Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2702123.2702239 | CHI |
Keywords | Field | DocType |
loss aversion,user/machine systems,decision making,intelligent assistance,conservatism,decision support system,multiple-cue probability learning,cognitive bias | Data science,Cognitive bias,Debiasing,Heuristic,Information visualization,Computer science,Decision support system,Human–computer interaction,Artificial intelligence,Cognition,Empirical research,Information design | Conference |
Citations | PageRank | References |
7 | 0.44 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunfeng Zhang | 1 | 54 | 19.28 |
| Rachel K. E. Bellamy | 2 | 323 | 70.86 |
| Wendy A. Kellogg | 3 | 1636 | 264.27 |