The Engine of Complexity: Evolution as Computation
The concepts of evolution and complexity theory have become part of the intellectual ether permeating the life sciences, the social and behavioral sciences, and more recently, management science and economics. In this new title, John Mayfield elegantly synthesizes core concepts from across disciplines to offer a new approach to understanding how evolution works and how complex organisms, structures, organizations, and social orders can and do arise based on information theory and computational science.
This is a big picture book intended for the intellectually adventuresome. While not deeply technical or mathematical in style, the text challenges readers and rewards them with a nuanced understanding of evolution and complexity that offers consistent, durable, and coherent explanations for major aspects of our life experiences. Numerous examples throughout the book illustrate evolution and complexity formation in action and highlight the core function of computation lying at the heart of the book.
What people are saying - Write a review
We haven't found any reviews in the usual places.
11 Cultural Evolution
12 The Evolution of Complexity
13 Past and Present
14 The Future
Other editions - View all
action potentials activity adaptive adaptive immune system amino acids antibodies antigen aspect atoms autocatalytic set avalanche B-cells bacteria behaviors Bénard cells bind biology body brain called cells chain chapter characterized chemical complicated computer science concept configuration copying Coulomb’s law created cycle determined deterministic DNA molecules DNA sequences electronic encoded information energy engine of complexity entropy environment enzymes evolution evolutionary algorithms evolutionary computation evolve example formation function genes genetic genetic algorithm human improbable input instructions interactions ions large number laws learning logic mathematical means measure mechanism membrane messenger RNA million molecular mutation nature negentropy neurons nodes nucleotides objects occur organisms outcomes output parse trees particular patterns population possible predict principle probability problem produce programs proteins provides random change randomly reactions result scientific scientists selection Shannon simple rules sodium solution specific step strategy string structure theory things tiles tion understanding universe