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We illustrate our approach with a quantitative analysis using published laboratory data, including tests of novel versions of Random Cumulative Prospect Theory. A major asset of the approach is the potential to distinguish decision makers who have a fixed preference and commit errors in observed choices from decision makers who waver in their preferences. Keywords: Behavioral decision research, order-constrained likelihood-based inference, Luce's challenge, probabilistic specification, theory testing 1 Introduction Behavioral decision researchers in the social and behavioral sciences, who are interested in choice under risk or uncertainty, in intertemporal choice, in probabilistic inference, or many other research areas, invest much effort into proposing, testing, and discussing descriptive theories of pairwise preference.
This article provides the theoretical and conceptual framework underlying a new, general purpose, public-domain tool set, the QT est software. 1 QT est leverages high-level quantitative methodology through mathematical modeling and state-of-the-art, maximum likelihood based, statistics.