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.relation.lastpage 288 -
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dc.relation.volume 141 -
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 -
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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.contributor.surname Apreda -
isi.contributor.surname Bonaccorsi -
isi.contributor.surname dell'Orletta -
isi.contributor.surname Fantoni -
isi.date.issued 2019 *
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.date.issued 2019 *
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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scopus.title Expert forecast and realized outcomes in technology foresight *
scopus.titleeng Expert forecast and realized outcomes in technology foresight *
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