Research

Ku, J. (2023) Sequential Search under Range-Dependent Attribute Weighting (working paper)
Abstract: Sequential search has typically been explored with single attribute objects and without recall, despite both empirical and experimental evidence for the latter. In sequential search, recall is defined as taking home an alternative that is not the one that most recently arrived when the decision maker stops searching, which is ruled out in neoclassical theory. I design a new search experiment where objects have multiple attributes and recall is allowed. The accompanying model extends two opposing behavioral models, Koszegi & Szeidl (2013) and Bushong, Rabin, and Schwarzstein (2021), into the sequential search environment. It allows for idiosyncratic and time varying valuations of multi-attribute objects, leading to predictions of recall in specific situations. My parametric analysis indicates that there are multiple types of searching agents within the subject pool: neoclassical agents, behavioral agents, and others. Across all participants, recall is used 44% of the time on average, similar to findings in previous studies.
Presented at:
BABEEW (Bay Area Behavioral and Experimental Economics Workshop) 2024. University of California, Davis.
2023 North American ESA Meeting (Economic Science Association). University of North Carolina at Charlotte.

Ku, J. (2022) Range-Dependent Attribute Weighting: An Experiment on Spreading and Consolidating (working paper)
Abstract: The range of an attribute's outcomes in a choice set can possibly change its relative importance. From two prominent theories of range-dependent attribute weighting, the focusing model of Köszegi and Szeidl (2013) and the relative thinking model of Bushong, Rabin, and Schwarzstein (2021), many researchers have tested the two models against each other by means of adding irrelevant alternatives/decoys. Building off of the experiment in Somerville (2022), I derive predictions from these two theories and test them in an experiment in which subjects choose from pairs of bundles where the ``number" of advantages or disadvantages vary.
Presented at:
BABEEW (Bay Area Behavioral and Experimental Economics Workshop) 2023. San Jose State University.