This invaluable book is the first of its kind on “selforganizology”, the science of self-organization. It covers a wide range of topics, such as the theory, principle and methodology of selforganizology, agent-based modelling, intelligence basis, ant colony optimization, fish/particle swarm optimization, cellular automata, spatial diffusion models, evolutionary algorithms, self-adaptation and control systems, self-organizing neural networks, catastrophe theory and methods, and self-organization of biological communities, etc.
Readers will have an in-depth and comprehensive understanding of selforganizology, with detailed background information provided for those who wish to delve deeper into the subject and explore research literature.
This book is a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of computational science, artificial intelligence, applied mathematics, engineering science, social science and life sciences.
The Science of Self-Organization
By: WenJun Zhang
Between 2011 and 2014 the European Non-Equilibrium Social Science Project (NESS) investigated the place of equilibrium in the social sciences and policy. Orthodox economics is based on an equilibrium view of how the economy functions and does not offer a complete description of how the world operates. However, mainstream economics is not an empty box. Its fundamental insight, that people respond to incentives, may be the only universal law of behaviour in the social sciences. Only economics has used equilibrium as a primary driver of system behaviour, but economics has become much more empirical at the microlevel over the past two decades. This is due to two factors: advances in statistical theory enabling better estimates of policy consequences at the microlevel, and the rise of behavioural economics which looks at how people, firms and governments really do behave in practice. In this context, this chapter briefly reviews the contributions of this book across the social sciences and ends with a discussion of the research themes that act as a roadmap for further research. These include: realistic models of agent behaviour; multilevel systems; policy informatics; narratives and decision making under uncertainty; and validation of agent-based complex systems models.
‘Unless you have a brilliant hidden plan, I think you really screwed it up this time!’
It was unusual for Lex to blame iGod without any signs of holding back.
‘I am afraid I have not taken into account all possible linkages and feedbacks when I tried to optimize the financial system’; she answered in her dark brown raspy voice that maintained its usual calm and confidence. Unlike most other encounters, there was no trace of irony in her voice. ‘But it can be fixed. In fact, I have already started rescue operations – as you may have noticed. Soon, it is all under control again.’
iGod immediately projected a hologram. All of a sudden Lex’ small apartment was filled with the mass demonstration that had taken place earlier that day in Washington DC. Outraged people did no longer trust the financial system with the virtual money streams. They were holding banners demanding to get their old BitCoins back, shouting and throwing fireballs towards him. Lex’ instinctively moved aside, but the fireballs dissolved just before their images would reach him. The hologram of the furious crowd faded and next, iGod projected a video of the president of the United States delivering a speech before the United Nations on Lex’ wall on the left.
As it turns out, we are in the middle of a revolution – the digital revolution. This revolution isn’t just about technology: it will reinvent most business models and transform all economic sectors, but, it will also fundamentally change the organization of our society. The best way to imagine this transition may be the metamorphosis of a caterpillar into a butterfly. In a few years, the world will look very different…
The Golden Age: How to Build a Better Digital Society
See Also: Chapter 1: At the Edge
Big data, genomics, and quantitative approaches to network-based analysis are combining to advance the frontiers of medicine as never before. Network Medicine introduces this rapidly evolving field of medical research, which promises to revolutionize the diagnosis and treatment of human diseases. With contributions from leading experts that highlight the necessity of a team-based approach in network medicine, this definitive volume provides readers with a state-of-the-art synthesis of the progress being made and the challenges that remain.
Medical researchers have long sought to identify single molecular defects that cause diseases, with the goal of developing silver-bullet therapies to treat them. But this paradigm overlooks the inherent complexity of human diseases and has often led to treatments that are inadequate or fraught with adverse side effects. Rather than trying to force disease pathogenesis into a reductionist model, network medicine embraces the complexity of multiple influences on disease and relies on many different types of networks: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression in biological samples. The authors offer a systematic approach to understanding complex diseases while explaining network medicine’s unique features, including the application of modern genomics technologies, biostatistics and bioinformatics, and dynamic systems analysis of complex molecular networks in an integrative context.
By developing techniques and technologies that comprehensively assess genetic variation, cellular metabolism, and protein function, network medicine is opening up new vistas for uncovering causes and identifying cures of disease.