We demonstrate by mathematical analysis and systematic computer simulations that redistribution can lead to sustainable growth in a society. The human capital dynamics of each agent is described by a stochastic multiplicative process which, in the long run, leads to the destruction of individual human capital and the extinction of the individualistic society. When agents are linked by fully-redistributive taxation the situation might turn to individual growth in the long run. We consider that a government collects a proportion of income and reduces it by a fraction as costs for administration (efficiency losses). The remaining public good is equally redistributed to all agents. We derive conditions under which the destruction of human capital can be turned into sustainable growth, despite the losses from the random growth process and despite the administrative costs. Sustainable growth is induced by redistribution. This effect could be explained by a simple portfolio-effect which re-balances individual stochastic processes. <br />The findings are verified for three different tax schemes: proportional tax, taking proportional more from the rich, and proportionally more from the poor. We discuss which of these tax schemes is optimal with respect to maximize growth under a fixed rate of administrative costs, or with respect to maximize the governmental income. This leads us to some general conclusions about governmental decisions, the relation to public good games, and the use of taxation in a risk taking society. [more]

We investigate a simple variation of the Generalized Harmonic method for evolving the Einstein equations. A flat space wave equation for metric perturbations is separated from the Ricci tensor, with the rest of the Ricci tensor becoming a source for these wave equations. We demonstrate that this splitting method allows for the accurate simulation of compact objects, with gravitational field strengths less than or equal to those of neutron stars. This method could thus provide a straightforward path for general relativistic effects to be added to astrophysics simulations, such as in core collapse, accretion disks, and extreme mass ratio systems. [more]

The number of citations is a widely used metric for evaluating the scientific credit of papers, scientists and journals. However, it so happens that papers with fewer citations from prestigious scientists have a higher influence than papers with more citations. In this paper, we argue that by whom the paper is being cited is of greater significance than merely the number of citations. Accordingly, we propose an interactive model of author–paper bipartite networks as well as an iterative algorithm to obtain better rankings for scientists and their publications. The main advantage of this method is twofold: (i) it is a parameter-free algorithm; (ii) it considers the relationship between the prestige of scientists and the quality of their publications. We conducted real experiments on publications in econophysics, and used this method to evaluate the influence of related scientific journals. The comparison between the rankings by our method and simple citation counts suggests that our method is effective in distinguishing prestige from popularity. [more]

IMT has opened the call for applications for the Ph.D. Program in Economics, Markets, Institutions (deadline September 28th, 2011). [more]

Imagine storing all the computational information produced in the world in just one year: you would have a pile of DVDs able to reach the Moon and back. How about all the data collected since the beginning of the computer era? The quantity is so huge that traditional units of measurement cannot cope. For this reason a few years ago computer scientists started talking about “Big Data”, referring to the gathering, formatting, analysing and manipulating of a massive amount of digital information. [more]

A guest editorial by Dirk Helbing (ETHZ, Switzerland) presenting an ongoing debate on the Achilles' heels of current economic theories. [more]

During the spectacular financial market crashes and the resulted recession, there has been renewed assault on the Efficient Market Hypothesis (EMH) and its theoretical basis—mainstream economics. George Soros announced Oct. 2009 that he will put 50 million dollars over ten years to establish an “Institute for New Economic Thinking”. [more]

Recent research in recommender systems has shown that collaborative filtering algorithms are highly susceptible to attacks that insert biased profile data. Theoretical analyses and empirical experiments have shown that certain attacks can have a significant impact on the ... [more]

The Institute for Biocomputation and Physics of Complex Systems (BIFI) of the University of Zaragoza, Spain, is organizing the Conference "Networks: A Framework for cross-disciplinary applications" that will take place from February 3 to February 6, 2010. More information can be found at http://bifi.unizar.es/events/bifi2010/ Complex Networks have been the subject of intense research during the last decade. After the burst of activity in this field, it has been increasingly recognized that network theory has become an essential ingredient for understanding and modeling the structure and dynamics of complex systems. The application of complex networks concepts and tools has rapidly spread across disciplines as diverse as Epidemiology, Sociology, Neuroscience and Computer Science. The common methodological framework provided by network theory is thus a crossroads in which different scientific perspectives share efforts and gain knowledge to their own applications. The aim of BIFI2010 is to bring together scientists studying complex systems in Physics, Sociology, Computer Science, Neuroscience and Biology that strongly rely on the use of complex networks for their research. The topics to be covered include, but is not limited to, the following scientific problems: - Predictive power of Network Theory. - Complex Networks in Technology, including communication and wireless networks. - Structure and Dynamics of Biological Networked Systems at all organizational levels (from the genome to ecosystems). - Social Networks. - Evolutionary Game and Graphs Dynamics. - Algorithms in and for Complex Networks. - Synchronization in Complex Networks, including Brain and neural networks. - Epidemic Spreading in Heterogeneous Populations. [more]