In "Prediction Machines," economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explore the advancement and growing use of artificial intelligence (AI). The key to AI is not actually intelligence but prediction. This text looks at the value of prediction and data, the importance of trade-offs, and the impact of AI in the workplace. Beneficial to business leaders, financial analysts, policy makers, and students, "Prediction Machines" offers insights, tools, and strategies on how to adapt businesses to the world's ever-growing use of AI. Part 2, covering chapters 7 through 11, focuses on decision making. The components of decision making are outlined, including judgment. The value and complexity of judgment and predicting judgment are thoroughly discussed. Levels of automation in decision making are also explored, from minor machine involvement to fully automated decision making. Chapter 9 discusses machines learning to predict human judgment. An example discussed is driverless cars. However, a lack of data limits a machine's ability to predict human judgment, and there is some valuable data that people have, but machines don't. When it comes to rare events, machines are poor at prediction; they cannot predict judgment when a situation hasn't happened several times previously. In this way, human judgment is still vital.