The world economy has returned to robust growth after the 2008 global financial crisis. But some familiar risks are creeping back, and new ones have emerged.

Source: www.mckinsey.com

Today, the massive use of information and communication technologies (ICT) has made it possible to attach a traceable set of data to almost any person. We argue that these data provide the opportunity to build a ‘physics of society’: describing a society — composed of many interacting heterogeneous entities (people, businesses, institutions) — as a physical system. While important ethical implications have to be taken into account, the benefits in developing such physics of society would be tremendous. Indeed, it could help understanding, anticipating and forecasting future societal trends and human behavioural responses, and their associated uncertainty; or address societal challenges in which globally networked risks play a role. A case in point is modern epidemiology and its success in predicting the large-scale spreading of infectious diseases.

 

Physics of humans, physics for society
Guido Caldarelli, Sarah Wolf & Yamir Moreno 
Nature Physics volume 14, page 870 (2018)

Source: www.nature.com

Distribution networks such as vasculature systems or urban transportation pathways are prevalent in our world. Understanding how different kinds of transport systems are organized to allow for efficient function in their environments and in the presence of constraints on material costs is currently an open area of investigation. In this study, we use methods from network science to compare and contrast the structure of two different classes of biological distribution networks: mycelial fungi and rodent brain vasculature. While each of these systems have been studied separately, less work has focused on understanding the diversity of their network organization. Here, we first examine several measures that characterize network connectivity on varying scales, finding that—although both systems have highly constrained network layouts—there are quantifiable differences in their architectures. Furthermore, using network analyses that specifically consider the embedding of these transport networks into real space, we observe that the two types of systems display distinct tradeoffs in network correlates of material cost, efficiency, and robustness. Together, our results provide evidence that while different distribution networks have general resemblances, they also exhibit variable design features that could reflect differences in their functions, environmental conditions, or development.

 

Papadopoulos L, Blinder P, Ronellenfitsch H, Klimm F, Katifori E, Kleinfeld D, et al. (2018) Comparing two classes of biological distribution systems using network analysis. PLoS Comput Biol 14(9): e1006428. https://doi.org/10.1371/journal.pcbi.1006428

Source: journals.plos.org

Link prediction is one of the fundamental research problems in network analysis. Intuitively, it involves identifying the edges that are most likely to be added to a given network, or the edges that appear to be missing from the network when in fact they are present. Various algorithms have been proposed to solve this problem over the past decades. For all their benefits, such algorithms raise serious privacy concerns, as they could be used to expose a connection between two individuals who wish to keep their relationship private. With this in mind, we investigate the ability of such individuals to evade link prediction algorithms. More precisely, we study their ability to strategically alter their connections so as to increase the probability that some of their connections remain unidentified by link prediction algorithms. We formalize this question as an optimization problem, and prove that finding an optimal solution is NP-complete. Despite this hardness, we show that the situation is not bleak in practice. In particular, we propose two heuristics that can easily be applied by members of the general public on existing social media. We demonstrate the effectiveness of those heuristics on a wide variety of networks and against a plethora of link prediction algorithms.

 

Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network
Marcin Waniek, Kai Zhou, Yevgeniy Vorobeychik, Esteban Moro, Tomasz P. Michalak, Talal Rahwan

Source: arxiv.org

We introduce a computational framework for consciousness. We hypothesize that emergent rules of computation in the Universe lead to life and consciousness. We live in a Universe that has a substrate capable of computing or information processing. We suggest that in principle, any Universe that is capable of supporting information processing and has energy can evolve life and consciousness. We hypothesize that the Universe encodes rules in the form of physical laws that allow for the emergence of both life and conscious organisms. A key insight is that there are different levels of consciousness starting from atoms to organisms to galaxies. We propose a metric of complexity that can quantify the amount of consciousness in a system by measuring both the amount of information and the capability to process that information. We hope that this framework will allow us to better understand consciousness and design machines that are conscious and empathetic. Consciousness and life may be a general phenomenon in our information rich Universe and their maybe other structures designed or otherwise that may be capable of it. Consciousness is an emergent property of an information rich Universe that is capable of processing that information in complex myriad ways.

 

Banerjee, Soumya. 2018. “Emergent Rules of Computation in the Universe Lead to Life and Consciousness: A Computational Framework for Consciousness.” OSF Preprints. June 22. doi:10.31219/osf.io/ce9m6.

Source: osf.io