Complexity by Mitchell M. Waldrop - Why did the stock market crash more than 500 points on a single Monday in 1987? Why do ancient species often remain stable in. In his book, “Complexity: the Emerging Science at the Edge of Order and Chaos”, author M. Mitchell Waldrop describes the objectives associated with the development and use of CAS concepts. Santa Fe members sought to pursue a common theoretical framework for complexity and a means of understanding the spontaneous, self-organizing.

1. Physics Today
2. University Of California Irvine

I cannot recall the last time I enjoyed a book of any kind this much. There is a stark difference between the way a book like this was written in 1990 and the way such books are written just 25 years later. The author, then, had two assumptions: His reader did not know very much, and his reader was very intelligent. Mitchell Waldrop, subsequently, explains with brevity and sophistication every idea. Today's equivalent, a catastrophe like The Upright Thinkers, assumes the opposite: Today's reade I cannot recall the last time I enjoyed a book of any kind this much. There is a stark difference between the way a book like this was written in 1990 and the way such books are written just 25 years later.

The author, then, had two assumptions: His reader did not know very much, and his reader was very intelligent. Mitchell Waldrop, subsequently, explains with brevity and sophistication every idea. Today's equivalent, a catastrophe like The Upright Thinkers, assumes the opposite: Today's reader knows everything, because of Google, and today's reader is a moron with a chipmunk's attention span. Therefore, stay in the first-person and make it glib! Anyway, here are some examples from the wonderful Complexity: It's essentially meaningless to talk about a complex adaptive system being in equilibrium: the system can never get there. It is always unfolding, always in transition.

In fact, if the system ever does reach equilibrium, it isn't just stable. And by the same token, there's no point in imagining that the agents in the system can ever 'optimize' their fitness, or their utility, or whatever.

The space of possibilities is too vast; they have no practical way of finding the optimum. The most they can do is change and improve themselves relative to what the other agents are doing. In short, complex adaptive systems are characterized by perpetual novelty.

147) and Holland, however, saw such top-down conflict resolution as precisely the wrong way to go. Is the world such a simple and predictable place that you always know the best rule in advance? And if the system has been told what to do in advance, then it's a fraud to call the thing artificial intelligence: the intelligence isn't in the program but in the programmer. No, Holland wanted control to be learned. He wanted to see it emerging from the bottom up, just as it did from the neural substrate of the brain.

185) and It's a lot like the difference between solids, where the atoms are locked into place, and fluids, where the atoms tumble over one another at random. But right in between the two extremes, he says, at a kind of abstract phase transition called 'the edge of chaos,' you also find complexity: a class of behaviors in which the components of the system never quite lock into place, yet never quite dissolve into turbulence, either. These are the systems that are both stable enough to store information, and yet evanescent enough to transmit it. These are the systems that can be organized to perform complex computations, to react to the world, to be spontaneous, adaptive, and alive. 293) This book is an intellectual experience that delights. What can I say about this book?

Complexity is one of my favorite topics - the world is made of individual agents reacting to limited local factors, and their interaction produces sophisticated emergent systems. And this book manages to make it seem like a boring administrative task.

Maybe I'm coming to this book too late. I've been seeing the world through complexity-colored glasses for years, so this book seemed plodding. No matter the discipline, Waldrop barely manages to capture What can I say about this book?

Complexity is one of my favorite topics - the world is made of individual agents reacting to limited local factors, and their interaction produces sophisticated emergent systems. And this book manages to make it seem like a boring administrative task.

Maybe I'm coming to this book too late. I've been seeing the world through complexity-colored glasses for years, so this book seemed plodding. No matter the discipline, Waldrop barely manages to capture the basics of complex systems. Maybe they were a difficult, new topic when this book was written in 1992. Maybe the ideas of the Santa Fe institute (WTF?

Who cares?) have already managed to permeate all of academia enough to make it seem stupid. But I doubt it. More likely, this guy was trying to make a story out of an understanding that most scientific disciplines already have. This book is not an exploration of ideas: it's a history of academia - of one institute that apparently lays claim to the idea of emergence, and the old White guys who think they're revolutionary by thinking about it. Ugh, a pet peeve - Waldrop seems to think that the order of complex systems is somehow antithetical to evolution. Where did you come up with this nonsense?

