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 Ph.D.’s 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 book is a vital new analysis of the world heralded as “modern” for anyone who wants to live wisely.
“[Arbesman] argues convincingly…He is undoubtedly right. The dream that we can fully understand our complex man-made systems is just that—a dream.”
—The Wall Street Journal
“Acquiring fluency in systems-speak is fast becoming a civic duty… Arbesman’s freshly elucidated concepts are excellent field tools: they are the translucent sampling containers you take with you as you wade through the glitch-infested shallows of an algorithm.”
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