Seeking advice: A sampling approach to advice taking

Abstract

AbstractThe present research addresses advice taking from a holistic perspective covering both advice seeking and weighting. We build on previous theorizing that assumes that underweighting of advice results from biased samples of information. That is, decision makers have more knowledge supporting their own judgment than that of another person and thus weight the former stronger than the latter. In the present approach, we assume that participants reduce this informational asymmetry by the sampling of advice and that sampling frequency depends on the information ecology. Advice that is distant from the decision maker’s initial estimate should lead to a higher frequency of advice sampling than close advice. Moreover, we assume that advice distant from the decision maker’s initial estimate and advice that is supported by larger samples of advisory estimates are weighted more strongly in the final judgment. We expand the classical research paradigm with a sampling phase that allows participants to sample any number of advisory estimates before revising their judgments. Three experiments strongly support these hypotheses, thereby advancing our understanding of advice taking as an adaptive process.