The Efficiency Paradox
What Big Data Can't Do
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3.5 • 2 Ratings
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- $4.99
Publisher Description
A "skillful and lucid" (The Wall Street Journal) way of thinking about efficiency, challenging our obsession with it—and offering a new understanding of how to benefit from the powerful potential of serendipity.
Algorithms, multitasking, the sharing economy, life hacks: our culture can't get enough of efficiency. One of the great promises of the Internet and big data revolutions is the idea that we can improve the processes and routines of our work and personal lives to get more done in less time than we ever have before. There is no doubt that we're performing at higher levels and moving at unprecedented speed, but what if we're headed in the wrong direction?
Melding the long-term history of technology with the latest headlines and findings of computer science and social science, The Efficiency Paradox questions our ingrained assumptions about efficiency, persuasively showing how relying on the algorithms of digital platforms can in fact lead to wasted efforts, missed opportunities, and, above all, an inability to break out of established patterns. Edward Tenner reveals what we and our institutions, when equipped with an astute combination of artificial intelligence and trained intuition, can learn from the random and unexpected.
PUBLISHERS WEEKLY
Historian Tenner (Why Things Bite Back) argues that supposed advances in technological efficiency can actually be self-subverting in this reasoned antidote to a culture increasingly obsessed with doing more with less. He starts by examining the history of innovations premised on efficiency, first seen in continuous production models such as Ford's assembly line, and more recently in the rise of digital platform companies, which are "based less on the organization of machines and human labor than the gathering, analysis, and exchange of data." The book then segues into hot topics such as rideshare apps, GPS, and self-driving cars. Tenner demonstrates how systems such as these, which are premised on efficiency, reduce serendipity, stifle learning, and limit humans' ability to respond when malfunction occurs; they also, he argues, create substantial lost opportunity cost in the long term. Tenner also addresses the fallacies of big data and how random initial advantages from algorithms (such as Google's PageRank, which attempts to deliver information that people want rather than what they asked for) can hide the long-term codification of systemic bias. Tenner is no luddite; he evaluates the positives and negatives of technology through a strong base of evidence rather than nostalgia or personal anecdote, and debunks some of the most popular concerns about automation. Tenner's insightful study of the effects of information technology on society warrants close attention.
