Contrary to what happens in forecasting, in which the repetitive nature of events lends itself to the ex post validation of expert judgments, it is usually very difficult to compare directly the forecast of technology foresight studies with realized outcomes. When the comparison is feasible, therefore, there is large opportunity for learning and methodological refinement. The authors of this study had the opportunity to re-examine the findings of a technology foresight exercise on the medical device industry with realized technological performance, five years later. Among the findings of the comparison exercise, intriguing false positive as well as false negative cases have been identified. The paper suggests that these cases are due to specific cognitive and motivational biases of experts and examines the way in which they are at work in the foresight process. It argues that these biases are due to the inability of experts to reason systematically in abstract (or "functional") terms during the whole foresight process. It also suggests a methodology to mitigate the biases and to manage the emergence of false positives and false negatives.
Expert forecast and realized outcomes in technology foresight
dell'Orletta Felice;
2019
Abstract
Contrary to what happens in forecasting, in which the repetitive nature of events lends itself to the ex post validation of expert judgments, it is usually very difficult to compare directly the forecast of technology foresight studies with realized outcomes. When the comparison is feasible, therefore, there is large opportunity for learning and methodological refinement. The authors of this study had the opportunity to re-examine the findings of a technology foresight exercise on the medical device industry with realized technological performance, five years later. Among the findings of the comparison exercise, intriguing false positive as well as false negative cases have been identified. The paper suggests that these cases are due to specific cognitive and motivational biases of experts and examines the way in which they are at work in the foresight process. It argues that these biases are due to the inability of experts to reason systematically in abstract (or "functional") terms during the whole foresight process. It also suggests a methodology to mitigate the biases and to manage the emergence of false positives and false negatives.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.ancejournal | TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE | - |
| dc.authority.orgunit | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | - |
| dc.authority.people | Apreda Riccardo | it |
| dc.authority.people | Bonaccorsi Andrea | it |
| dc.authority.people | dell'Orletta Felice | it |
| dc.authority.people | Fantoni Gualtiero | it |
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| dc.date.accessioned | 2024/02/20 16:27:12 | - |
| dc.date.available | 2024/02/20 16:27:12 | - |
| dc.date.issued | 2019 | - |
| dc.description.abstracteng | Contrary to what happens in forecasting, in which the repetitive nature of events lends itself to the ex post validation of expert judgments, it is usually very difficult to compare directly the forecast of technology foresight studies with realized outcomes. When the comparison is feasible, therefore, there is large opportunity for learning and methodological refinement. The authors of this study had the opportunity to re-examine the findings of a technology foresight exercise on the medical device industry with realized technological performance, five years later. Among the findings of the comparison exercise, intriguing false positive as well as false negative cases have been identified. The paper suggests that these cases are due to specific cognitive and motivational biases of experts and examines the way in which they are at work in the foresight process. It argues that these biases are due to the inability of experts to reason systematically in abstract (or "functional") terms during the whole foresight process. It also suggests a methodology to mitigate the biases and to manage the emergence of false positives and false negatives. | - |
| dc.description.affiliations | Erre Quadro Srl; Università di Pisa; ILC-CNR; FBK | - |
| dc.description.allpeople | Apreda, Riccardo; Bonaccorsi, Andrea; Dell'Orletta, Felice; Fantoni, Gualtiero | - |
| dc.description.allpeopleoriginal | Apreda, Riccardo; Bonaccorsi, Andrea; dell'Orletta, Felice; Fantoni, Gualtiero | - |
| dc.description.fulltext | none | en |
| dc.description.numberofauthors | 4 | - |
| dc.identifier.doi | 10.1016/j.techfore.2018.12.006 | - |
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| dc.subject.keywords | Expert forecast | - |
| dc.subject.keywords | Medical device industry | - |
| dc.subject.keywords | Cognitive biases | - |
| dc.subject.keywords | Abstract reasoning | - |
| dc.subject.keywords | Failure mode analysis | - |
| dc.subject.keywords | Functional analysis | - |
| dc.subject.singlekeyword | Expert forecast | * |
| dc.subject.singlekeyword | Medical device industry | * |
| dc.subject.singlekeyword | Cognitive biases | * |
| dc.subject.singlekeyword | Abstract reasoning | * |
| dc.subject.singlekeyword | Failure mode analysis | * |
| dc.subject.singlekeyword | Functional analysis | * |
| dc.title | Expert forecast and realized outcomes in technology foresight | en |
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| isi.description.abstracteng | Contrary to what happens in forecasting, in which the repetitive nature of events lends itself to the ex post validation of expert judgments, it is usually very difficult to compare directly the forecast of technology foresight studies with realized outcomes. When the comparison is feasible, therefore, there is large opportunity for learning and methodological refinement. The authors of this study had the opportunity to re-examine the findings of a technology foresight exercise on the medical device industry with realized technological performance, five years later. Among the findings of the comparison exercise, intriguing false positive as well as false negative cases have been identified. The paper suggests that these cases are due to specific cognitive and motivational biases of experts and examines the way in which they are at work in the foresight process. It argues that these biases are due to the inability of experts to reason systematically in abstract (or "functional") terms during the whole foresight process. It also suggests a methodology to mitigate the biases and to manage the emergence of false positives and false negatives. | * |
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| scopus.description.abstracteng | Contrary to what happens in forecasting, in which the repetitive nature of events lends itself to the ex post validation of expert judgments, it is usually very difficult to compare directly the forecast of technology foresight studies with realized outcomes. When the comparison is feasible, therefore, there is large opportunity for learning and methodological refinement. The authors of this study had the opportunity to re-examine the findings of a technology foresight exercise on the medical device industry with realized technological performance, five years later. Among the findings of the comparison exercise, intriguing false positive as well as false negative cases have been identified. The paper suggests that these cases are due to specific cognitive and motivational biases of experts and examines the way in which they are at work in the foresight process. It argues that these biases are due to the inability of experts to reason systematically in abstract (or “functional”) terms during the whole foresight process. It also suggests a methodology to mitigate the biases and to manage the emergence of false positives and false negatives. | * |
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| Appare nelle tipologie: | 01.01 Articolo in rivista | |
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