As a rule, when a distinguished scientist says he's come up with a fourth law of thermodynamics, he's wrong. In Investigations, Stuart Kauffman may be the exception.
The three laws of thermodynamics have been summarised as: "you can't win", "you can't break even", and "you can't get out of the game". Kauffman's candidate for a fourth law is: "but the game keeps getting more complicated, and there are always different ways to play."
One of Kauffman's key concepts is that of the adjacent possible. Imagine a set of things that exists in a particular system (such as a group of reacting chemicals, or an ecological community, or the kinds of toys available in a capitalist economy). The adjacent possible is the set of things that are only one step away from actual existence. Like potential energy in physics, the adjacent possible is a metaphysical idea with real utility.
You can think of "normal science" (as described by Thomas Kuhn in The Structure of Scientific Revolutions) as proceeding step by step into the adjacent possible. Most self-styled revolutionary scientific treatises are really crackpottery. They don't stop in the adjacent possible; they go wandering across the landscape and over the speculative horizon. Investigations may be the real thing. Kauffman is pushing into the adjacent possible at many points, from biology, chemistry, thermodynamics, and economics. As he says, "whatever Investigations is--useful, as I hope, or foolish--it is not normal science." --Mary Ellen Curtin
Synopsis
"It may be that I have stumbled upon an adequate description of life itself." These modest yet profound words trumpet an imminent paradigm shift in scientific, economic, and technological thinking. In the tradition of Schrodinger's classic "What Is Life?", Kauffman's "Investigations" is a tour-de-force exploration of the very essence of life itself, with conclusions that radically undermine the scientific approaches on which modern science rests--the approaches of Newton, Boltzman, Bohr,

Universality
Mark Ward

Synopsis
We are surrounded by order that physics cannot explain. The spread of veins in the back of your hand mirror the spread of branches on a tree; fern leaves look a lot like maps of fjords; and the pulse patterns of your heartbeat resemble some classical music. But the theory of universality uses fractal patterns to explain much of the world around us. Universality argues that there are similar patterns behind the most unpredictable events, such as earthquakes, avalanches, stock market crashes, and even the way businesses are run and the way fashions come and go. While identifying patterns does not mean we can always predict what will happen next, some of the trends scientists are noticing could deepen our understanding of natural phenomena and our relationship to them.

The Structure of Scientific Revolutions
Thomas S. Kuhn

There's a comic strip showing a chick breaking out of its shell, looking around, and saying, "Oh, wow! Paradigm shift!" Blame the late Thomas Kuhn. Few indeed are the philosophers or historians influential enough to make it into the funny papers, but Kuhn is one.
The Structure of Scientific Revolutions is indeed a paradigmatic work in the history of science. Kuhn's use of terms such as "paradigm shift" and "normal science", his ideas of how scientists move from disdain through doubt to acceptance of a new theory, his stress on social and psychological factors in science--all have had profound effects on historians, scientists, philosophers, critics, writers, business gurus, and even the cartoonist in the street.
Some scientists (such as Steven Weinberg and Ernst Mayr) are profoundly irritated by Kuhn, especially by the doubts he casts--or the way his work has been used to cast doubt--on the idea of scientific progress. Yet it has been said that the acceptance of plate tectonics in the 1960s, for instance, was sped by geologists' reluctance to be on the downside of a paradigm shift. Even Weinberg has said that "structure has had a wider influence than any other book on the history of science". As one of Kuhn's obituaries noted, "We all live in a post-Kuhnian age." --Mary Ellen Curtin
Synopsis
Thomas S. Kuhn's work explaining the process of scientific discovery. This text is the third edition and incorporates a new index

Synopsis
How do people, animals, and institutions make decisions in a complex and uncertain world? Rational choice theory answers this question from the perspective of an omniscient and omnipotent superintelligence that decides by optimizing. In contrast, this book promotes the concept of the "adaptive toolbox," a repertoire of fast and frugal heuristics for real people with limited time, knowledge, and resources. It views bounded rationality neither as optimality under constraints nor as the study of people's reasoning fallacies. The strategies in the adaptive toolbox dispense with optimization and, for the most part, with calculations of probabilities and utilities. The book extends the concept of bounded rationality from cognitive tools to emotions; it analyzes social norms, imitation, and other cultural tools as rational strategies; and it shows how smart strategies can exploit the structures of environments. It brings together experts from cognitive science, economics, evolutionary biology, and anthropology to create an interdisciplinary basis for understanding the adaptive toolbox.

