Testing transitivity using inequality-constrained inference: Transitivity, probabilistic models, and inequality-constrained inference
Huang, Yu
This item's files can only be accessed by the System Administrators group.
Permalink
https://hdl.handle.net/2142/127479
Description
Title
Testing transitivity using inequality-constrained inference: Transitivity, probabilistic models, and inequality-constrained inference
Author(s)
Huang, Yu
Issue Date
2024-12-05
Director of Research (if dissertation) or Advisor (if thesis)
Regenwetter, Michel
Doctoral Committee Chair(s)
Regenwetter, Michel
Committee Member(s)
Koehn, Hans Friedrich
Hotaling, Jared
Cervantes, V´ıctor
Cavagnaro, Daniel
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Rational Choice Theory
Transitivity
Probabilistic Choice
Inequality-Constrained Statistical Methods
Bayes Factors
Convex Polytopes
Social Dominance Hierarchies
Social Choice Theory
Random Utility Model
Abstract
This dissertation investigates rational decision-making through transitivity, probabilistic choice models, and inequality-constrained statistical inference. Applying established statistical frameworks to new domains, it examines transitivity in animal dominance hierarchies, moral judgment, and social choice theory. The work demonstrates that dominance hierarchies in animals and moral preferences in humans largely satisfy transitivity, while providing empirical evaluations of social choice methods. A key methodological contribution is a new algorithm for computing Bayes factors in high-dimensional inequality-constrained models, offering superior performance for high-dimensional statistical analyses. This work extends existing theoretical and statistical tools to new empirical domains while advancing our understanding of rational choice behavior.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
