Why did the New York Stock Exchange suspend trading without warning on July 8, 2015? Why did certain Toyota vehicles accelerate uncontrollably against the will of their drivers? Why does the programming inside our airplanes occasionally surprise its creators?
After a thorough analysis by the top experts, the answers still elude us.
You don't understand the software running your car or your iPhone. But here's a secret: Neither do the geniuses at Apple or the PhDs at Toyota - not perfectly, anyway. No one - not lawyers, doctors, accountants, or policy makers - fully grasps the rules governing your tax return, your retirement account, or your hospital's medical machinery. The same technological advances that have simplified our lives have made the systems governing our lives incomprehensible, unpredictable, and overcomplicated.
In Overcomplicated, complexity scientist Samuel Arbesman offers a fresh, insightful field guide to living with complex technologies that defy human comprehension. As technology grows more complex, Arbesman argues, its behavior mimics the vagaries of the natural world more than it conforms to a mathematical model. If we are to survive and thrive in this new age, we must abandon our need for governing principles and rules and accept the chaos. By embracing and observing the freak accidents and flukes that disrupt our lives, we can gain valuable clues about how our algorithms really work. What's more, we will become better thinkers, scientists, and innovators as a result.
Lucid and energizing, this audiobook is a vital new analysis of the world heralded as "modern" for anyone who wants to live wisely.
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- Shopping for Gifts
Absolutely Useless. What a waste of time.
The author, despite being a "complexity expert" seems utterly unable (or uninterested) to answer the big questions: 1. How much of a problem--other than "wow! really big and scary!" is it that complexity grows exponentially in the modern world, and 2. What techniques can be used to combat it?
Famously, he uses the debunked "Toyota unintentional acceleration" scare as one of his key points that "Gee, things have gotten so complex that _nobody_ fully understands why things happen in systems like this." Similarly, he constantly lectures from either anecdote or example, quoting science columnists or opinion leads for data instead of actual science or research. Where he does offer "statistical" data, beyond "just lots and lots", it usually devolves into the form of "Well, if you assume multiplied by " you get ". Never mind whether either such number exists in the original example being talked about, or whether there might be other factors which limit the growth of the resulting complexity in the model.
He is constantly using false analogies--bordering on non-sequitors--to make his points, and seems allergic to both quoting any actual scientific study or... you know... numbers. Instead, he'll make points by saying that a given psychological phenomena is "just like that Vorhees story" without ever quoting actual research to establish either the extent or effect of it.
Maddeningly, to anyone who actually knows anything about computes and programming, he's also inordinately fond of making "tech-sounding" metaphors while simultaneously pointing to such ridiculously outmoded concepts as "Goto" statements to point out why programs are getting so hard to decipher. I could go on for ages, but I feel like this book has wasted enough of my life already.
- Peter Bickford