This was a fun read complexity, plectics, and chaos theory. There is deep material, just a romp through ideas and people. It is meant for the general reader and it simply acquaints you with new concepts in science.
Here is a taste of the book:
Complexity scientists studying social reform often cite what is known as the hidden Markov model of change, a nod to nineteenth-century Russian mathematician Andrei Markov, who helped quantify the ways different systems -- physical, chemical, numerical -- evolve from one state to the next. Markov stressed that while some variables in any process or model are visible, others are always concealed, though the ones you can't see are inferable from the ones you can. This, of course, is a somewhat self-evident statement of how the world works. A freshly set table full of newly dirtied dishes tells you one has just been eaten. But the principle applies to other, more complex ways too.You can see how this is a breezy style that acquaints you with people and concepts but doesn't dawdle in order to instruct. You won't learn any science here, but you will develop an appreciation.
Markov-based processes, for example, are critical when you're learning a language or when software engineers are developing speech-recognition programs. Becoming adept at reading and speaking means hearing or seeing just a bit of a word or phrase and being able to draw inferences about what's around it. That's what allows you to scan a page of text quickly and understand whole thoughts from just a glance, or figure out the garbled speech on a bad recording in which many sounds and syllables are inaudible. Distill these probability-based hunches down to algorithms and teach them to a computer, and you have a mechanical brain that can hear and understand spoken speech -- even if it can't yet do it nearly as well as a human brain can. Markov modeling has similarly helped scientists working on such exceedingly complex projects as disease epidemics. And it can also explain social change.
University of Sienna economist Sam Bowles, who heads the behavioral sciences program at SFI, believes the death of South African apartheid offers the best possible example of how hidden markov processes drive politics.