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Simplexity: Why Simple Things Become Complex (and How Complex Things Can Be Made Simple) Hardcover – June 3, 2008
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From Publishers Weekly
Frustrated by the traffic on narrow bridges? Stunned by the number of buttons on a remote control? Saddened by the lack of basic medical care in the developing world? Kluger (Splendid Solutions) makes the modern world comprehensible, analyzing social and technological systems to reveal that things that seem complicated can be preposterously simple; things that seem simple can be dizzyingly complex. He compares cells to cities to stock markets, renders quarks and fractals accessible and draws parallels between Wal-Mart and AIDS clinics in Tanzania. Although Kluger is prone to hyperbole, his astonishing discoveries require no exaggeration: the book describes how even the most technologically advanced manufacturing plant is infinitely simpler than a humble houseplant with its microhydraulics and fine-tuned metabolism and dense schematic of nucleic acids—and baseball fans will be dismayed to discover that football is, in fact, the more complex of the two games: the possible number of starting configurations before the play even begins is... 31.4 billion. Kluger's findings are likely to incite controversy, confirming his contention that explaining simplicity and complexity is never as straightforward as it seems. (June)
Copyright © Reed Business Information, a division of Reed Elsevier Inc. All rights reserved.
Yes, simplexity is a new word—a whole new theory, in fact. In essence, simplexity holds that simple things become more complex (even unnecessarily complex) over time, while complex things can become (or be made) simpler. OK, so that sounds rather blindingly obvious: who, for example, hasn’t shaken his head at the sheer verbiage of cell phone or camera instructions? But here Kluger, coauthor of the best-selling Lost Moon (1994), which became the film Apollo 13, doesn’t merely trot out examples of simple things that became complex, and complex things that were simplified; he explores how they got that way and why. Instructions, for example, are complicated because the products themselves can do so many different things—the consumer’s demand for flexibility leads to complexity. Simplexity, the theory, is intriguing and plausible. Simplexity, the book, 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. --David Pitt
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Examples used: Groups of people tend to make better decisions than single individuals (e.g., guessing the number of jelly beans or investing clubs' ability to get a good return); traffic engineers trying to reduce traffic jams and major slowdowns by rather simple adjustments in traffic flow. A political example? Failed states and totalitarian states are very easy to model; they are pretty predictable. Multiparty states or Western democracies, on the other hand, are more complex. This facilitates openness in communication.
There are many intriguing and thought-provoking examples. But the final chapter does not pull things together; one ends with a sense of pastiche. In addition, there really aren't any principles presented to show how to handle complexity, how to gain leverage with respect to simplexity. So, thought-provoking but also a bit maddening.
1. It is the region between order and disorder which gives complexity. For instance, a piece of copper pipe can be thought of as a simple piece of frozen solid, but it gets more complex as a network of plumbing in a skyscraper, and extremely compex at the subatomic level.
2. Any system must be seen at all levels before determining if it is complex.
3. The stock market may seem complex, but major changes in it come more from some bland pieces of information, rather than from some catastrophic news events.
4. With experiments involving how people will divide up money, they show that we "appear to be wired for justice".
5. Although human behavior can't be mathematized, it is influenced thusly. For example, 1.3M people die in auto accidents globally, yet something as simple as well placed speed bumps could reduce that amount quite significantly, since there is about an 80% death rate when accidents happen above 35mph.
6. Fads spread faster rather than by first influencing the most popular people first, but by first influencing a smaller number of people but those having a close relationship with the originator.
7. Politically, it is better to market ideas about a candidate which voters already think, than by breaking bigger, more surprising things. Hence, a simple approach is more effective in such situations.
8. Complicated work skills appear at some of the least prestigous jobs. For instance, truck drivers must process visual, tactile, auditory and cognitive skills all instantaneously.
9. Animals have about 1B heartbeats per lifetime, influenced by the mass of the animal. Based on that, humans should only live to age 20 or so, but they are the exception because they use their brains to "game the system".
10. Since humans survive by converting carbs to CO2 + H2O + energy, perhaps inorganic substances share overlapping similarities with humans since CO2 and H2O also are associated with them, like with cities compared with humans.
11. Zipf's Law - certain mathematical patterns appear as how often centain words and terms show up in text. Therefore, maybe similar patterns show up elsewhere, like income, size of corporations and urban populations. They do seem to do so.
12. "Probability neglect" - people seem to fear a catastophic event (terrorist attack) rather than a more chronic one (climate change) when the chronic one can cause greater harm. Apparently, there are two systems for analyzing risk, automatic (feelings) and more thoughtful (experience).
13. "Availability heuristic" - the better able it is to summon an image of a dangerous event, the easier it is to be afraid of it.
14. In the computer world, it has been said "there are two kinds of people, software engineers and those who are afraid of them".
15. Muhammad Yunnis, who pioneered microloans, showed that surgical strikes, so to speak, are more effective in reducing poverty, but that involves complex analysis.
16. The Pareto principle applies to an 80:20 rule when looking at income distribution, but also shows up in all sorts of things.
17. For the arts, complexity falls flat, but there is still a connection. Sure, in music, but also in art, reflective of nature's quasicrystals, which have seemingly symmetric patterns though they never repeat. Jackson Pollack's paintings exhibit the mathematical concept of fractals.
So, overall, if one wants to ponder how the mind works or might work better, there are some helpful thoughts here.
The problem is this: we would like a well-defined, mathematically precise definition of complexity, the kind of complexity that we see in biological life, but we don't really have one. The most widely used definition of information, negative Shannon entropy, is completely wrong for the job. Systems with high entropy and low information, like a hot gas, have no complexity because they have no structure. Systems with low entropy and near-perfect information, like a flawless crystal near absolute zero, have no complexity because they have no variety. Complex systems exist in between these two extremes, and their complexity must be measured along a different axis. But what axis?
With this set up, and given the book's title, I was expecting Kluger to attempt to answer this question. But instead, chapter after chapter, he just gives us a bunch of vaguely related anecdotes about systems he considers complex and why they are interesting. Nowhere in the remainder of the book does he step back and try to draw any conclusions or general principles from these many examples. Nearly 300 more pages go by with no reflection, no analysis, and no attempt to achieve any deeper level of understanding. And then the book ends, with no summary or conclusions.
I fail to see the point. If you want a bunch of informal description of possibly complex systems, this book might be adequate. But if you're looking for a deep understanding of complexity, check this book out of a library, read the first chapter, and then take it back. What you seek is elsewhere.
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Very good reference material and theories on risk psychology with supporting/relevant case examples.Read more