very simple agent based model in which all components of the model are validated against real data. Our empirical studies of order flow uncover several interesting regularities in the way trading orders are placed and cancelled. The resulting simple model of order flow is used to simulate price formation under a continuous double auction, and the statistical properties of the resulting simulated sequence of prices are compared to those of real data. The model is constructed using one stock (AZN) and tested on 24 other stocks. For low volatility, small tick size stocks (called Group I) the predictions are very good, but for stocks outside Group I they are not good. For Group I, the model predicts the correct magnitude and functional form of the distribution of the volatility and the bid-ask spread, without adjusting any parameters based on prices. This suggests that at least for Group I stocks, the volatility and heavy tails of prices are related to market microstructure effects, and supports the hypothesis that, at least on short time scales, the large fluctuations of absolute returns are well described by a power law with an exponent that varies from stock to stock.

A general integrative model for scaling plant growth, carbon flux, and functional trait spectra

Abstract: Linking functional traits to plant growth is critical for scaling attributes of organisms to the dynamics of ecosystems and for understanding how selection shapes integrated botanical phenotypes. However, a general mechanistic theory showing how traits specifically influence carbon and biomass flux within and across plants is needed. Building on foundational work on relative growth rate, recent work on functional trait spectra, and metabolic scaling theory, here we derive a generalized trait-based model of plant growth. In agreement with a wide variety of empirical data, our model uniquely predicts how key functional traits interact to regulate variation in relative growth rate, the allometric growth normalizations for both angiosperms and gymnosperms, and the quantitative form of several functional trait spectra relationships. The model also provides a general quantitative framework to incorporate additional leaf-level trait scaling relationships and hence to unite functional trait spectra with theories of relative growth rate, and metabolic scaling. We apply the model to calculate carbon use efficiency. This often ignored trait, which may influence variation in relative growth rate, appears to vary directionally across geographic gradients. Together, our results show how both quantitative plant traits and the geometry of vascular transport networks can be merged into a common scaling theory. Our model provides a framework for predicting not only how traits covary within an integrated allometric phenotype but also how trait variation mechanistically influences plant growth and carbon flux within and across diverse ecosystems.

The scaling of human interactions with city size

Abstract: The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases.