Experiential learning under ambiguity

Abstract

In this dissertation I study choice contexts of learning under ambiguity. Experiential learning takes place by making choices, observing the outcomes, and altering future choices accordingly. Over time, such learning is expected to improve choices and outcomes but only when decision makers can classify outcomes as either successes or failures. This dichotomous outcome has clear behavioral implications - change after failure or persist after success. Such classification may be complicated when experience is ambiguous, i.e., a choice may not always produce an outcome that can be readily classified as success or failure. Classifying outcomes is challenging when (1) decision makers do not have enough prior experience to determine a sensible threshold between success and failure (i.e. they do not have enough data to formulate historical aspirations), (2) choices do not produce observable outcomes, or (3) it is unclear if the decision maker’s choice or external factors are causing the observed outcome. I explore three choice contexts of learning under ambiguity using experiments and a large dataset on restaurants in New York affected by gentrification as well as short simulation experiments. The objective of study 1 is to identify when social comparison improves or reduces search efficacy in contexts of insufficient prior experience. The objective of study 2 is to examine choices which do not produce observable outcomes. Specifically, I am interested in the effect of omissions which do not produce observable feedback, including contexts where this missing feedback is substituted by the experience of comparable others. The objective of study 3 is to explore how learning is inhibited by noisy feedback on outcomes. This last study shows that the ability to disentangle noise and fundamental change hinges on the pace at which the environment is changing. In conclusion, I identify three choice contexts that produce ambiguous outcomes, and discuss under which conditions decision makers should attempt to learn from such outcomes.

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