I finished Melanie Mitchell's Complexity, a Guided Tour a few days ago, and it was pretty neat. Introduction For some years, complexity theory gains popularity in the realm of the social sciences, organization studies and management studies.
The scope of this book is broad, and covers a plethora of topics - evolution, computational complexity, turing machines and definite procedures, molecular genetics, immunology, neurology, graph and network theory, power laws, fractal geometry, information theory and thermodynamics, to give an incomplete list, all as seen through the lens of complexity theory.
This is an extremely useful book for anyone who wants to become familiar with basic concepts in the study of complexity.
I also found the passages which related complexity to genetics and biochemistry most intriguing as they planted the notions in the fertile soil of my background (wow, that is just as terrible to read as I thought it would be).
We learn about ant colonies, evolution, the immune system and the human brain. This cognition supports adaptation, and hence the term Complex Adaptive Systems is often used inclusive at the apex of human brain and global internet systems. effectively computable) is difficult, but she takes the reader with little math or computer science through it step-by-step in layperson's terms. Perhaps its beginning as an organized discipline could be dated to the founding of the Santa Fe Institute and its first conference on the economy as an evolving complex system in 1987. It was an interesting introduction to several new topics pertaining to complexity, such as the logistic map and cellular automata. Chapter seven deals with a number of proposed measures for the complexity of a given system, their strengths and limitations, and the problem of comparing the complexity of various systems. Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. Complexity deals with a large number of micro-variables that are networked and thus connected with varying degrees of strength. ants) seems in net to exhibit a certain level of environmental ecosystem cognition (i.e.
Expansive but not comprehensive. Most popular science books usually bore me to tears and end up being only pedantic for their historical backgrounds, but this one is very succinct with some interesting viewpoints (some of which I agree with and some of which my intuition says are terribly wrong) on the overall structure presented. I didn't wanna write any review, but this book was really perfect and persuade me to bring some paragraphs :I didn't wanna write any review, but this book was really perfect and persuade me to bring some paragraphs :It's funny the ideas you get about books before you read them.
I for some reason got the sense that this would be long and dense and impactful, and put off reading it for that reason. The knowledge here is also needed to make sense of its application particularly in chapters eight, nine and eighteen.
Chapter six on genetics provides a very short introduction to the structure and mechanics of DNA replication, and its translation into proteins. That would mean a false statement could be proved—arithmetic would be inconsistent.
As a young research scientist at the start of my career, I found the description very encouraging and insightful.One of the things I enjoyed most about Complexity was the author's description of how she entered the field and embarked upon research under the tutelage of Hofstadter. The knowledge here is needed to make sense of its application particularly in Part Two, "Life and Evolution in Computers" (chapters eight on self-reproducing computer programs and nine on genetic algorithms), and chapter eighteen "Evolution, Complexified". There are some worthwhile chapters giving examples on the emergence of complex adaptive behaviour from a large number of simple but connected componeMaybe I should not blame the messenger for delivering the news that measuring or even defining complexity is complex, and there are multiple conflicting ways to try and do that. Read online Book Review Complexity: A guided tour book pdf free download link book now. As a non-fiction book, this is very well written - it's on the level of an undergrad, with few actual formulas and very little "jargon" (most of it is hidden in the footnotes for interested readers). There are some worthwhile chapters giving examples on the emergence of complex adaptive behaviour from a large number of simple but connected components.
At best, they're distinct concepts like network theory, genetic algorithms, cellular automata, and chaos theory, all of which have clearly defined uses on their own. The origins of this new branch of science is in the realm of biologic ecosystems such as ant colonies in which a network of relatively simply programmed "building block" agents (i.e.
Second, the sciences of complexity as a recognized discipline are barely 25 years old (even though individual work on some of its aspects go back to the 1930s). Part Four, "Network Thinking", covers the basic definitions and metrics of network science, scaling phenomena, and the evolution of genetic networks, with particular focus on the new field of evolutionary development, and to Stuart Kauffman's work on random Boolean networks of "genes" where different levels of connectedness result in different macro behaviors of the network. A good survey book on complex systems. The scope of this book is broad, and covers a plethora of topics - evolution, computational complexity, turing machines and definite procedures, molecular genetics, immunology, neurology, graph and network theory, I actually brought this book by accident, thinking it was strictly about computation complexity theory.