Designing Information for Remediating Cognitive Biases in Decision-Making

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. Author Keywords decision making; decision support system; intelligent assistance; multiple-cue probability learning; cognitive bias; conservatism; loss aversio