It's a huge revelation for him when someone comes close to reconciling them. The awesome thing about emergent systems is that the complexity comes about through evolution at the individual level! Have you even been listening? Search Google for emergence, emergent systems, complex adaptive systems, flocking or schooling behavior, evolutionary algorithms, cellular automata, neural networks, self-organizing systems, or PRETTY MUCH ANYTHING ELSE REALLY COOL, and you'll get a better idea of complexity than this book would give you.

One of those books that kicked a door open into a whole other realm of a science that I have been looking for my whole life. Emergence, Complex Adaptive Systems, Ecology, Chaos Theory, Simplicity, Neural Networks, Embryology, Cell growth, Evolution, Computer Programming, Immunology, Artificial Intelligence, Human Intelligence, Mathematics, Economies, Earthquakes, Power Laws, Statistics, Physics, Stocks, (and several other subjects I am leaving out due to not wanting to overdo the review) are tou One of those books that kicked a door open into a whole other realm of a science that I have been looking for my whole life.

Emergence, Complex Adaptive Systems, Ecology, Chaos Theory, Simplicity, Neural Networks, Embryology, Cell growth, Evolution, Computer Programming, Immunology, Artificial Intelligence, Human Intelligence, Mathematics, Economies, Earthquakes, Power Laws, Statistics, Physics, Stocks, (and several other subjects I am leaving out due to not wanting to overdo the review) are touched upon with great insight. What is most striking about this book is how well it is told and the great clarity in which it unfolds. Scientists are painted as fully fleshed out human beings and the science is perfectly translated to be understandable to the layman. For me this book is a life changer in the sense that I plan on studying this interesting science very thoroughly. By page 256 I knew I had to order my own copy. I know that this will be a book that I will continue to go back to as a reference. I feel that it plays an important role as the cornerstone for the building and expansion of my mental library as well as my physical library on the supremely interesting subject of Complexity.

This is the story of the creation of the Santa Fe Institute and the personalities and thinking of the scientists who came together to explore disciplines that just might relate to their own. So we have mathematicians, physicists, biologists, computer programmers and analysts, chemists, astronomers, and many more giving workshops and lectures to one another to explore what lies between order and chaos.

(A car key is simple. A car is complicated.

A car in traffic is complex.) This book is about co This is the story of the creation of the Santa Fe Institute and the personalities and thinking of the scientists who came together to explore disciplines that just might relate to their own. So we have mathematicians, physicists, biologists, computer programmers and analysts, chemists, astronomers, and many more giving workshops and lectures to one another to explore what lies between order and chaos. (A car key is simple. A car is complicated. A car in traffic is complex.) This book is about complexity. Author Waldrop tells stories and explores the personalities of the men and women who made the Santa Fe Institute happen, thereby creating a tale that enlightens the reader on many intellectual levels.

This book was written in 1992, but it is timeless. This is an old book, on my shelf, so finally skimmed it. It's really more a narrative of the people involved in creating the field of complexity economics. From that standpoint, it's a great story of how people interact and how ideas percolate. It's as relevant and true today as it was then. If you want to get a better understanding of what complexity economics is today, read some books by the people covered in this book, like Brian Arthur, or some of the work done at the Santa Fe Institute. I'm This is an old book, on my shelf, so finally skimmed it.

It's really more a narrative of the people involved in creating the field of complexity economics. From that standpoint, it's a great story of how people interact and how ideas percolate. It's as relevant and true today as it was then. If you want to get a better understanding of what complexity economics is today, read some books by the people covered in this book, like Brian Arthur, or some of the work done at the Santa Fe Institute. I'm more interested in how this might apply to Grand Challenge (Wicked problem) research. This book was published 24 years before the date of this review.

With a focus on complexity in nature (to include the cell, neural networks, natural selection and evolution, gene expression, and other topics), it is interesting to see how much we have progressed since then, and to see which 'new' names in the book from 1992 are sage old guard today. This is probably not a 'beach read' for most people. However, the author, himself an accomplished scientific writer (to distinguish him from those w This book was published 24 years before the date of this review. With a focus on complexity in nature (to include the cell, neural networks, natural selection and evolution, gene expression, and other topics), it is interesting to see how much we have progressed since then, and to see which 'new' names in the book from 1992 are sage old guard today.

