Books about Complexity from Amazon.com

Bestselling author Nassim Nicholas Taleb continues his exploration of randomness in his fascinating new book, The Black Swan, in which he examines the influence of highly improbable and unpredictable events that have massive impact. Engaging and enlightening, The Black Swan is a book that may change the way you think about the world, a book that Chris Anderson calls, "a delightful romp through history, economics, and the frailties of human nature." See Anderson's entire guest review below.

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Guest Reviewer: Chris Anderson

Chris Anderson is editor-in-chief of Wired magazine and the author of The Long Tail: Why the Future of Business Is Selling Less of More.

Four hundred years ago, Francis Bacon warned that our minds are wired to deceive us. "Beware the fallacies into which undisciplined thinkers most easily fall--they are the real distorting prisms of human nature." Chief among them: "Assuming more order than exists in chaotic nature." Now consider the typical stock market report: "Today investors bid shares down out of concern over Iranian oil production." Sigh. We're still doing it.

Our brains are wired for narrative, not statistical uncertainty. And so we tell ourselves simple stories to explain complex thing we don't--and, most importantly, can't--know. The truth is that we have no idea why stock markets go up or down on any given day, and whatever reason we give is sure to be grossly simplified, if not flat out wrong.

Nassim Nicholas Taleb first made this argument in Fooled by Randomness, an engaging look at the history and reasons for our predilection for self-deception when it comes to statistics. Now, in The Black Swan: the Impact of the Highly Improbable, he focuses on that most dismal of sciences, predicting the future. Forecasting is not just at the heart of Wall Street, but it's something each of us does every time we make an insurance payment or strap on a seat belt.

The problem, Nassim explains, is that we place too much weight on the odds that past events will repeat (diligently trying to follow the path of the "millionaire next door," when unrepeatable chance is a better explanation). Instead, the really important events are rare and unpredictable. He calls them Black Swans, which is a reference to a 17th century philosophical thought experiment. In Europe all anyone had ever seen were white swans; indeed, "all swans are white" had long been used as the standard example of a scientific truth. So what was the chance of seeing a black one? Impossible to calculate, or at least they were until 1697, when explorers found Cygnus atratus in Australia.

Nassim argues that most of the really big events in our world are rare and unpredictable, and thus trying to extract generalizable stories to explain them may be emotionally satisfying, but it's practically useless. September 11th is one such example, and stock market crashes are another. Or, as he puts it, "History does not crawl, it jumps." Our assumptions grow out of the bell-curve predictability of what he calls "Mediocristan," while our world is really shaped by the wild powerlaw swings of "Extremistan."

In full disclosure, I'm a long admirer of Taleb's work and a few of my comments on drafts found their way into the book. I, too, look at the world through the powerlaw lens, and I too find that it reveals how many of our assumptions are wrong. But Taleb takes this to a new level with a delightful romp through history, economics, and the frailties of human nature. --Chris Anderson

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Programming Legend Charles Petzold unlocks the secrets of the extraordinary and prescient 1936 paper by Alan M. Turing

Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be computable, creating the field of computability theory in the process, a foundation of present-day computer programming.

The book expands Turing’s original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing’s statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.

Interwoven into the narrative are the highlights of Turing’s own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41..
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Aimed at any serious programmer or computer science student, the new second edition of Introduction to Algorithms builds on the tradition of the original with a truly magisterial guide to the world of algorithms. Clearly presented, mathematically rigorous, and yet approachable even for the math-averse, this title sets a high standard for a textbook and reference to the best algorithms for solving a wide range of computing problems.

With sample problems and mathematical proofs demonstrating the correctness of each algorithm, this book is ideal as a textbook for classroom study, but its reach doesn't end there. The authors do a fine job of explaining each algorithm. (Reference sections on basic mathematical notation will help readers bridge the gap, but it will help to have some math background to appreciate the full achievement of this handsome hardcover volume.) Every algorithm is presented in pseudo-code, which can be implemented in any computer language, including C/C++ and Java. This ecumenical approach is one of the book's strengths. When it comes to sorting and common data structures, from basic linked lists to trees (including binary trees, red-black, and B-trees), this title really shines, with clear diagrams that show algorithms in operation. Even if you just glance over the mathematical notation here, you can definitely benefit from this text in other ways.

