A Dynamic, Stochastic, Computational Model of Preference Reversal Phenomena.

Abstract

Preference orderings among a set of options may depend on the elicitation method (e.g., choice or pricing); these preference reversals challenge traditional decision theories. Previous attempts to explain these reversals have relied on allowing utility of the options to change across elicitation methods by changing the decision weights, the attribute values, or the combination of this information--still, no theory has successfully accounted for all the phenomena. In this article, the authors present a new computational model that accounts for the empirical trends without changing decision weights, values, or combination rules. Rather, the current model specifies a dynamic evaluation and response process that correctly predicts preference orderings across 6 elicitation methods, retains stable evaluations across methods, and makes novel predictions regarding response distributions and response times.

Constructing preference from experience: The endowment effect reflected in external information search.

“…Johnson et al, 2007), we argued that buyers and sellers differ in their information search. Based on a computational model of preference construction (J. G. Johnson & Busemeyer, 2005), we proposed and tested a search termination thesis of the endowment effect in a situation that involves external information search. 5 Note that the link between outcome-contingent stopping behavior and the endowment effect implies a recency effect.…”

“…Toward the goal of developing a more process-oriented account of the endowment effect, it has been speculated that buyers and sellers focus their attention on different aspects of the target object (e.g., Carmon & Ariely, 2000). More recently, it has been proposed that these differences in attention between buyers and sellers lead to differences in information search (e.g., J. G. Johnson & Busemeyer, 2005; E. J. Johnson, Häubl, & Keinan, 2007).…”

“…In line with previous proposals, we assume that because sellers and buyers have different goals (Carmon & Ariely, 2000), they differ in their attention to the positive and negative aspects of an object (Birnbaum, Yeary, Luce, & Zhao, 2002): Sellers focus on reasons for demanding a high price, whereas buyers focus on reasons for offering a low price. In their sequential value matching (SVM) model, J. G. Johnson and Busemeyer (2005) proposed that due to these different perspectives, sellers and buyers draw initial candidate responses for their evaluation of an object (e.g., a lottery) from differently shaped distributions. For sellers, the distribution is skewed toward high values, whereas for buyers the distribution is skewed toward low values.…”

“…The main goal of this article is to test whether differences in search termination indeed exist and whether these differences are predictive of the endowment effect. In addition, we test the prediction of the SVM model that sampling should be more extensive the higher the variance of a lottery (for details, see J. G. Johnson & Busemeyer, 2005).…”

Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

“…Models based on these three plausible assumptions have been used to model choices and decision times in sensory detection (Smith, 1995), perceptual discrimination (Link & Heath, 1975;Ratcliff & Rouder, 1998), memory recognition (Ratcliff, 1978), categorization (Ashby, 2000;Nosofsky & Palmeri, 1997), risky decision making (Busemeyer & Townsend, 1993;J. G. Johnson & Busemeyer, 2005), multiattribute, multialternative decisions (Diederich, 1997;Roe, Busemeyer, & Townsend, 2001), as well as other types of response tasks like the go/no-go task (Gomez, Perea, & Ratcliff, 2007).…”