This is probably not a 'beach read' for most people. However, the author, himself an accomplished scientific writer (to distinguish him from those who are journalist first, interested party second), does such an.excellent.

job of explaining in layman's terms some very complicated concepts and models, that it makes everything in the book both accessible and interesting. Those in the applied sciences will probably be familiar with the results, 24 years later, of the organizations and projects outlined in the book, but this might be interesting to them as well, for a 'how did we get here' brief historical look. For those with no formal training in chemistry, biology, physics, etc (like the reviewer), this is a wonderful introduction to the modern intersection of computing, neural networks, and genetic complexity can combine with something as seemingly disparate as the expansion of the universe. The book made this reviewer excited to see the results of professional collaboration, of lab work, and to reference projects from the 80s and 90s and understand their significance today. At the time the author was probably writing this book, a Macintosh Powerbook had a 16MHz processor and 2MB minimum RAM. The portable machine I type this on has a 3.00GHz processor and 8GB RAM, an increase of 187x and 4000x respectively. To know that they were limited for what we would consider almost trivial simulations by hard performance limits puts into perspective, for the hundredth time, just how much compute is available to the home user today, and the quite literally mind-boggling amount available to research institutions in today's supercomputers.

If you have a background or interest in evolution, a real convergence of computing and the natural world in the fields of biology or physics, or investigating complexity and chaos in the parenthetical list at the top of this review, then pick up the book. This was a library borrow. I will be purchasing a copy for home. If you want to look smart, carry this book with you onto a plane or into a park and start reading because wow that title is a mouth full! I forget how I discovered this book. It was mentioned in another book (I believe Flash Boys by Michael Lewis), and I decided to request it from my library, and I am very glad that I did.

This book covers a wide range of topics and how they intersect. Everything from Machine Learning to Artificial Life to Economic Theory. It is a story how the leaders of these f If you want to look smart, carry this book with you onto a plane or into a park and start reading because wow that title is a mouth full!

I forget how I discovered this book. It was mentioned in another book (I believe Flash Boys by Michael Lewis), and I decided to request it from my library, and I am very glad that I did. This book covers a wide range of topics and how they intersect. Everything from Machine Learning to Artificial Life to Economic Theory. It is a story how the leaders of these fields came together in a very unexpected way to bring about a new way of science in their fields. It is a story about the importance of interdisciplinary studies and the advantages that diversity in thinking can bring to a project.

And it is also a fascinating story about several very smart people and how they came to be doing and loving the work that they do. I recommend this book if you like books such as those written by Michael Lewis. Waldrop does a great job of breaking down some very advanced scientific and mathematical material into bite-size chunks that the average reader can comprehend. That being said, do not read this book if you do not enjoy science, mathematics, and computing and do not have a somewhat advanced understanding of the fields.

I do and in a very nerdy way found this book to be quite the page-turner. In conclusion, do not let the title intimidate you. This is more of a story about people than about Complexity and Order and Chaos. Go into the book with an open mind expecting to learn something new through learning about people.

The only reason I have given 4 and not 5 starts is because the book did tend to drag on a bit towards the end. The science of complexity is presented here as an emerging discipline, not to say the discipline of emergence - oh, and adaptive behavior, nonlinear dynamics, and the unseen forces that drive ecosystems to the edge of chaos (which is not as bad as it sounds). Waldrop covers the development of the new paradigm by interconnecting professional biographies of the leading theorists with the establishment of their interdisciplinary Santa Fe Institute, a place devoted to the exploration of complexity. The science of complexity is presented here as an emerging discipline, not to say the discipline of emergence - oh, and adaptive behavior, nonlinear dynamics, and the unseen forces that drive ecosystems to the edge of chaos (which is not as bad as it sounds). Waldrop covers the development of the new paradigm by interconnecting professional biographies of the leading theorists with the establishment of their interdisciplinary Santa Fe Institute, a place devoted to the exploration of complexity.

It's a solid conceit, since the conjunction of hero-philosophers, thought experiments, and an organization-protagonist always leaves the reader someone or something to puzzle out or root for. The 'emergence' in complexity can be defined as repeated interactions of a few very simple rules or elements in a way that leads to an unpredictable outcome. One example might be Darwinian: mutations in the context of reproductive self-interest and competition leading to adaptation and survival. Another might be games like chess or Go, in which the further out you get from the opening position, the more difficult it becomes to reverse engineer the processes that led to the current state.