The book moves forward with more advanced algorithms that implement strategies for solving more complicated problems (including dynamic programming techniques, greedy algorithms, and amortized analysis). Algorithms for graphing problems (used in such real-world business problems as optimizing flight schedules or flow through pipelines) come next. In each case, the authors provide the best from current research in each topic, along with sample solutions.

This text closes with a grab bag of useful algorithms including matrix operations and linear programming, evaluating polynomials, and the well-known Fast Fourier Transformation (FFT) (useful in signal processing and engineering). Final sections on "NP-complete" problems, like the well-known traveling salesman problem, show off that while not all problems have a demonstrably final and best answer, algorithms that generate acceptable approximate solutions can still be used to generate useful, real-world answers.

Throughout this text, the authors anchor their discussion of algorithms with current examples drawn from molecular biology (like the Human Genome Project), business, and engineering. Each section ends with short discussions of related historical material, often discussing original research in each area of algorithms. On the whole, they argue successfully that algorithms are a "technology" just like hardware and software that can be used to write better software that does more, with better performance. Along with classic books on algorithms (like Donald Knuth's three-volume set, The Art of Computer Programming), this title sets a new standard for compiling the best research in algorithms. For any experienced developer, regardless of their chosen language, this text deserves a close look for extending the range and performance of real-world software. --Richard Dragan

Topics covered: Overview of algorithms (including algorithms as a technology); designing and analyzing algorithms; asymptotic notation; recurrences and recursion; probabilistic analysis and randomized algorithms; heapsort algorithms; priority queues; quicksort algorithms; linear time sorting (including radix and bucket sort); medians and order statistics (including minimum and maximum); introduction to data structures (stacks, queues, linked lists, and rooted trees); hash tables (including hash functions); binary search trees; red-black trees; augmenting data structures for custom applications; dynamic programming explained (including assembly-line scheduling, matrix-chain multiplication, and optimal binary search trees); greedy algorithms (including Huffman codes and task-scheduling problems); amortized analysis (the accounting and potential methods); advanced data structures (including B-trees, binomial and Fibonacci heaps, representing disjoint sets in data structures); graph algorithms (representing graphs, minimum spanning trees, single-source shortest paths, all-pairs shortest paths, and maximum flow algorithms); sorting networks; matrix operations; linear programming (standard and slack forms); polynomials and the Fast Fourier Transformation (FFT); number theoretic algorithms (including greatest common divisor, modular arithmetic, the Chinese remainder theorem, RSA public-key encryption, primality testing, integer factorization); string matching; computational geometry (including finding the convex hull); NP-completeness (including sample real-world NP-complete problems and their insolvability); approximation algorithms for NP-complete problems (including the traveling salesman problem); reference sections for summations and other mathematical notation, sets, relations, functions, graphs and trees, as well as counting and probability backgrounder (plus geometric and binomial distributions)..
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"Using real world examples, such as traffic flow, politics and baby linguistics, the author makes the theories of 'simplexity' accessible to the layperson ..Kluger makes complex science seem simple."
--Kirkus

"Kluger makes the modern world comprehensible...his astonishing discoveries require no exaggeration..[his] findings are likely to incite controversy, confirming his contention that explaining simplicity and complexity is never as straightforward as it seems."
--Publishers Weekly

"Simplexity...is a study of human behavior, and the way we perceive things and events, and how our perception frequently causes us to make wrong assumptions and to perceive simplicity (or complexity) where it does not exist, The book is sure to be a deserved hit among the ever-growing Freakonomics crowd."
-Booklist

Why are the instruction manuals for cell phones incomprehensible?
Why is a truck driver's job as hard as a CEO's?
How can 10 percent of every medical dollar cure 90 percent of the world's disease?
Why do bad teams win so many games?

Complexity, as any scientist will tell you, is a slippery idea. Things that seem complicated can be astoundingly simple; things that seem simple can be dizzyingly complex. A houseplant may be more intricate than a manufacturing plant. A colony of garden ants may be more complicated than a community of people. A sentence may be richer than a book, a couplet more complicated than a song.

