Bayesian Advice Taking: Adaptive Strategy Selection in Sequential Advice Seeking

In sampling approaches to advice taking, participants can sequentially sample multiple pieces of advice before making a final judgment. To contribute to the understanding of active advice seeking, we develop and compare different strategies for information integration from external sources, including Bayesian belief updating. In a reanalysis of empirical data, we find that participants most frequently compromise between their initial beliefs and the distributions of multiple pieces of advice sampled from others. Moreover, across all participants, compromising predicts their final beliefs better than choosing one of the two sources of information. However, participants’ willingness to integrate external opinions is relatively higher for multiple pieces of reasonably distant as compared to close advice. Nevertheless, egocentrism is as pronounced as in the traditional paradigm where only a single piece of external evidence is provided. Crucially, there are large inter- and intra-individual differences in strategy selection for sequential advice taking. On the one hand, some participants choose their own or others’ judgments more often, and other participants are better described as compromisers between internal and external sources of information. On the other hand, virtually all participants apply different advice taking strategies for different items and trials. Our findings constitute initial evidence of the adaptive utilization of multiple, sequentially sampled external opinions.