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2016 – Up now, an overall of 1567 citations among 74 works (including 3 books) on *GOOGLE SCHOLAR* (https://scholar.google.com/citations?user=gSyQ-g8AAAAJ&hl=en) [with an *Hirsh* h-index=19, and an average of 160.2 citations each for any work on my top five] + 900 citations among 57 works on the new *RESEARCH GATE* site (https://www.researchgate.net/profile/Vitorino_Ramos).

Refs.: * Science*,

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*Artificial Intelligence***,**

*Swarm Intelligence***,**

*Data-Mining***,**

*Big-Data***,**

*Evolutionary Computation***,**

*Complex Systems***,**

*Image Analysis***,**

*Pattern Recognition***.**

*Data Analysis*

*Complex adaptive systems* (*CAS*), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other CAS. Despite a wealth of data and descriptions concerning different CAS, there remain many unanswered questions about “steering” these systems. In *Signals and Boundaries*, ** John Holland** (

*Wikipedia*entry) argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering CAS through the mechanisms that generate their signal/boundary hierarchies.

*Holland*lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes.

*in, *Introduction to* John H. Holland*, “** Signals and Boundaries – Building blocks for Complex Adaptive Systems**“, Cambridge, Mass. : ©MIT Press, 2012.

Video -“*Sand, sand, pile of sand*” by Robert Proch (2009) – Impression without an idea, about trying to catch one (music by Pinkfreud).

*Complex Adaptive Systems* (CAS) are dynamic developing systems which arrange themselves according to external influences and to their own inner current state. If you are familiar with Conway’s “Game of Life“, you have an example of such a system. Complex adaptive systems arrange themselves around one or more critical factors (like in a sandpile, for example, where the pile will rearrange itself when you drop additional sand grains onto it). The theory behind these systems is related to chaos theory, but the systems are said to be “*on the edge of chaos*“, because they have the ability to adjust themselves (around the critical factors), unlike truly chaotic systems. One interesting thing with CA-systems is that they can suddenly rearrange themselves rather violently or criticality. Like in a sandpile which has a grain added to it, which topples onto other grains, which in turn topple onto other grains, etc. In physics, the* Bak-Tang-Wiesenfeld sandpile model* is the first discovered example of a dynamical system displaying self-organized criticality and is named after *Per Bak* [1] [2] [3], *Chao Tang* [1] [2] and *Kurt Wiesenfeld* [1] [2]. While running the model, you soon then have an avalanche effect and signatures produced equivalent to those found in nature. In fact, many phenomena in daily life are complex adaptive systems, like weather, traffic, earthquakes, eco-systems, or the stock market, and many of them share this precise 1/f noise pattern. As our brains.

[1] Per Bak, Chao Tang and Kurt Wiesenfeld (1987). “*Self-organized criticality: an explanation of 1/f noise*“. Physical Review Letters 59: pp. 381-384.

[2] Per Bak, Chao Tang and Kurt Wiesenfeld (1988). “*Self-organized criticality*“. Physical Review A 38: pp. 364-374.

[3] Per Bak (1996). *How Nature Works: The Science of Self-Organized Criticality*. New York: Copernicus. ISBN 0-387-94791-4.

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