Date of Award

5-2018

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Industrial Engineering

Committee Chair

Steven J. Landry

Committee Member 1

Sara A. McComb

Committee Member 2

Hong Wan

Committee Member 3

Brandon J. Pitts

Abstract

Judgment studies using different set of cues have been used from over 50 years to understand how people make judgments. Brunswik’s Lens Model, a regression model which combines the ecological and the human’s judgment model using a common set of cues, has been the primary tool of choice for these judgment studies. Yet, despite a common model, there is no consensus on how much information people need to make judgments, or how the information should be represented or even how the judgment options should be presented to people. Therefore, this study in the first phase, used a Monte Carlo simulation to check whether lens model is sensitive to these task characteristics, followed by human subject experiments to understand the effect of these task characteristics on human judgment in a controlled laboratory setting. The simulation runs with two judgment policies provide evidence that lens model is insensitive to the change in task characteristics, which were consistent with the past meta-analysis. Also, from the human subject experiments, no evidence was found that varying task characteristics (cardinality of the cue set, representation of the cues and representation of the judgment options) have any effect on human judgment. Furthermore, the methodology used in this study for both simulation and judgment analysis should be used as a stepping stone by judgment analysis researchers to understand the effect on human judgment in other domains and while using different judgment models.

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