In our view, the typical approach to diagnosing the empirical validity of such proposals tends to fall into either of two extreme categories, similar to the patient either consulting with a lay person (and maybe a thermometer) or with a radiologist. Over recent decades, the behavioral sciences have experienced an explosion in theoretical proposals to explain one or the other phenomenon in choice behavior across a variety of substantive areas. You may seek diagnostic information from a radiologist. You may seek diagnostic information from a nurse practitioner. You may seek diagnostic information from another lay person and/or a fever thermometer. You consider three methods of diagnostics: Imagine that you experience intense abdominal pain. The program features a simple Graphical User Interface and is general enough that it can be applied to a large number of substantive domains.Ĭonsider a motivating analogy between theory testing and diagnostics in daily life. Yet, it automates enough of the process that many of its features require no more than relatively basic skills in math and statistics. 1 QT est leverages high-level quantitative methodology through mathematical modeling and state-of-the-art, maximum likelihood based, statistics. This article provides the theoretical and conceptual framework underlying a new, general purpose, public-domain tool set, the QT est software. 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.īehavioral 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. The paper is supplemented with a custom-designed public-domain statistical analysis package, the QT est software.

Our theoretical framework links static algebraic decision theory with observed variability in behavioral binary choice data. This permits the nontechnical scholar to proceed far beyond traditionally rather superficial methods of analysis, and it permits the quantitatively savvy scholar to triage theoretical proposals before investing effort into complex and specialized quantitative analyses.

We provide a novel, rigorous, yet very general, quantitative diagnostic framework for testing theories of binary choice. The goal of this paper is to make modeling and quantitative testing accessible to behavioral decision researchers interested in substantive questions.

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