Robin Hood Math
Take Control of the Algorithms That Run Your Life
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Robin Hood Math
ナレーター：
Dan Bittner
￥2,000 で購入
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ナレーター：
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Dan Bittner
このコンテンツについて
Everything we do today is recorded as data that's sold to the highest bidder. Plugging our personal data into impersonal algorithms has made government agencies more efficient and tech companies more profitable. But all this comes at a price. It's easy to feel like an insignificant number in a world of number crunchers who care more about their bottom line than your humanity. It's time to flip the equation, turning math into an empowering tool for the rest of us.
Award-winning mathematician Noah Giansiracusa explains how the tech giants and financial institutions use formulas to get ahead—and how anyone can use these same formulas in their everyday life. You’ll learn how to handle risk rationally, make better investments, take control of your social media, and reclaim agency over the decisions you make each day.
In a society that all too often takes from the poor and gives to the rich, math can be a vital democratizing force. Robin Hood Math helps you to think for yourself, act in your own best interests, and thrive.
批評家のレビュー
“Full of engaging stories about how ideas about calculation took shape, [Robin Hood Math] reveals how algorithms intersect with our everyday lives… a lively and engaging storyteller we all need to hear.” –Kirkus
“[A] cogent and breezy account… Throughout, Giansiracusa optimistically illustrates how math can help an individual regain autonomy and makes a solid case that it ‘can help you consider your options more clearly and make your decisions more thoughtfully.’ This successfully brings math to the people.” —Publishers Weekly
“Great clarity, easy-to-understand examples, and plenty of humor. . . . Even the most math-averse readers can find engagement and empowerment in this highly accessible work.” —Library Journal
“This concise book shows that anyone can understand the math used by the big-tech manipulators; anyone can take advantage of the algorithms; anyone can respond thoughtfully to the risks, opportunities, and proliferating digital predators of our brave new world. Its engaging stories and clear explanations offer practical advice and grounds for optimism.” —Paul Romer, Nobel laureate, University Professor in Economics at Boston College, and former Chief Economist of the World Bank
"Rule by algorithms scares a lot of people, and it probably should. In Robin Hood Math, Noah Giansiracusa shows how with a little math training of your own, you can see what the algorithms are trying to hide from you, and even better, take back your own decision-making power." —Jordan Ellenberg, New York Times bestselling author of Shape and How Not to Be Wrong
“[A] cogent and breezy account… Throughout, Giansiracusa optimistically illustrates how math can help an individual regain autonomy and makes a solid case that it ‘can help you consider your options more clearly and make your decisions more thoughtfully.’ This successfully brings math to the people.” —Publishers Weekly
“Great clarity, easy-to-understand examples, and plenty of humor. . . . Even the most math-averse readers can find engagement and empowerment in this highly accessible work.” —Library Journal
“This concise book shows that anyone can understand the math used by the big-tech manipulators; anyone can take advantage of the algorithms; anyone can respond thoughtfully to the risks, opportunities, and proliferating digital predators of our brave new world. Its engaging stories and clear explanations offer practical advice and grounds for optimism.” —Paul Romer, Nobel laureate, University Professor in Economics at Boston College, and former Chief Economist of the World Bank
"Rule by algorithms scares a lot of people, and it probably should. In Robin Hood Math, Noah Giansiracusa shows how with a little math training of your own, you can see what the algorithms are trying to hide from you, and even better, take back your own decision-making power." —Jordan Ellenberg, New York Times bestselling author of Shape and How Not to Be Wrong
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