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Ajay Agrawal, Joshua Gans and Avi Goldfarb with Prediction Machines: The Simple Economics of Artificial Intelligence
April 17, 2018
Avi Goldfarb is the Ellison Professor of Marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab, Senior Editor at Marketing Science, a Fellow at Behavioral Economics in Action at Rotman, and a Research Associate at the National Bureau of Economic Research. His research has been widely covered in the popular press, including the Economist, the Globe and Mail, the National Post, CBC Radio, National Public Radio, Forbes, Fortune, the Atlantic, the New York Times, the Financial Times, the Wall Street Journal, and many others.
Ajay Agrawal is Professor of Strategic Management and Peter Munk Professor of Entrepreneurship at the University of Toronto’s Rotman School of Management. He is also a Research Associate at the National Bureau of Economic Research, cofounder of The Next 36 and Next AI, and founder of the Creative Destruction Lab. Professor Agrawal conducts research on technology strategy, science policy, entrepreneurial finance, and the geography of innovation. He is also cofounder of the AI/robotics company Kindred.
Joshua Gans is Professor of Strategic Management and the holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management, University of Toronto. Gans is a frequent contributor to outlets like the New York Times, Harvard Business Review, Forbes, Slate, and the Financial Times. He regularly appears on television and radio including appearances on BBC World Service, All Things Considered, Planet Money, and Freakonomics Radio. Joshua also writes regularly at several blogs including Digitopoly.
About Prediction Machines: The Simple Economics of Artificial Intelligence
The idea of artificial intelligence–job-killing robots, self-driving cars, and self-managing organizations–captures the imagination, evoking a combination of wonder and dread for those of us who will have to deal with the consequences. But what if it’s not quite so complicated? The real job of artificial intelligence, argue these three eminent economists, is to lower the cost of prediction. And once you start talking about costs, you can use some well‐established economics to cut through the hype.
The constant challenge for all managers is to make decisions under uncertainty. And AI contributes by making knowing what’s coming in the future cheaper and more certain. But decision making has another component: judgment, which is firmly in the realm of humans, not machines. Making prediction cheaper means that we can make more predictions more accurately and assess them with our better (human) judgment. Once managers can separate tasks into components of prediction and judgment, we can begin to understand how to optimize the interface between humans and machines.
More than just an account of AI’s powerful capabilities, Prediction Machines shows managers how they can most effectively leverage AI, disrupting business as usual only where required, and provides businesses with a toolkit to navigate the coming wave of challenges and opportunities.