Is optimal recommendation the best? A laboratory investigation under the newsvendor problem
Authors: Feng, Xiaojing; Gao, Jia
Journal: Decision Support Systems (2020)
DOI: 10.1016/j.dss.2020.113251
We investigate the impacts of the decision support system's recommendations on decision makers' psychology and decision behaviors under uncertain contexts where optimal solutions exist. As a representative of such contexts, the newsvendor problem is studied by using the method of laboratory experiments. Through providing an elaborately designed decision support system in Experiment I, we validate that the optimal recommendations help to alleviate human newsvendors' Pull-to-Center bias, i.e., the actual orders fall in the range between mean demand and optimal order that maximizes the expected profit theoretically, and decrease the bias asymmetry under two profit conditions (high or low). We also reveal that optimal recommendations can't eliminate the bias as decision makers exhibit two competing psychological factors simultaneously when using the decision support system: algorithm aversion and regret aversion. Algorithm aversion persistently impedes them from following the superior recommendations, while regret aversion sometimes pulls them to approach to the recommendations driven by the feeling of experienced regret. Further, we redesign the decision support system in Experiment…