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

Andrea Bonaccorsi;
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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/320005
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