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Register a new userZipfs, Heaps and Taylors laws are determined by the expansion into the adjacent possible
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Added by
Vito Domenico Pietro Servedio
Publication date
2018
fields
Physics
and research's language is
English
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Zipfs, Heaps and Taylors laws are ubiquitous in many different systems where innovation processes are at play. Together, they represent a compelling set of stylized facts regarding the overall statistics, the innovation rate and the scaling of fluctuations for systems as diverse as written texts and cities, ecological systems and stock markets. Many modeling schemes have been proposed in literature to explain those laws, but only recently a modeling framework has been introduced that accounts for the emergence of those laws without deducing the emergence of one of the laws from the others or without ad hoc assumptions. This modeling framework is based on the concept of adjacent possible space and its key feature of being dynamically restructured while its boundaries get explored, i.e., conditional to the occurrence of novel events. Here, we illustrate this approach and show how this simple modelling framework, instantiated through a modified Polyas urn model, is able reproduce Zipfs, Heaps and Taylors laws within a unique self-consistent scheme. In addition the same modelling scheme embraces other less common evolutionary laws (Hoppes model and Dirichlet processes) as particular cases.
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The interactions among human beings represent the backbone of our societies. How people interact, establish new connections, and allocate their activities among these links can reveal a lot of our social organization. Despite focused attention by very diverse scientific communities, we still lack a first-principles modeling framework able to account for the birth and evolution of social networks. Here, we tackle this problem by looking at social interactions as a way to explore a very peculiar space, namely the adjacent possible space, i.e., the set of individuals we can meet at any given point in time during our lifetime. We leverage on a recent mathematical formalization of the adjacent possible space to propose a first-principles theory of social exploration based on simple microscopic rules defining how people get in touch and interact. The new theory predicts both microscopic and macroscopic features of social networks. The most striking feature captured on the microscopic side is the probability for an individual, with already $k$ connections, to acquire a new acquaintance. On the macroscopic side, the model reproduces the main static and dynamic features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the innovation rate at the global and local level. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society and a mobile-phone-call network.
Taylors law quantifies the scaling properties of the fluctuations of the number of innovations occurring in open systems. Urn based modelling schemes have already proven to be effective in modelling this complex behaviour. Here, we present analytical estimations of Taylors law exponents in such models, by leveraging on their representation in terms of triangular urn models. We also highlight the correspondence of these models with Poisson-Dirichlet processes and demonstrate how a non-trivial Taylors law exponent is a kind of universal feature in systems related to human activities. We base this result on the analysis of four collections of data generated by human activity: (i) written language (from a Gutenberg corpus); (ii) a n online music website (Last.fm); (iii) Twitter hashtags; (iv) a on-line collaborative tagging system (Del.icio.us). While Taylors law observed in the last two datasets agrees with the plain model predictions, we need to introduce a generalization to fully characterize the behaviour of the first two datasets, where temporal correlations are possibly more relevant. We suggest that Taylors law is a fundamental complement to Zipfs and Heaps laws in unveiling the complex dynamical processes underlying the evolution of systems featuring innovation.
We present new measurements of scaling laws relating the luminosity of galaxies to the amplitude and shape of their dark matter halos. Early imaging and spectroscopic data from the Sloan Digital Sky Survey are used to make weak lensing measurements of the surface mass density contrast Delta Sigma_+ around classes of lens objects. This surface mass density contrast as a function of radius is a measure of the galaxy-mass correlation function (GMCF). Because spectroscopic redshifts are available for all lens objects, the mass and distance scales are well constrained. The GMCF measured around ~31,000 lenses is well fit by a power law of the form Delta Sigma_+ = (2.5+0.7-0.6) (R/1 Mpc)^{-0.8+-0.2} h M_sun pc^-2. We compare this GMCF to galaxy luminosity, type, and environment, and find that it varies strongly with all three. We quantify these variations by comparing the normalization of a fit to the inner 260 h^-1 kpc, M_260, to the galaxy luminosity. While M_260 is not strongly related to luminosity in bluest band u, there is a simple, linear relation between M_260 and luminosity in redder bands (g, r, i, and z). We test the universality of these mass-to-light scalings by independently measuring them for spiral and elliptical galaxies,and for galaxies in a variety of environments. We find remarkable consistency in these determinations in the red bands, especially i and z. This consistency across a wide range of systems suggests that the measured scaling represents an excellent cosmic average, and that the integrated star formation history of galaxies is strongly related to the dark matter environments in which they form.
Consensus about the universality of the power law feature in complex networks is experiencing profound challenges. To shine fresh light on this controversy, we propose a generic theoretical framework in order to examine the power law property. First, we study a class of birth-and-death networks that is ubiquitous in the real world, and calculate its degree distributions. Our results show that the tails of its degree distributions exhibits a distinct power law feature, providing robust theoretical support for the ubiquity of the power law feature. Second, we suggest that in the real world two important factors, network size and node disappearance probability, point to the existence of the power law feature in the observed networks. As network size reduces, or as the probability of node disappearance increases, then the power law feature becomes increasingly difficult to observe. Finally, we suggest that an effective way of detecting the power law property is to observe the asymptotic (limiting) behaviour of the degree distribution within its effective intervals.
We propose hypotheses describing the empirical finding of an association between the exponents of urban GDP scaling and Zipfs law for cities. These hypotheses represent various combinations of directional or reciprocal causal links between the two phenomena and include inter- and intra-city processes. Future theories and models can be motivated with and categorized according to these hypotheses. This paper intends to stimulate the discussion around the processes behind these phenomena and pave the way to a Unified Urban Theory.
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