A theory of information search in multi-attribute decisions

Humans are confronted with a complex world, in which many choice situations are characterized by a large number of options described on multiple attributes. To meet this challenge, they must find a suitable trade-off between making informed decisions on the one hand and limiting invested resources such as time and effort on the other hand. We argue that humans achieve this balance by searching systematically for relevant information in an efficient and goal-directed, but not strictly optimal manner. More specifically, we propose a Bayesian cognitive model of information search in multi-attribute decisions. According to this model, the values of different attributes and options are represented as belief distributions that are updated by sampling information through the allocation of selective attention. A decision is made when the belief distribution of the currently best option is sufficiently higher than the distribution of all other options. The core element of our model is a myopic transition rule, according to which people plan one step ahead and allocate attention to an option’s attribute that is most likely to reveal decisive information in favor of the associated option. As an emergent property of this transition rule, our model predicts that information search is driven by three factors: the weights of attributes, the uncertainty about attribute values, and the accumulated value of options. Simulations of the model demonstrate that our theory accounts for a rich body of empirical findings on attention-choice interactions in both binary and multi-alternative decisions. For example, the model predicts i) the positive correlation between attention to an option and choice probability, ii) the attraction search effect, according to which people are more likely to keep attending to initially promising choice candidates, and iii) the negative correlation of the Payne Index (which quantifies alternative- vs. attribute-wise search) with the dispersion of attribute weights. Taken together, our computational theory offers a unifying description of information search and choice dynamics in multi-attribute decisions and suggests that humans search in an adaptive and efficient but still not strictly optimal way.