I am a PhD candidate specializing in micro theory and experimental economics at the University of Toronto.
I will be available for interviews during the 2020-2021 American, Canadian, and European job markets.
(Job Market Paper)
To predict changes in choice behavior and estimate preferences we need to understand how the opportunity cost of information impacts choice mistakes. This paper characterizes the mistake patterns that are consistent with two major models of inattention when price changes, and extends the analysis to aggregate data that incorporates heterogeneous decision makers. Experiment participants are shown to be sophisticated when choosing both if and what to learn. Participants are most likely to learn in the setting predicted by the theory, and there is evidence that they accumulate information gradually in a way that allows them to alter the relative chances of different kinds of choice mistakes.
This paper uses an axiomatic foundation to create a new measure for the cost of learning that allows for multiple perceptual distances in a single choice environment so that some events can be harder to differentiate between than others. The new measure maintains the tractability of Shannon's classic measure but produces richer choice predictions and identifies a new form of informational bias significant for welfare and counterfactual analysis.
This note studies the implications of perceptual distance for choice behavior in models of rational inattention. Using a measure for the cost of information that is more flexible than Shannon's standard measure of entropy, this note creates a new foundation for 'non-compensatory' behavior, whereby increasing the value of an option can result in a lower chance of it being selected, and demonstrates novel predictions for the formation of consideration sets. This note thus connects the literatures on rational inattention and heuristic choice rules and presents new challenges for revealed preference analysis.