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This paper considers the problem of understanding intellectual sharing/pooling arrangements and the construction of cultural commons arrangements. We argue that an adaptation of the approach pioneered by Elinor Ostrom and collaborators to commons arrangements in the natural environment may provide a template for the examination of constructed commons in the cultural environment. Such an approach promises to lead to a better understanding of how participants in commons and pooling arrangements structure their interactions in relation to the environment(s) within which they are embedded and with which they share interdependent relationships. We propose a framework for evaluating and comparing the contours of different pooling arrangements with an eye toward developing an understanding of the institutional and structural differences across arrangements and industries as well as the underlying contextual reasons for such differences. The proposed approach would draw upon case studies from a wide range of disciplines. Among other things, we argue that a theoretical approach to cultural constructed commons should consider rules pertaining to membership criteria, contribution and use of pooled resources, internal licensing conditions, management of external relationships, and institutional form along with the degree of collaboration among members, sharing of human capital, degrees of integration among participants, and whether there is a specified purpose to the arrangement.

We obtain quasicrystalline structures in Monte Carlo simulations of a simple two-component Lennard-Jones system in two dimensions. The quasicrystal, which shows tenfold symmetry, appears to be an equilibrium state of the system. Although the structure corresponds to tiling of the plane with rhombuses, it is not a Penrose pattern.

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In this paper we present the application of a novel methodology to scientific citation and collaboration networks. This methodology is designed for understanding the governing dynamics of evolving networks and relies on an attachment kernel, a scalar function of node properties, which stochastically drives the addition and deletion of vertices and edges. We illustrate how the kernel function of a given network can be extracted from the history of the network and discuss other possible applications.

Diverse cellular systems evolve to remarkably similar stationary states. We therefore have studied and simulated a purely topological model. We use a maximum-entropy argument to predict that the average number of /-sided cells adjacent to an n-sided cell, Miin), will be linear in n. One consequence is the empirically observed linearity of the total number of edges of cells adjacent to an /i-sided cell, known as the Aboav-Weaire law. The prevailing justification of that law is shown to be incorrect, and thus the apparently universal experimental slope of -5 remains unexplained. PACS numbers: 87.10.+e Soap bubbles, polycrystalline grain mosaics, and biological tissues are natural examples of random, spacefilling cellular networks. Despite length scales spanning geology [1], metallurgy [2], and biology [3], cellular networks have similar structure and evolve to a steady state, characterized by a scaling (stationary) distribution of cell sizes, shapes, and correlations. The similarity of the scaling state across systems molded by different physical forces has led many workers to seek an explanation independent of the driving forces [4][5][6][7][8]. Our purpose in this paper is to explore maximum entropy as an explanation for the similarity of many physical cellular networks once they have attained the scaling state.Among the properties of the scaling state, the probability Pn of cells with n sides is the most frequently measured in experimental systems. However, the best-obeyed empirical regularity pertains to two-cell correlations. The Aboav-Weaire law [2,9,10] states that on average the sum of the number of sides of the cells immediately adjacent to an /i-sided cell [11] [nmin)] is linear in n: nmin) ={6-a)n-\^{6a+ 1x2) ,(with a-^X (Aboav-Weaire), where jH2'^Jlin(n-6)^P" is the second moment of the P" distribution. (The first moment for networks of trivalent vertices must be 6.) We look upon the Aboav-Weaire law as consisting of three assertions: (i) nmin) is linear in n, (ii) The slope of nmin) is approximately 5, i.e., « -1, empirically, (iii) Whatever the slope, we have 6m(6) =36 + jU2. [This assertion is a direct result [9] of the linearity of nmin).]In this paper we focus on the two-cell correlation M/in), the average number of /-sided neighbors of an nsided cell. Using a maximum-entropy argument we predict that Miin) should be of the linear form M/in) =Aj-^nBi.We discuss the experimental status of this prediction.One consequence of linear Mjin) is the empirically observed linearity of the Aboav-Weaire law. The Aboav-Weaire slope is not fixed by our maximum-entropy analysis. Both by results of simulation and by statistical reasoning we show that a previous (''microreversibility") argument for linearity and slope 5 is incorrect. While our maximum-entropy approach reestablishes a theoretical basis for linearity, the value of the slope in experimental studies is now unexplained.Maximum-entropy argument,-The topology of cellular networks imposes constraints on the possible configurations of the cells, and in parti...