Chinese urbanization 2050: SD modeling and process simulation

Is Chinese urbanization going to take a long time, or can its development goal be achieved by the government in a short time? What is the highest stable urbanization level that China can reach? When can China complete its urbanization? To answer these questions, this paper presents a system dynamic (SD) model of Chinese urbanization, and its validity and simulation are justified by a stock-flow test and a sensitivity analysis using real data from 1998 to 2013. Setting the initial conditions of the simulation by referring to the real data of 2013, the multi-scenario analysis from 2013 to 2050 reveals that Chinese urbanization will reach a level higher than 70% in 2035 and then proceed to a slow urbanization stage regardless of the population policy and GDP growth rate settings; in 2050, Chinese urbanization levels will reach approximately 75%, which is a stable and equilibrium level for China. Thus, it can be argued that Chinese urbanization is a long social development process that will require approximately 20 years to complete and that the ultimate urbanization level will be 75–80%, which means that in the distant future, 20–25% of China’s population will still settle in rural regions of China.


Chinese urbanization 2050: SD modeling and process simulation
GU Chao Lin, GUAN Wei Hua, LIU He Lin

SCIENCE CHINA Earth Sciences 60(6), 1067-1082(2017);  10.1007/s11430-016-9022-2


The spread of fake news by social bots

The massive spread of fake news has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of digital misinformation and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. However, to date, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand claims on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots play a key role in the spread of fake news. Accounts that actively spread misinformation are significantly more likely to be bots. Automated accounts are particularly active in the early spreading phases of viral claims, and tend to target influential users. Humans are vulnerable to this manipulation, retweeting bots who post false news. Successful sources of false and biased claims are heavily supported by social bots. These results suggests that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.


The spread of fake news by social bots

Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Alessandro Flammini, Filippo Menczer


The Ninth International Conference on Guided Self-Organisation (GSO-2018) : Information Geometry and Statistical Physics

March 26 – 28, 2018
Max Planck Institute for Mathematics in the Sciences


The goal of Guided Self-Organization (GSO) is to leverage the strengths of self-organization (i.e., its simplicity, parallelization, adaptability, robustness, scalability) while still being able to direct the outcome of the self-organizing process. GSO typically has the following features:

(i) An increase in organization (i.e., structure and/or functionality) over time;

(ii) Local interactions that are not explicitly guided by any external agent;

(iii) Task-independent objectives that are combined with task-dependent constraints.

GSO-2018 is the 9th conference in a bi-annual series on GSO. Recent research is starting to indicate that information geometry, nonequilibrium statistical physics in general, and the thermodynamics of computation in particular, all play a key role in GSO. Accordingly, a particular focus of this conference will be the interplay of those three topics as revealed by their relationship with GSO.


AI and Beyond | NECSI

A practical guide for decision makers to the transformation of business
Artificial intelligence is changing the fundamentals of business. There are new ways to improve performance and new business opportunities. As AI is adopted the role of human beings will change. Understanding how to chart this transition is increasingly central to entrepreneurs, executives and the organizations they lead. What functions do you fully automate with AI, what functions do you augment with AI, and what functions should rely on human intelligence? Complex systems science reveals the different and complementary strengths of human and artificial intelligence, and how they can be combined for performance advantage in business.


Why we live in hierarchies: a quantitative treatise

This book is concerned with the various aspects of hierarchical collective behaviour which is manifested by most complex systems in nature. From the many of the possible topics, we plan to present a selection of those that we think are useful from the point of shedding light from very different directions onto our quite general subject. Our intention is to both present the essential contributions by the existing approaches as well as go significantly beyond the results obtained by traditional methods by applying a more quantitative approach then the common ones (there are many books on qualitative interpretations). In addition to considering hierarchy in systems made of similar kinds of units, we shall concentrate on problems involving either dominance relations or the process of collective decision-making from various viewpoints.


Why we live in hierarchies: a quantitative treatise
Anna Zafeiris, Tamás Vicsek