Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/105309
Type: Conference paper
Title: Predicting overprecision in range estimation
Author: Matthew Kaesler, P.
Matthew Welsh, B.
Semmler, C.
Citation: Proceedings of the 38th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society, 2016 / Papafragou, A., Grodner, D., Mirman, D., Trueswell, J. (ed./s), vol.B, pp.502-507
Publisher: Cognitive Science Society
Publisher Place: Austin, TX
Issue Date: 2016
ISBN: 9780991196739
Conference Name: The 38th Annual Meeting of the Cognitive Science Society (COGSCI) (10 Aug 2016 - 13 Aug 2016 : Philadelphia, Pennsylvania)
Statement of
Responsibility: 
Matthew Kaesler, Matthew B. Welsh, Carolyn Semmler
Abstract: Overprecision (overconfidence in interval estimation) is a bias with clear implications for economic outcomes in industries reliant on forecasting possible ranges for future prices and unknown states of nature - such as mineral and petroleum exploration. Prior research has shown the ranges people provide are too narrow given the knowledge they have – that is, they underestimate uncertainty and are overconfident in their knowledge. The underlying causes of this bias are, however, still unclear and individual differences research has shed little light on traits predictive of susceptibility. Taking this as a starting point, this paper directly contrasts the Naïve Sampling Model and Informativeness-Accuracy Tradeoff accounts of overprecision – seeing which better predicts performance in an interval estimation task. This was achieved by identifying traits associated with these theories – Short Term Memory and Need for Cognitive Closure, respectively. Analyses indicate that NFCC but not STM predicts interval width and thus, potentially, impacts overprecision.
Keywords: Confidence; overprecision; need for cognitive closure; STM; informativeness; naïve sampling model.
Description: Theme for 2016: Recognizing and representing events
Rights: © the authors
RMID: 0030059637
Published version: https://mindmodeling.org/cogsci2016/papers/0098/index.html
Appears in Collections:Australian School of Petroleum publications

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