Can a human society be constrained in such a way that self-organization will thereafter tend to produce outcomes that advance the goals of the society? Such a society would be self-organizing in the sense that individuals who pursue only their own interests would none-the-less act in the interests of the society as a whole, irrespective of any intention to do so. This paper identifies the conditions that must be met if such a self-organizing society is to emerge. It demonstrates that the key enabling requirement for a self-organizing society is consequence-capture. Broadly this means that all agents in the society must capture sufficient of the benefits (and harms) that are produced by their actions on the goals of the society. Consequence-capture can be organized in a society by appropriate management (systems of evolvable constraints) that suppresses free riders and supports pro-social actions. In human societies these constraints include institutions such as systems of governance and social norms. The paper identifies ways of organizing societies so that effective governance will also self-organize. This will produce a fully self-organizing society in which the interests of all agents (including individuals, associations, firms, multi-national corporations, political organizations, institutions and governments) are aligned with the interests of the society as a whole.

 

The Self-Organizing Society: A Grower’s Guide
John E. Stewart

Source: arxiv.org

We present a model of contagion that unifies and generalizes existing models of the spread of social influences and micro-organismal infections. Our model incorporates individual memory of exposure to a contagious entity (e.g., a rumor or disease), variable magnitudes of exposure (dose sizes), and heterogeneity in the susceptibility of individuals. Through analysis and simulation, we examine in detail the case where individuals may recover from an infection and then immediately become susceptible again (analogous to the so-called SIS model). We identify three basic classes of contagion models which we call \textit{epidemic threshold}, \textit{vanishing critical mass}, and \textit{critical mass} classes, where each class of models corresponds to different strategies for prevention or facilitation. We find that the conditions for a particular contagion model to belong to one of the these three classes depend only on memory length and the probabilities of being infected by one and two exposures respectively. These parameters are in principle measurable for real contagious influences or entities, thus yielding empirical implications for our model. We also study the case where individuals attain permanent immunity once recovered, finding that epidemics inevitably die out but may be surprisingly persistent when individuals possess memory.

 

A generalized model of social and biological contagion
Peter Sheridan Dodds, Duncan J. Watts

Source: arxiv.org

Surprisingly common outliers of a distribution tail, known as Dragon Kings, are seen in many complex systems. It has been argued that the general conditions for Dragon Kings in self-organized systems are high system coupling and low heterogeneity. In this Letter, we introduce a novel mechanism of Dragon Kings by discussing two closely-related stylized models of cascading failures. Although the first variant (based on simple contagion spreading and inoculation) exhibits well-studied self-organized criticality, the second one (based on both simple and complex contagion spreading) creates self-organized Dragon Kings in the failure size distribution. Next, we begin to understand the mechanistic origin of these Dragon Kings by mapping the probability of an initial cascade to a generalized birthday problem, which helps demonstrate that the Dragon King cascade is due to initial failures whose size exceeds a threshold that is infinitesimal compared to the size of the network. We use this finding to predict the onset of Dragon Kings with high accuracy using only logistic regression. Finally, we devise a simple control strategy that can decrease the frequency of Dragon Kings by orders of magnitude. We conclude with remarks on the applicability of both models to natural and engineered systems.

 

The Self-Organization of Dragon Kings
Yuansheng Lin, Keith Burghardt, Martin Rohden, Pierre-André Noël, Raissa M. D’Souza

Source: arxiv.org

Socioeconomic inequalities in cities are embedded in space and result in neighborhood effects, whose harmful consequences have proved very hard to counterbalance efficiently by planning policies alone. Considering redistribution of money flows as a first step toward improved spatial equity, we study a bottom-up approach that would rely on a slight evolution of shopping mobility practices. Building on a database of anonymized card transactions in Madrid and Barcelona, we quantify the mobility effort required to reach a reference situation where commercial income is evenly shared among neighborhoods. The redirections of shopping trips preserve key properties of human mobility, including travel distances. Surprisingly, for both cities only a small fraction (∼5%) of trips need to be modified to reach equality situations, improving even other sustainability indicators. The method could be implemented in mobile applications that would assist individuals in reshaping their shopping practices, to promote the spatial redistribution of opportunities in the city.

 

Crowdsourcing the Robin Hood effect in cities
Thomas Louail, Maxime Lenormand, Juan Murillo Arias and José J. Ramasco
Applied Network Science 2017 2:11
DOI: 10.1007/s41109-017-0026-3

Source: appliednetsci.springeropen.com

The cryptocurrency market has reached a record of $91 billion market capitalization in May 2017, after months of steady growth. Despite its increasing relevance in the financial world, however, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behavior of one (Bitcoin) or few cryptocurrencies. Here, we consider the history of the entire market and analyze the behavior of 1, 469 cryptocurrencies introduced since April 2013. We reveal that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing exponentially, several statistical properties of the market have been stable for years. These include the number of active cryptocurrencies, the market share distribution and the turnover of cryptocurrencies. Adopting an ecological perspective, we show that the so-called neutral model of evolution is able to reproduce a number of key empirical observations, despite its simplicity and the assumption of no selective advantage of one cryptocurrency over another. Our results shed light on the properties of the cryptocurrency market and establish a first formal link between ecological modeling and the study of this growing system. We anticipate they will spark further research in this direction.

 

Bitcoin ecology: Quantifying and modelling the long-term dynamics of the cryptocurrency market
Abeer ElBahrawy, Laura Alessandretti, Anne Kandler, Romualdo Pastor-Satorras, Andrea Baronchelli

Source: arxiv.org