How the twain can meet: Prospect theory and models of heuristics in risky choice

Abstract

Two influential approaches to modeling choice between risky options are algebraic models (which focus on predicting the overt decisions) and models of heuristics (which are also concerned with capturing the underlying cognitive process). Because they rest on fundamentally different assumptions and algorithms, the two approaches are usually treated as antithetical, or even incommensurable. Drawing on cumulative prospect theory (CPT; Tversky & Kahneman, 1992) as the currently most influential instance of a descriptive algebraic model, we demonstrate how the two modeling traditions can be linked. CPT's algebraic functions characterize choices in terms of psychophysical (diminishing sensitivity to probabilities and outcomes) as well as psychological (risk aversion and loss aversion) constructs. Models of heuristics characterize choices as rooted in simple information-processing principles such as lexicographic and limited search. In computer simulations, we estimated CPT's parameters for choices produced by various heuristics. The resulting CPT parameter profiles portray each of the choice-generating heuristics in psychologically meaningful ways-capturing, for instance, differences in how the heuristics process probability information. Furthermore, CPT parameters can reflect a key property of many heuristics, lexicographic search, and track the environment-dependent behavior of heuristics. Finally, we show, both in an empirical and a model recovery study, how CPT parameter profiles can be used to detect the operation of heuristics. We also address the limits of CPT's ability to capture choices produced by heuristics. Our results highlight an untapped potential of CPT as a measurement tool to characterize the information processing underlying risky choice.

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

“…Although here variants of a reinforcement learning model perform best (see Appendix D), it is those of the natural-mean model, which assumes, as cumulative prospect theory, no distortion through learning and memory (e.g., recency). Thus, we are confident that the lack of support for memory and learning processes in our data is not due to model flexibility issues; nevertheless, future comparisons of computational models of the endowment effect should also employ alternative methods, such as out-of-sample prediction (e.g., Busemeyer & Wang, 2000) or Bayesian methods (e.g., Lee & Wagenmakers, 2013;Pachur, Suter, & Hertwig, 2017). The lack of evidence for memory and learning mechanisms is consistent with previous comparisons of learning models and cumulative prospect theory (Frey, Mata, & Hertwig, 2015;Glöckner et al, 2016), whereas in Erev et al (2010) the latter was outperformed by learning models.…”

Susceptibility to Spear-Phishing Emails

“…Humans often apply heuristics that can be beneficial in promoting fast and frugal actions, but can also result in faulty decision-making (Ferreira and Lenzini 2015; Gigerenzer and Gaissmaier 2011; Kahneman and Egan 2011;Pachur et al 2017). In the context of persuasion, Cialdini (2007) proposed that humans have fixed behavioral patterns triggered by certain events, which may be considered the psychological correlate of sensorimotor reflexes.…”

Money does not stink: Using unpleasant odors as stimulus material changes risky decision making

“…To understand the changes in the decision process better, Pachur et al (2014Pachur et al ( , 2017 and Suter et al (2016) modeled participants' choices with cumulative prospect theory (CPT; Tversky & Kahneman, 1992). CPT assumes that people's choices between risky options can be described by an expectation-based calculus that multiplies the subjective value of the outcomes by the subjective probabilities.…”

“…To analyze whether the differences in the observed decisions stem from a change in the probability weighting function as suggested by the literature on hypothetical affect-rich gambles, we modeled the observed choices with CPT (for details, see Appendix B). Following previous research (Lejarraga et al, 2016;Pachur et al, 2017;Suter et al, 2016), we used the four-parameter version of CPT, with the alpha parameter defining the curvature of the utility function, the delta and gamma parameters defining the elevation and curvature of the probability weighting function, respectively, and the theta parameter capturing choice sensitivity. 5 We implemented CPT in a hierarchical Bayesian framework, assuming for each parameter a joint distribution at the group level across both odor and monetary decisions and one additional parameter that coded the difference between odor and monetary decisions.…”

“…Although little research has investigated how odors change the decision process, a relatively large number of studies have examined how risky decision making changes when outcomes are affect rich (i.e., elicit strong affective responses) compared with affect poor (i.e., elicit only weak or no affective responses; e.g., Hsee & Rottenstreich, 2004; Lejarraga, Pachur, Frey, & Hertwig, 2016; Pachur, Hertwig, & Wolkewitz, 2014; Pachur, Suter, & Hertwig, 2017; Petrova, Van der Pligt, & Garcia‐Retamero, 2014; Rottenstreich & Hsee, 2001; Sunstein & Zeckhauser, 2011; Suter, Pachur, & Hertwig, 2016). Given the salient hedonic dimension of odors (e.g., Mohanty & Gottfried, 2013; for an overview, see O'Doherty, 2007), these studies seem highly relevant when exploring how olfactory stimuli can change risky decision making.…”