Modern physics has hit a wall in a number of areas. Some proponents of information theory believe embracing it may help us to say, sew up the rift between general relativity and quantum mechanics. Or perhaps it’ll aid in detecting and comprehending dark matter and dark energy, which combined are thought to make up 95% of the known universe. As it stands, we have no idea what they are. Ironically, some hard data is required in order to elevate information theory. Until then, it remains theoretical.

Source: bigthink.com

Classic economic science is reaching the limits of its explanatory powers. Complexity science uses an increasingly larger set of different methods to analyze physical, biological, cultural, social, and economic factors, providing a broader understanding of the socio-economic dynamics involved in the development of nations worldwide. The use of tools developed in the natural sciences, such as thermodynamics, evolutionary biology, and analysis of complex systems, help us to integrate aspects, formerly reserved to the social sciences, with the natural sciences. This integration reveals details of the synergistic mechanisms that drive the evolution of societies. By doing so, we increase the available alternatives for economic analysis and provide ways to increase the efficiency of decision-making mechanisms in complex social contexts. This interdisciplinary analysis seeks to deepen our understanding of why chronic poverty is still common, and how the emergence of prosperous technological societies can be made possible. This understanding should increase the chances of achieving a sustainable, harmonious and prosperous future for humanity. The analysis evidences that complex fundamental economic problems require multidisciplinary approaches and rigorous application of the scientific method if we want to advance significantly our understanding of them. The analysis reveals viable routes for the generation of wealth and the reduction of poverty, but also reveals huge gaps in our knowledge about the dynamics of our societies and about the means to guide social development towards a better future for all.

 

The Wealth of Nations: Complexity Science for an Interdisciplinary Approach in Economics
Klaus Jaffe

Source: arxiv.org

The results from urban scaling in recent years have held the promise of increased efficiency to the societies who could actively control the distribution of their cities’ size. However, little evidence exists as to the factors which influence the level of urban unevenness, as expressed by the slope of the rank-size distribution, partly because the diversity of results found in the literature follows the heterogeneity of analysis specifications. In this study, I set up a meta-analysis of Zipf’s law which accounts for technical as well as topical factors of variations of Zipf’s coefficient. I found 86 studies publishing at least one empirical estimation of this coefficient and recorded their metadata into an open database. I regressed the 1962 corresponding estimates with variables describing the study and the estimation process as well as socio-demographic variables describing the territory under enquiry. A dynamic meta-analysis was also performed to look for factors of evolution of city size unevenness. The results of the most interesting models are presented in the article, whereas all analyses can be reproduced on a dedicated online platform. The results show that on average, 40% of the variation of Zipf’s coefficients is due to the technical choices. The main other variables associated with distinct evolutions are linked to the urbanisation process rather than the process of economic development and population growth. Finally, no evidence was found to support the effectiveness of past planning actions in modifying this urban feature.

 

Cottineau C (2017) MetaZipf. A dynamic meta-analysis of city size distributions. PLoS ONE 12(8): e0183919. https://doi.org/10.1371/journal.pone.0183919

Source: journals.plos.org

The arrangements of particles and forces in granular materials and particulate matter have a complex organization on multiple spatial scales that range from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate mathematical, statistical, physical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular materials (and particulate matter more generally) and explore the potential of such frameworks to provide a useful description of these materials and to enhance understanding of the underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular materials and other particulate matter.

 

Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett

Source: arxiv.org