Physics Today

More like Go than chess is John Conway's cellular automata, a fun model comprised of only four rules which fully govern the retention, removal, or placement of checkers onto an infinite grid. Depending on your starting setup, successive rounds run as a movie will showcase a multitude of (emerging) configurations, ranging from regular blocks to gliders that fly across the game board to other objects which themselves generate other animate, stable figures. The outcome of Conway's game depend heavily on initial conditions. A population of cells that is either too densely packed or too spread out will rapidly achieve the stasis of an unchanging board.

Conversely, spacing of cells in such a way that produces equal numbers of 'births' and 'deaths' will yield an incessantly boiling soup. Somewhere on the border of a frozen, cold order and noisy, hot chaos resides a disequilibrium that mimics life. This Goldilocks zone is the home of unpredictable, but recognizable change, a place where individual figures like gliders, spaceships, blinkers, traffic lights, and other, far more complicated animations can emerge. Nor are cellular automata the only complex constructs with Goldilocks zones. Fluid dynamics appear governed by similar principles, whether occurring in the churning vortices of planetary and stellar atmospheres or the incessant drip of a leaky faucet. Let infinite grains of sand pour onto a table and they will gradually raise a pile, the sides of which cascade at random times and in random quantities (albeit describable in the gestalt by a power law). Similarly understandable yet unpredictable is the behavior of earthquakes, the result of constant fluxions in neighboring tectonic plates.

You'll find like transitional volatility in chemistry where solids become liquids, gases, and plasmas; in biology where cells become organs become systems become competing organisms; in economics where tech startups become monopolies and stocks boom or bust (to hell with neoclassicists' nonsensical, if mathematically precise, equilibria); and in pretty much any system with individual elements whose behavior and environment are interdependent. All this constitutes the 'complexity' that we experience day to day and which the Santa Fe Institute's fellows and residents study. As suggest, computer modeling is an essential tool for the study of complexity.

Befitting a field that emerged alongside the evolution of computing power, Waldrop offers a number of amusing anecdotes. So complexity pioneer John Holland is described at page 190 graduating from programming a 1960's Whirlwind to the IBM 701 to a Micromind to a Commodore 64 ('quite a step up' because 'the Commodore would let me play games') to - look out! - the mighty Apple II. As the author explains, 'True, it wasn't much more than a bunch of circuit boards in a black box that could be hooked up to a teletype machine for input and output. It had no screen. But it did have 8 kilobytes of 8-bit memory.

And it only cost $3000.' Waldrop chronicles a great deal of scientific ferment, but the end result so far appears to more in the realm of building a precise language to allow classification and ultimately recognition of unpredictable, unstable systems, to make them easier to study if not to be tamed.

That said, both the science and Santa Fe fellows have matured significantly in the last 25 years (my edition of this book was updated in 2008). In the early days of the institute, there were a lot of evocative computer models that were too hopelessly vague to apply to anything practical. And a great deal of abstract hand-waving. According to one joke from the time (that was not quite a joke), if somebody came in and said, 'I had oatmeal for breakfast,' everyone would say, 'Oooooo! What's the theory of oatmeal?'

366) Fundamentally, complexity is simple: big, powerful effects can follow from lots of little connections. The whole is greater than the sum of its parts. The devil arises from the interactions of the details, if not the details themselves. Easier said than understood, and here Waldrop says it repeatedly. I wonder if the author found it as difficult to conclude his book as to reach a conclusion. It's not a killer, though; the various complex questions under study and analogies offered to explain complexity as a field probably benefit from the various retellings. On finishing, my oatmeal theory is that Complexity shares a lot in common with hot porridge.

It's hearty, lumpy, fast-filling and fatiguing, yet ultimately satisfying. In case you're wondering, I'm summarizing my impressions of the book here. As Waldrop makes clear, it's still too early to speak for the science. This is a very good introduction to Complexity Theory. Which is a new field of science which is very very important. The book goes into the people, the theory and experiments of complexity theory.

