Abstract
The aim of the rational-consensus method is to produce “rational consensus”,
that is, “mathematical aggregation”, by weighing the performance of each
expert on the basis of his or her knowledge and ability to judge relevant uncertainties. The measurement of the performance of the experts is based on the expert’s assessment of “seed variables”. These performances are used to determine the weights of the expert’s judgments in the aggregation of them. The disadvantage of the rational-consensus method in social science is the lack of agreed upon seed variables, and that it does not instead use the shared knowledge captured by models. Moreover, there seems to be sufficient evidence that combining models with expert judgments leads to better judgments. This is even more evident with respect to forecasts.
that is, “mathematical aggregation”, by weighing the performance of each
expert on the basis of his or her knowledge and ability to judge relevant uncertainties. The measurement of the performance of the experts is based on the expert’s assessment of “seed variables”. These performances are used to determine the weights of the expert’s judgments in the aggregation of them. The disadvantage of the rational-consensus method in social science is the lack of agreed upon seed variables, and that it does not instead use the shared knowledge captured by models. Moreover, there seems to be sufficient evidence that combining models with expert judgments leads to better judgments. This is even more evident with respect to forecasts.
| Original language | English |
|---|---|
| Title of host publication | Experts and Consensus in Social Science |
| Place of Publication | Cham |
| Publisher | Springer |
| Pages | 49-69 |
| Publication status | Published - 2014 |
| Externally published | Yes |