At Home in the Universe
Stuart Kauffman

Synopsis
Complexity theory is one of the most controversial areas of current scientific research. Developing out of chaos theory, complexity suggests that there are hidden tendencies in nature to select ordered states, even when statistically they are vastly outnumbered by chaotic possibilities: that there is a deep natural impulse towards order, counteracting the degenerative tendencies of the Second Law of Thermodynamics. Like chaos, complexity is a multidisciplinary area of research and those involved include physicists, economists and biologists. This is a study of complexity
Enthusiastic complexity theory, 29 October, 2000
Reviewer: A reader from La Palma, Canarias, Spain
An eye-opener on how closely certain mathematical models can reproduce, or mimic, real life behaviour. Kauffman describes and discusses the complex behaviour exhibited by autocatalytic sets - webs of interacting chemicals and catalysts (real and simulated), individually with simple behaviour and rules of interaction, but en masse producing complex systems with non-trivial reactions to environment and other systems.
This leads naturally into a discussion of evolution where we are treated to a more refined, but perhaps less real-world, discussion of the mechanics of evolution than that provided by more popular authors (e.g. Dawkins). Kauffman describes evolution not only as a process of natural selection, but also as the interaction of complex systems with their environments, discussing how single systems or entire species may move around and interactively modify fitness landscapes to acquire the highest peaks. These necessarily general models are convincingly tied to specific, real-world examples, and the result is a clear impression of a fast developing field with relevance to real life, the extent of that relevance remaining to be seen.
Unsurprisingly, the book ends up somewhat speculative, but unfortunately chooses to direct this speculation at economics. The writing occasionally becomes somehwat "gee gosh darn". And while I'm on petty complaints, I found the occasional stabs at human interest to be distracting and unnecessary, but that's a common problem with popular science writing.
Finally, I don't think this is the kind of book to change lives. Interesting, certainly, occasionally surprising, and full of fairly new ideas, but I found that Kauffman repeatedly stopped short of saying anything really profound. Yes, "we the expected" is a fascinating concept so why _end_ the chapter with it? Likewise, the "invisible hand" is a leading analogy, then so what...? Fundamentally, I think the book sits firmly on the fence when it comes to religion, or lack thereof, other reviewers notwithstanding

Synopsis
The psychologist William James observed that "a native talent for perceiving analogies is...the leading fact in genius of every order". The centrality and the ubiquity of analogy in creative thought have been noted again and again by scientists, artists and writers, and understanding and modelling analogical thought have emerged as two of the most important challenges for cognitive science. "Analogy Making as Perception" is based on the premise that analogy-making is fundamentally a high-level perceptual process in which the interaction of perception and concepts gives rise to "conceptual slippages" which allow analogies to be made. It describes Copycat - a computer model of analogy-making, developed by the author with Douglas Hofstadter, that models the complex, subconscious interaction between perception and concepts that underlies the creation of analogies. In Copycat, both concepts and high-level perception are emergent phenomena, arising from large numbers of low-level, parallel, non-deterministic activities. In the spectrum of cognitive modeling approachers, Copycat occupies a unique intermediate position between symbolic systems and connectionist systems - a position that is at present the most useful one for understanding the fluidity of concepts and high-level perception. On one level the work described here is about analogy making, but on another level it is about cognition in general. It explores such issues as the nature of concepts and perception and the emergence of highly flexible concepts from a lower-level "subcognitive" substrate.

Linked: The New Science of Networks
Albert-László Barabási

Synopsis
This work explores the new science of networks and their impact on nature, business, medicine, and everyday life. In the 1980s, James Gleick's "Chaos" introduced the world to complexity. Now, Albert-Laszlo Barabsi's "Linked" reveals the next major scientific leap: the study of networks. We've long suspected that we live in a small world, where everything is connected to everything else. Indeed, networks are pervasive - from the human brain to the Internet to the economy to our group of friends. These linkages, it turns out, aren't random. All networks, to the great surprise of scientists, have an underlying order and follow simple laws. Understanding the structure and behaviour of these networks will help us do some amazing things, from designing the optimal organization of a firm to stopping a disease outbreak before it spreads catastrophically. In this work, Barabasi traces the development of this rapidly unfolding science and introduces us to the scientists carrying out this pioneering work. These "new cartographers" are mapping networks in a wide range of scientific disciplines, proving that social networks, corporations, and cells are more similar than they are different, and providing important new insights into the interconnected world around us. This title provides a preview of the next century in science, which should be transformed by these amazing discoveries.