The Sante Fe Institute is where complexity theory was worked on and major breakthroughs made in understanding it through simulations. In its gist, its the study of complex adaptive systems where individual agents interact with each other to self organize spontaneously to create incredibly complex syst This is a very good introduction to Complexity Theory. Which is a new field of science which is very very important. The book goes into the people, the theory and experiments of complexity theory. The Sante Fe Institute is where complexity theory was worked on and major breakthroughs made in understanding it through simulations.

In its gist, its the study of complex adaptive systems where individual agents interact with each other to self organize spontaneously to create incredibly complex system. The forces of adaption also play a role. The field tries to answer such questions like 'how did this incredibly rich, diverse, and complex world come about' and 'Why do we have life, and how did we come about'.

Essentially, what the field is getting at is that there are properties in how the world works that actually result in galaxies, life, economies etc. There is something like a mathematical propensity for self organization and the emergence of complex structures. This leads to the relative order we see. A big part of this is emergence. You can't predict the behavior of a system from individual components. The system acquires new properties at each level of complexity. I.e there's no way you could have foretold the richness of human culture by looking at a bunch of tissue.

Complex systems are more than the sum of their parts. This is not an easy read unless you have a strong background in computer science or biology or physics.

I don't, so there were parts of the book I had to read a few times over to grasp it. I strongly recommend this book even if it may be difficult at times.

This book delves deep into how our world works. If you've read Anti-Fragile. This book is kind of the foundational knowledge behind that book. Reading this has helped me gain a better understanding of the ideas of change, robustness, and anti-fragility. In terms of practical take-aways, there are some major takeaways you can take- 1) Change is a constant part of complex systems. There's a small zone between the boundary of chaos and order (i.e complete chaos and dead stability) where complex systems emerge. This is the world we live in.

There's enough order that systems and complexity can emerge, but at the same time things are in flux, constantly evolving and changing. Basically what this means is that change is a constant part of our world, and internalizing that is key to live well considering we live in a complex world.

Never assume things will remain the same and learn to constantly expect it. 2) You need an interdisciplinary approach to solving anything but the simplest problems. The world is extremely interconnected. Solving major problems involves understanding the many factors involved. I.e if you want to solve issues such as global warming, you have to look at all aspects of it, from psychology, business, politics, the science of global warming itself, the stakeholders involved, etc. Hence the importance of an interdisciplinary approach to life.

The same applies to business. 3) We are part of complex ecosystems.

An example the book gave is that star employees that are poached away often perform worse in their new jobs. That's because the ecosystem is completely different. And the ecosystem in their first job is what allowed them to do well. Basically, this means that you shouldn't assume you're doing well or badly just because you're a hard worker, smart, or you suck. There could be many other factors involved. Hence, deeply analyze the systems you're in to see how they affect your performance, and then put yourself in systems and situations that are ideal for success.

University Of California Irvine

This is an odd book. It explains complex ideas in the form of a narrative. Most of the book surrounds the founding and the characters embedded in the Santa Fe Institute drama, but all of that is by way of organization and presentation in what is basically a disorganized jumble of information. Really, this is almost like a fiction book for the presentation of ideas. Waldrop however only presents the ideas, he doesn't really critique them. Nor does the people in the book seem to do that, since the This is an odd book.

It explains complex ideas in the form of a narrative. Most of the book surrounds the founding and the characters embedded in the Santa Fe Institute drama, but all of that is by way of organization and presentation in what is basically a disorganized jumble of information. Really, this is almost like a fiction book for the presentation of ideas. Waldrop however only presents the ideas, he doesn't really critique them.

Nor does the people in the book seem to do that, since they operate mostly on faith. I am surprised by how dry but also how precise Waldrop is.

He really did some work to put this together. All in all this book follows the ideology of super-science by taking these conjectures on mathematical obscurity and speculative faith. Still, it's an interesting that this science would be 'emerging' much like the models it suggests, a combination of chaos and order.

The synopsis is really misleading though since it only promises answers, much like this post-empirical science, but it can't deliver them. Not yet, and maybe not ever. My impression upon completion is that we've taken lots of information and tossed many words around, but we still aren't sure what any of it means. It's ironic that the Santa Fe Institute was started to stop reduction and yet, we are practicing it here, just on a wider domain.

