Robert G. Farrell, Catalina M. Danis, et al.
RecSys 2012
Software is playing an increasingly important role in supporting human decision-making. Previous HCI research on decision support systems (DSS) has improved the information visualization aspect of DSS information design, but has somewhat overlooked the cognitive aspect of decision-making, namely that human reasoning is heuristic and reflects systematic errors or cognitive biases. We report on an empirical study of two cognitive biases: Conservatism and loss aversion. Two remediation techniques recommended by previous research were tested: The expected return method, an actuarial-inspired approach presenting objective metrics; and bootstrapping, a technique successful in improving judgment consistency. The results show that the two biases can occur simultaneously and can have a huge impact on decision-making. The results also show that the two debiasing techniques are only partly effective. These findings suggest a need for more research on debiasing, and indicate some directions for exploring debiasing techniques and building decision support systems.
Robert G. Farrell, Catalina M. Danis, et al.
RecSys 2012
Yang Wang, Liang Gou, et al.
CHI 2015
Rachel Bellamy, Sean Andrist, et al.
CHI EA 2017
Catalina Danis, Wendy A. Kellogg, et al.
CHI EA 2005