Complex Networks & Their Applications V
Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016)

Source: link.springer.com

Interest in emergence amongst philosophers and scientists has grown in recent years, yet the concept continues to be viewed with skepticism by many. In this book, Paul Humphreys argues that many of the problems arise from a long philosophical tradition that is overly committed to synchronic reduction and has been overly focused on problems in philosophy of mind. He develops a novel account of diachronic ontological emergence called transformational emergence, shows that it is free of the problems raised against synchronic accounts, shows that there are plausible examples of transformational emergence within physics and chemistry, and argues that the central ideas fit into a well-established historical tradition of emergence that includes John Stuart Mill, G.E. Moore, and C.D. Broad. The book also provides a comprehensive assessment of current theories of emergence and so can be used as a way into what is by now a very large literature on the topic. It places theories of emergence within a plausible classification, provides criteria for emergence, and argues that there is no single unifying account of emergence. Reevaluations of related topics in metaphysics are provided, including fundamentality, physicalism, holism, methodological individualism, and multiple realizability, among others. The relations between scientific and philosophical conceptions of emergence are assessed, with examples such as self-organization, ferromagnetism, cellular automata, and nonlinear systems being discussed. Although the book is written for professional philosophers, simple and intuitively accessible examples are used to illustrate the new concepts.

https://global.oup.com/academic/product/emergence-9780190620325?q=Humphreys&lang=en&cc=us#

T. Bossomaier, L. Barnett, M. Harré, J.T. Lizier
“An Introduction to Transfer Entropy: Information Flow in Complex Systems“
Springer, 2016.

This book considers a relatively new measure in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas from statistics required to understand the text, the authors then present information theory and transfer entropy in depth. A key feature of the approach is the authors’ work to show the relationship between information flow and complexity. The later chapters demonstrate information transfer in canonical systems, and applications, for example in neuroscience and in finance.
 
The book will be of value to advanced undergraduate and graduate students and researchers in the areas of computer science, neuroscience, physics, and engineering.

 

SpringerLink access to PDFs: http://bit.ly/te-book-2016

Springer hard copy listing: http://bit.ly/te-book-2016-hardcopy

Amazon listing: http://amzn.to/2f5YdYW

Source: link.springer.com

If you’re wondering why every week seems to bring some new disruption to your world, why once-solid institutions seem shaky, author Darrell West has some explanations. At the heart of them is the idea of megachange – itself rooted mostly in economics. Such periods of rapid disruption are cyclical, argues West, director of governance studies and the Center for Technology Innovation at the Brookings Institution. He explored these ideas in his new book, entitled Megachange: Economic Disruption, Political Upheaval, and Social Strife in the 21st Century.

Source: knowledge.wharton.upenn.edu

Scariest of all is a scenario in which a computer figures out both the advantages of collusion and how to make it happen. Here, the situation might resemble what happened with AlphaGo, the computer program developed to play the board game Go. The program’s success was mostly due to machine learning. The computer played countless games against itself and figured out what worked best. The end result is a black box: We don’t really know how the computer is making decisions, only that it works. Because successful collusion leads to higher profits, it would make sense that computers—left to their own devices—would figure this out. Antitrust authorities would have no way to punish this type of collusion under existing laws.

 

Priceless
Barry Nalebuff
Virtual Competition The Promise and Perils of the Algorithm-Driven Economy Ariel Ezrachi and Maurice E. Stucke Harvard University Press, 2016. 364 pp.
Science  04 Nov 2016:
Vol. 354, Issue 6312, pp. 560
DOI: 10.1126/science.aaj2011

Source: science.sciencemag.org