There is very little critique of the tools are being used even while the effects of the general scientific practices are themselves questioned. My point is the questioning is very incomplete as is the subject matter of this book. Not the fault of the author, but I suspect that this book is put out as part of the propaganda of the institute to try to garnish more attention to themselves and what they are doing. Caveat: I bought this book in a used bookstore by accident. I thought it was a book by Melanie Mitchell, who also has a book entitled Complexity.

On the positive side, this book presents an interesting picture of the founding of the Sante Fe Institute. Although I am moderately familiar with the complexity field, I did learn a few things by the clever placement of emphasis on certain points, though the presentation remained non-technical. I also thought the book did a good job at presenting perso Caveat: I bought this book in a used bookstore by accident. I thought it was a book by Melanie Mitchell, who also has a book entitled Complexity.

On the positive side, this book presents an interesting picture of the founding of the Sante Fe Institute. Although I am moderately familiar with the complexity field, I did learn a few things by the clever placement of emphasis on certain points, though the presentation remained non-technical. I also thought the book did a good job at presenting personal conflicts and difficulties in a way that took nothing away from the subjects personally, which I thought was deftly handled. On the negative side, which is why the book I gave the book three stars, is hinted at in some of the other reviews: some called it slow, and other called it fawning. I think the problem is that the description is kind of breathlessly enthusiastic, proceeding to describe the subjects continually with high energy. Over a sustained period, this can make even a short amount of text seem very long, and an ordinary description sound unduly favorable.

For this reason, I found that I got the most out of the text by reading it for short periods, one anecdote or description at a time. By adding the pace the book itself lacks, I think the average reader might find it quite enjoyable. Overall, I'm glad that the Santa Fe story was told in such an accessible and (in small bites) inspiring way. This book is from 1992, but it came along at the perfect time for me. It's basically the contemporaneous scientific history of one of my favorite topics: emergence. Written as a narrative, it describes the academic community self-organizing in the 1980's to establish an interdisciplinary branch of science devoted to dynamic systems, with the Santa Fe Institute as its catalyst. A dominant property of these systems is a small set of simple rules leading to highly complex, completely unknowable outp This book is from 1992, but it came along at the perfect time for me.

It's basically the contemporaneous scientific history of one of my favorite topics: emergence. Written as a narrative, it describes the academic community self-organizing in the 1980's to establish an interdisciplinary branch of science devoted to dynamic systems, with the Santa Fe Institute as its catalyst. A dominant property of these systems is a small set of simple rules leading to highly complex, completely unknowable outputs and behavior. The reader is given a careful and thorough description of the various theories and hypotheses in play at the time, in a way that is technical but understandable without knowing advanced mathematics. As the scientists modify and enhance their concepts over time, we walk those same paths with them and we're able to follow the evolution of their thought thanks to the solid background preparation. We are given first-person accounts by quotes from the actual characters in the story, complete with an insider's view of academia and the institutional forces that create an inherent barrier to interdisciplinary collaboration. If you're interested in fractals, swarms, and emergent behavior, this book is a compelling read.

Plus it devotes several pages to Craig Reynolds's 'Boids'! A biographical account of the founding of the Santa Fe Institute that introduces the basic scientific concepts researched at the institute in its early days. This book really resonated with me. I've been running into the phrase 'complexity' in other books and articles I've been reading so it was nice to finally read something on Complexity Science. I will have to re-read it because a bunch of the science went over my head but all in all it was a fairly accessible volume. I have a visual arts back A biographical account of the founding of the Santa Fe Institute that introduces the basic scientific concepts researched at the institute in its early days.

This book really resonated with me. I've been running into the phrase 'complexity' in other books and articles I've been reading so it was nice to finally read something on Complexity Science. I will have to re-read it because a bunch of the science went over my head but all in all it was a fairly accessible volume. I have a visual arts background and was drawn to the arts because it seemed like a good way to receive an interdisciplinary education during the specialty-program orientated late nineties.

It sort of worked but most of my formal education was limited to the humanities, with a few introductory courses in the soft sciences thrown in for good measure. The sciences have always interested me but even in high school they were so stuffy and hyper specialized that it turned me off.