These and other paradoxes are driving a whole new science--simplexity--that is redefining how we look at the world and using that new view to improve our lives in fields as diverse as economics, biology, cosmology, chemistry, psychology, politics, child development, the arts, and more. Seen through the lens of this surprising new science, the world becomes a delicate place filled with predictable patterns--patterns we often fail to see as we're time and again fooled by our instincts, by our fear, by the size of things, and even by their beauty.

In Simplexity, Time senior writer Jeffrey Kluger shows how a drinking straw can save thousands of lives; how a million cars can be on the streets but just a few hundred of them can lead to gridlock; how investors behave like atoms; how arithmetic governs abstract art and physics drives jazz; why swatting a TV indeed makes it work better. As simplexity moves from the research lab into popular consciousness it will challenge our models for modern living. Jeffrey Kluger adeptly translates newly evolving theory into a delightful theory of everything that will have you rethinking the rules of business, family, art--your world.

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How is the human brain like the AIDS epidemic? Ask physicist Albert-László Barabási and he'll explain them both in terms of networks of individual nodes connected via complex but understandable relationships. Linked: The New Science of Networks is his bright, accessible guide to the fundamentals underlying neurology, epidemiology, Internet traffic, and many other fields united by complexity.

Barabási's gift for concrete, nonmathematical explanations and penchant for eccentric humor would make the book thoroughly enjoyable even if the content weren't engaging. But the results of Barabási's research into the behavior of networks are deeply compelling. Not all networks are created equal, he says, and he shows how even fairly robust systems like the Internet could be crippled by taking out a few super-connected nodes, or hubs. His mathematical descriptions of this behavior are helping doctors, programmers, and security professionals design systems better suited to their needs. Linked presents the next step in complexity theory--from understanding chaos to practical applications. --Rob Lightner.
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Few writers distinguish themselves by their ability to write about complicated, even obscure topics clearly and engagingly James Gleick, a former science writer for the New York Times, resides in this exclusive category. In Chaos, he takes on the job of depicting the first years of the study of chaos--the seemingly random patterns that characterize many natural phenomena.

This is not a purely technical book. Instead, it focuses as much on the scientists studying chaos as on the chaos itself. In the pages of Gleick's book, the reader meets dozens of extraordinary and eccentric people. For instance, Mitchell Feigenbaum, who constructed and regulated his life by a 26-hour clock and watched his waking hours come in and out of phase with those of his coworkers at Los Alamos National Laboratory.

As for chaos itself, Gleick does an outstanding job of explaining the thought processes and investigative techniques that researchers bring to bear on chaos problems. Rather than attempt to explain Julia sets, Lorenz attractors, and the Mandelbrot Set with gigantically complicated equations, Chaos relies on sketches, photographs, and Gleick's wonderful descriptive prose..
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An individual ant, like an individual neuron, is just about as dumb as can be. Connect enough of them together properly, though, and you get spontaneous intelligence. Web pundit Steven Johnson explains what we know about this phenomenon with a rare lucidity in Emergence: The Connected Lives of Ants, Brains, Cities, and Software. Starting with the weird behavior of the semi-colonial organisms we call slime molds, Johnson details the development of increasingly complex and familiar behavior among simple components: cells, insects, and software developers all find their place in greater schemes.

Most game players, alas, live on something close to day-trader time, at least when they're in the middle of a game--thinking more about their next move than their next meal, and usually blissfully oblivious to the ten- or twenty-year trajectory of software development. No one wants to play with a toy that's going to be fun after a few decades of tinkering--the toys have to be engaging now, or kids will find other toys.

Johnson has a knack for explaining complicated and counterintuitive ideas cleverly without stealing the scene. Though we're far from fully understanding how complex behavior manifests from simple units and rules, our awareness that such emergence is possible is guiding research across disciplines. Readers unfamiliar with the sciences of complexity will find Emergence an excellent starting point, while those who were chaotic before it was cool will appreciate its updates and wider scope. --Rob Lightner.
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This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents.

John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.

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