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Learning Across Alternatives - Steven Callander

gsb-faculty.stanford.edu · 134 words · saved by 1 readers

In many settings agents face difficult problems, be they managers, policymakers, judges, or consumers. A significant part of the difficulty is that decision makers face many options from which to choose yet possess only a tenuous understanding of how these alternatives map into outcomes. In my work, I have introduced an approach to modeling problems of this sort and, along with co-authors, explored its application to a variety of settings in economics, political science, and management. Other researchers have picked up this approach, advancing our understanding further and pushing the approach into new areas. The key novelty of the approach is to represent the mapping from actions to outcomes as the realized path of a Brownian motion. The mapping is fixed throughout time and the agents learn about the mapping through experience, by consulting an expert, by sampling actions, and so on. No matter how many (finite) action-outcome pairs they observe, however, there is always more to learn

Steven Callander Search this site Embedded Files Skip to main content Skip to navigation Steven Callander The Herbert Hoover Professor in Public and Private Management, Stanford Graduate School of Business, and Professor of Political Economy. Professor of Economics (by courtesy), and Professor of Political Science (by courtesy), School of Humanities and Sciences. I am a political economist working at the intersection of business, government, and society. My focus is on how markets, politics, and society work and how they interact. You can find more information about my academic writing at the

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