Oracles: How Prediction Markets Turn Employees into Visionaries

Oracles: How Prediction Markets Turn Employees into Visionaries

by Donald N. Thompson

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Overview

Oracles: How Prediction Markets Turn Employees into Visionaries by Donald N. Thompson

Why Prediction Markets Are Good for Business

From selecting the lead actress in a Broadway musical, to predicting a crucial delay in the delivery of Boeing’s 787 Dreamliner months before the CEO knew about it, to accurately forecasting US presidential elections—prediction markets have realized some amazing successes by aggregating the wisdom of crowds.

Until now, the potential for this unique approach has remained merely an interesting curiosity. But a handful of innovative organizations—GE, Google, Motorola, Microsoft, Eli Lily, even the CIA—has successfully tapped employee insights to change how business gets done.

In Oracles , Don Thompson explains how these and other firms use prediction markets to make better decisions, describing what could be the origins of a social revolution. Thompson shows how prediction markets can:

• draw on the hidden knowledge of every employee
• tap the “intellectual bandwidth” of retired employees
• replace surveys
• substitute for endless meetings

By showing successes and failures of real organizations, and identifying the common roadblocks they’ve overcome, Oracles offers a guide to begin testing expertise against the collective wisdom of employees and the market—all to the benefit of their bottom line.

Product Details

ISBN-13: 9781422183175
Publisher: Harvard Business Review Press
Publication date: 06/05/2012
Pages: 272
Product dimensions: 6.38(w) x 9.20(h) x 0.92(d)

About the Author

Donald N. Thompson is an economist and professor of marketing, emeritus, at the Schulich School of Business at York University in Toronto. He has taught at Harvard Business School and the London School of Economics. Thompson is author of nine books, including The $12 Million Stuffed Shark: The Curious Economics of Contemporary Art , which has been published in thirteen languages.

Table of Contents

Part 1 Predictions from Markets

1 The Mutual Fun Market 3

2 What Are These Prediction Markets? 13

3 Sports and Movie Markets 27

4 Election Markets 43

5 Estimation Markets 55

6 What Can Prediction Markets Replace? 67

Part 2 What Can Business Markets Do?

7 Google 83

8 Best Buy 97

9 The Technology Evangelist 109

10 Boardroom Markets 117

Part 3 Where Can Markets Take Us?

11 Way-Outside-the-Box Markets 133

Casting Calls and Epidemiology

12 Further Outside the Box 143

Terrorism Markets

13 Government Markets 157

14 Long-Term Markets 167

Part 4 Making Markets Work

15 When No One Wants to Know 179

16 More Red Flags Than Beijing 191

17 Finding the Scorpion 197

18 Becoming an Oracle 207

Postscript and Acknowledgments 217

The Legal Conundrum Facing US Prediction Markets 223

Checklist for Starting a Prediction Market 229

Prediction Market Software 233

Prediction Market Sites Worth Following 237

Further Reading 241

Index 249

About the Author 263

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Oracles: How Prediction Markets Turn Employees into Visionaries 5 out of 5 based on 0 ratings. 1 reviews.
RolfDobelli More than 1 year ago
What do casting a hit musical and predicting the space shuttle Challenger disaster have in common? Both endeavors demonstrate the value of prediction markets – a way to aggregate knowledge, hunches and crowd wisdom – for reaching the best decision or the correct answer in a fashion that resembles a stock market. Organizations use prediction markets, or similar tools, to find a variety of solutions, from deciding on product launches to predicting the winner of presidential elections. Economist Donald N. Thompson explains how these powerful but little-known (and even less-understood) processes can help you improve your business. His text is not a how-to manual but rather a collection of anecdotes, each of which teaches a lesson on how, when or why to use a prediction market. getAbstract finds his treatise to be an easy and surprisingly entertaining read, but what will aggregated reader reviews predict? Only the crowd knows.