Great explanatory power!, 3 July, 2002
Reviewer: coert.visser@wxs.nl from Driebergen Netherlands
Nowadays, everybody talks about networks. Yet, what networks really are and how they function, often remains rather vague in conversations. This book offers great insight into the evolution, the structure and the relevance of networks. The author, Albert Barabási, himself a creative and important
contributor to network science, makes the rapid and fascinating advances made in this field comprehensible.
Our world is filled with complex networks, webs of highly connected nodes. Not all nodes are equal, however. In fact, in many real-world complex networks, there is a typical hierarchy of nodes (called a POWERLAW DISTRIBUTION). This means there are a few extremely well connected nodes (these are called HUBS), there are quite a few moderately connected nodes and there are large numbers of tiny nodes (having very few connections to
other nodes). The Internet, for instance, has only several hubs -like amazon.com and Yahoo - and countless tiny nodes -like my own website :-(.
The structure of networks with a powerlaw distribution is called a SCALEFREE TOPOLOGY. Such a scale free topology is found in networks that 1)are GROWING (extra nodes and links emerge), and 2) are characterised by PREFERENTIAL ATTACHMENT (this means that some links are far more likely to get linked than others). Preferential attachment, is driven by two factors: 1) the number of links the node already has (this is in fact the first mover advantage: a nodes that has been there since the early evelopment of the network gets the biggest chance to get connected), and 2) the node's fitness (for instance a new website offering a truely unique service has an excellent chance to get many links).
A fascinating characteristic of scale free networks is the following. The density of the interconnectivity paradoxically creates two properties at the same time: 1) ROBUSTNESS (removing nodes will not easily lead to the breakdown of the network, precisely because of the fact that all nodes are connected. Only simultaneous removal of the largest hubs will break down the network), and 2) VULNERABILITY TO ATTACK (because of the fact that all nodes are indirectely connected to each other failures, like viruses, can very easily spread through the whole network. This fenomenon is called 'cascading failures'.

Reading this book made me realise that the recently acquired knowledge about networks is revolutionizing many fields of science, like biology, medical science and economics. Also, the practical applications will be numerous, like protecting the internet, fighting terrorist networks, finding a cure for cancer (!), and developing new organizational forms.

A brilliant overview of a fascinating new area of science, 25 June, 2002
Reviewer: Dr D Evans from Gloucestershire, United Kingdom
This is one of the clearest, most original and most exciting popular-science books I have ever read. It manages to get across the main points of network theory with a minimum of technical jargon, and yet without oversimplification.
Many natural and artificial systems can profitably be viewed as networks in which a number of nodes are connected by links. For many years, the only networks that mathematicians studied were so-called 'random graphs' in which all nodes had more or less the same number of links. But in the late 1990s, when Albert Barabasi, a physicist at the University of Nortre Dame, began to study real networks such as the World Wide Web, he realised that they are rarely structured like random graphs. In most real networks, it turns out, the connectivity distribution decays as a power law - which means that there is no such thing as a 'typical node'. Instead, there are a few highly-connected nodes and many sparsely connected nodes.
Since then, Barabasi and his research team at Notre Dame have found many more examples of networks with this kind of structure, from the metabolic network of protein-protein interactions inside cells, to the social ties that link CEOs in the 'old-boy network'. Despite being composed of very different kinds of element, all these systems share certain interesting properties simply because they have similar structures. In other words, you can discover certain things about a network simply by looking at its connectivity.
All this is fascinating in its own right, but it's even better to get the message 'from the horse's mouth', rather than from a journalist. I've followed the author's papers in Nature with great interest over the past few years, but it was nice to have an overview of the whole field of network theory that stands back and presents the general context as well as the specific details.

Terrific non-traditional research, 19 August, 2002
Reviewer: stephenmuires from Denmark
'A new kind of science' came out in May 2002 and seems to slowly generate all manner of responses, many of them unsure and contradictory. There don't seem to be many top scientific responses (I may be wrong), they probably can't take the book very seriously. Of course it's not written as a technical paper, but is instead very pleasantly readable. Personally I think this is the most important feature of the book: my standpoint is that if you want to get a message through to me write it in a way that I can understand without me needing 5 university degrees, otherwise don't bother.
So what about the message itself? This is hardly the place for it, even if I could explain it any better than Wolfram.
There is one point, though, that the book gratifyingly confirms for me. I have become stuck looking at Artificial Intelligence: the simplest piece of functionality seems to require very complex mathematical models and even then the results are primitive and disappointing. Now I KNOW that I do not have advanced mathematical analyses going on in my head when recognizing a blue square and noticing it is different from a red triangle. So what's the point simulating this process in software using highly complex models? This cannot be the way to go. And Wolfram seems to have found the reasons that confirm this intuition. Very simple initial conditions and very simple transformation rules can produce unlimited complexity. You do not need complexity to produce complexity (or intelligence). For me this is a useful insight, prodding the search for AI that simplifies the process of intelligence, not complicates it even further.
This book is clearly worth study. Just the fact that it exists and that someone has done this non-traditional research is terrific.