Now I've got a whole list of science related things I want to go learn about and I'm not as intimidated by the idea because this book talks about a number of people who started their studies in things like Philosophy and Anthropology and then moved into Mathematics and Computer Science. Robert Sapolsky promises (in Stanford's behavioral biology lectures, available on youtube) that will lead subsets of its readers to either spiritual awakening, intense annoyance. Or mild confusion.

I feel like this book pairs well with Chaos. If you're hooked then these ideas will keep you up at night, but in a good way. Intellectually thrilling ideas wrapped in the history of the emerging field of complexity, artificial life, and emergence. It is thrilling to read abou Robert Sapolsky promises (in Stanford's behavioral biology lectures, available on youtube) that will lead subsets of its readers to either spiritual awakening, intense annoyance.

Or mild confusion. I feel like this book pairs well with Chaos. If you're hooked then these ideas will keep you up at night, but in a good way. Intellectually thrilling ideas wrapped in the history of the emerging field of complexity, artificial life, and emergence. It is thrilling to read about scientists in different fields having these converging ideas so different than the standard theory meet and found this discipline. If anything in the description of this books sounds interesting, then I recommend this book wholeheartedly. This book served as my introduction to complex adaptive systems.

I read it in 1994 and have been fascinated by the subject ever since. It's well written and engaging, and follows the established popular science formula of threading technical explanations of topics in science and math into a series of stories about the people who are developing and expanding upon those topics. It seemed a little disjointed at times, but the scientific explanations were generally clear and quite good. In fact, Wal This book served as my introduction to complex adaptive systems. I read it in 1994 and have been fascinated by the subject ever since. It's well written and engaging, and follows the established popular science formula of threading technical explanations of topics in science and math into a series of stories about the people who are developing and expanding upon those topics.

It seemed a little disjointed at times, but the scientific explanations were generally clear and quite good. In fact, Waldrop's description of genetic algorithms was good enough that I was able to code-up a simple GA and solve a few pretty interesting optimization problems without having to reference any textbook on the subject at all. That was also my introduction to genetic algorithms, which is another subject that I find fascinating. ★ - Most books with this rating I never finish and so don't make this list. This one I probably started speed-reading to get it over with. ★★ - Average.

Wasn't terrible, but not a lot to recommend it. Probably skimmed parts of it. ★★★ - Decent. A few good ideas, well-written passages, interesting characters, or the like.

This one had parts that inspired me, impressed me, made me laugh out loud, made me think - it got positive reactions and most of the rest of it was pretty decent too. ★ ★ - Most books with this rating I never finish and so don't make this list. This one I probably started speed-reading to get it over with. ★★ - Average. Wasn't terrible, but not a lot to recommend it. Probably skimmed parts of it.

★★★ - Decent. A few good ideas, well-written passages, interesting characters, or the like. This one had parts that inspired me, impressed me, made me laugh out loud, made me think - it got positive reactions and most of the rest of it was pretty decent too. ★★★★★ - Amazing. This is the best I've read of its genre, the ones I hold on to so I can re-read them and/or loan them out to people looking for a great book. The best of these change the way I look at the world and operate within it. This is a journalistic account of the beginnings of the Santa Fe Institute, and the incredible thinkers who find patterns in everything from biology to economics.

I can't describe how thrilling it was to finally have these half-baked theories I've toyed with for most of my adult life validated by far more intelligent people who have actually done work building models and who can articulate so much more clearly. I found myself reading this with my heart beating out of my chest. God it almost made This is a journalistic account of the beginnings of the Santa Fe Institute, and the incredible thinkers who find patterns in everything from biology to economics.

I can't describe how thrilling it was to finally have these half-baked theories I've toyed with for most of my adult life validated by far more intelligent people who have actually done work building models and who can articulate so much more clearly. I found myself reading this with my heart beating out of my chest.

God it almost made me want to go back to science. “If you have a truly complex system,' he says, 'then the exact patterns are not repeatable. And yet there are themes that are recognizable. In history, for example, you can talk about 'revolutions,' even though one revolution might be quite different from another.

So we assign metaphors. It turns out that an awful lot of policy-making has to do with finding the appropriate metaphor.

Conversely, bad policy-making almost always involves finding inappropriate metaphors. For example, it may not be appropriate to think about a drug 'war,' with guns and assaults.” —.