The dynamics of deferred decision

Abstract

Decision makers are often unable to choose between the options that they are offered. In these settings they typically defer their decision, that is, delay the decision to a later point in time or avoid the decision altogether. In this paper, we outline eight behavioral findings regarding the causes and consequences of choice deferral that cognitive theories of decision making should be able to capture. We show that these findings can be accounted for by a deferral-based time limit applied to existing sequential sampling models of preferential choice. Our approach to modeling deferral as a time limit in a sequential sampling model also makes a number of novel predictions regarding the interactions between choice probabilities, deferral probabilities, and decision times, and we confirm these predictions in an experiment. Choice deferral is a key feature of everyday decision making, and our paper illustrates how established theoretical approaches can be used to understand the cognitive underpinnings of this important behavioral phenomenon.

Process and Content in Decisions from Memory

“…Likewise, most sequential sampling models predict response times, and some are also capable of predicting confidence ratings, choice deferral, and other decision-relevant variables (e.g. Bhatia & Mullett, 2016;Pleskac & Busemeyer, 2010). Many decision models also characterize how various situational features of the decision task (e.g.…”

Racing Against The Clock: Evidence-Based Vs. Time-Based Decisions

“…Terminating visual search has also been modeled as a 'time to quit' signal: each inspected element of the display not containing the target contributes toward a dynamically accumulated signal that is compared to a 'quitting time' threshold, producing a target-absent response when crossed (Shi, Allenmark, Zhu, Elliott, & Müller, 2020;Wolfe & Van Wert, 2010). Paradigms where a decision can be made to defer making a choice have been modeled with an evidence accumulation mechanism racing against an accumulator driven by a constant input; if the latter accumulator reaches threshold first then the choice is deferred (Bhatia & Mullett, 2016).…”

Similarity and decision time in preferential choice

“…They allow preference accumulation models to account for violations of the proportionality and strong stochastic transitivity axioms in multiattribute choice generated by varying choice option similarity (Roe et al, 2001). When combined with other psychological mechanisms, they are also able to predict numerous similarity-based moderators of context effects (Bhatia, 2013;Bhatia & Mullett, 2016;Diederich, 1997Diederich, , 2003Roe et al, 2001). In related domains, such as risky choice, this mechanism allows preference accumulation models to generate violations of stochastic dominance and to display sensitivity to the covariance in the payoffs of the available options (Andraszewicz, Rieskamp, & Scheibehenne, 2015;Busemeyer & Townsend, 1993;Diederich & Busemeyer, 1999).…”

“…Preference accumulation models possess desirable statistical and neurobiological interpretations (e.g., Gold & Shadlen, 2007), and variants of these models are used to describe decision-making in low-level domains (e.g., Ratcliff & Smith, 2004). Importantly, preference accumulation models are able to account for a range of contextual determinants of choice probability in multiattribute choice tasks, including decoy effects, reference point effects, attention, and choice deferral effects (Bhatia, 2013; Bhatia & Mullett, 2016; Diederich, 1997; Krajbich, Armel, & Rangel, 2010; Roe, Busemeyer, & Townsend, 2001; Trueblood, Brown, & Heathcote, 2014; Usher & McClelland, 2004). They are also able to describe the relationship between decision time and various features of the choice task.…”