Rational preference shifts in multi-attribute choice: what is fair?

Rational preference shifts in multi-attribute choice: What is fair? Pradeep Shenoy (pshenoy@cs.washington.edu) Microsoft AdCenter, Bangalore, India Angela J. Yu (ajyu@ucsd.edu) Department of Cognitive Science, UC San Diego, 9500 Gilman Dr. MC 0515 La Jolla, CA 92093 USA Abstract In the attraction effect (Fig. 1A), given two similarly preferred options, A and B, the introduction of a third option Z that is similar to B, but also clearly inferior to B in one or both attribute dimensions, results in an in- crease in relative preference for B over A (Huber, Payne, & Puto, 1982; Heath & Chatterjee, 1995). In the com- promise effect (Fig. 1B), when B > A in one attribute and B < A in another attribute, and Z has the same tradeoff but is even more extreme than B, then B be- comes the “compromise” option and becomes preferred relative to A (Simonson, 1989). In the similarity effect (Fig. 1C), the introduction of a third option Z, that is very similar and comparable to B in both attribute di- mensions, shifts the relative preference away from B to A (Tversky, 1972). Keywords: multi-attribute decision-making; preference shift; context effects; attraction effect; compromise effect; similarity effect Attraction A B B Compromise A B Introduction Everyday decision-making often involves choosing among options that differ in multiple attribute dimen- sions. For example, should you buy a house that is more spacious or one that is better located? Understanding how humans make these multi-attribute decisions, and how their choices depend on the context, is an important problem in cognitive science. Multi-attribute decision-making is particularly chal- lenging because there is often no universal or intrinsic way to assign relative values to the different attributes. This is especially true in contexts where the decision- maker has limited experience (and thus significant uncer- tainty about market conditions), such as with big-ticket items like houses, or new technology like smart phones. Human choice behavior in multi-attribute problems ex- hibits certain systematic shifts due to context changes, such as when the relative preference between two op- tions shift or even reverse when a third option, known as a decoy, is added, leading to suggestions of underly- ing irrationality or suboptimality (Kahneman & Tver- sky, 1979; Kahneman, Slovic, & Tversky, 1982; Tversky & Simonson, 1993). Similarity A B Z Z Z Attr 1 C Attr 2 Attr 2 A Attr 2 Humans exhibit certain systematic context-dependent preference reversals when choosing among options that vary along multiple attribute dimensions. For instance, the attraction, similarity, and compromise effects each involves a change in relative preference between two op- tions when a third option is introduced. Previously, such effects have been attributed to irrationality or sub- optimality in decision-making, or to specific architec- tural or dynamical constraints on cognition. We use a Bayesian model of multi-attribute choice to demon- strate that these effects naturally arise from three ba- sic assumptions: (1) humans assess options relative to “fair market value” as inferred from prior experience and available options; (2) attributes are imperfectly substi- tutable, and scarce attributes are relatively more valu- able; (3) uncertainty about market conditions and op- tion values contributes to stochasticity in choice behav- ior. This work provides both a novel normative explana- tion for contextual modulation of choice behavior, and a means to predict choice as a function of past experiences and novel contexts. Attr 1 Attr 1 Figure 1: Three classical contextual effects in multi- attribute choice: (A) attraction effect, (B) compromise effect, (C) similarity effect. A and B are two equally preferable choices that differ in two attribute dimensions. The introduction of a third option Z induces a prefer- ence shift between A and B (indicated by arrows). Solid and dashed lines illustrate model-inferred “fair value” indifference curve before and after introducing Z. Two broad classes of models have previously been proposed for contextual effects in multi-attribute choice behavior: (1) normative models (Marr, 1982) that are built on behavioral constraints/goals and delineated in terms of internal beliefs and assumptions (Luce, 1959; Thurstone, 1954; Luce, 1965; Tversky, 1972; Tversky & Simonson, 1993); (2) algorithmic or implementational models that explain behavioral phenomena as arising from specific architectural and dynamical constraints on neural processing (Busemeyer & Townsend, 1993; Usher & McClelland, 2004; J. S. Trueblood, 2012). The first class of models are related to bounded ra- tionality (Simon, 1955), but have so far been unable to explain all three contextual effects, leading to sugges-