The goal of this paper is to make modeling and quantitative testing accessible to behavioral decision researchers interested in substantive questions. We provide a novel, rigorous, yet very general, quantitative diagnostic framework for testing theories of binary choice. 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. Our theoretical framework links static algebraic decision theory with observed variability in behavioral binary choice data. The paper is supplemented with a custom-designed public-domain statistical analysis package, the QTest 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.
An important challenge in Markov decision processes is to ensure robustness with respect to unexpected or adversarial system behavior while taking advantage of well-behaving parts of the system. We consider a problem setting where some unknown parts of the state space can have arbitrary transitions while other parts are purely stochastic. We devise an algorithm that is adaptive to potentially adversarial behavior and show that it achieves similar regret bounds as the purely stochastic case.
Behavioral theories of intertemporal choice involve many moving parts. Most descriptive theories model how time delays and rewards are perceived, compared, and/or combined into preferences or utilities. Most behavioral studies neglect to spell out how such constructs translate into heterogeneous observable choices. We consider several broad models of transitive intertemporal preference and combine these with several mathematically formal, yet very general, models of heterogeneity. We evaluate 20 probabilistic models of intertemporal choice using binary choice data from two large scale experiments. Our analysis documents the interplay between heterogeneity and parsimony in accounting for empirical data: We find evidence for heterogeneity across individuals and across stimulus sets that can be accommodated with transitive models of varying complexity. We do not find systematic violations of transitivity in our data. Future work should continue to tackle the complex trade-off between parsimony and heterogeneity.
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