Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning about Drug Quality
Speaker
Abstract
| Experimental and survey-based research suggests that consumers often rely on their intuition and cognitive shortcuts to make decisions. Intuition and cognitive shortcuts can lead to suboptimal decisions and, especially in high-stakes decisions to legitimate welfare concerns. In this paper, we propose an extension of a Bayesian learning model that allows us to quantify the impact of salience – the fact that some pieces of information are easier to retrieve from memory than others – on physician learning. We show, using data on actual prescriptions for real patients, that physicians’ belief formation is strongly influenced by salience effects. Feedback from switching patients – the ones the physician decided to switch to a clinically equivalent treatment – receives considerably more weight than feedback from other patients. In the category we study, salience effects slowed down physicians’ speed of learning and the adoption of a new treatment, which raises welfare concerns. For managers, our findings suggest that firms that are able to eliminate, or at least reduce, salience effects to a greater extent than their competitors can speed-up the adoption of new treatments. We explore the implications of these results and suggest alternative applications of our model that are relevant for policy makers and managers. |
| Contact information: |
| Dr. G. Liberali |
