Artificial Intelligence (AI), in an unprecedented and transformational manner, has recently opened up new possibilities for firms that significantly affect how humans aided with technology make decisions. This course will focus on understanding AI capabilities (including predictive and generative models), designing effective decision-making frameworks that incorporate AI and humans, building a deep appreciation and understanding of some key issues surrounding AI in organizations, and helping develop governance structures for AI in organizations.

Investigating the implications of the major findings of behavioral economics for policymakers. Analysis of the ways in which individuals make decisions that systematically depart from the standard model, which assumes perfect rationality, perfect selfishness, and perfect willpower. Students will develop an understanding of how models are constructed, and how this forms the basis for empirical estimation strategies. We first discuss concepts such as endowment effect, loss aversion, and status-quo bias. Then we introduce uncertainty. The standard model of decision-making under uncertainty is the expected utility model. We then go on to describe some empirical evidence from both economics and psychology to illustrate violations of the expected utility model. Next, we will discuss intertemporal choice. After introducing the standard model of discounting, we will again discuss some observed violations of the standard model in the data.