Overprecision—an excessive confidence that one knows the truth—is both the most durable and the least understood form of overconfidence. This article outlines an approach to the study of overprecision that avoids some of the methodological problems of other approaches and better reflects the way uncertainty affects choices in everyday life. We measured the precision in judgment implied by people’s tendency to adjust their point estimates of an uncertain quantity in response to the costs of overestimating or underestimating the correct answer. The results revealed robust overprecision. People adjusted their estimates less than they should have given their actual knowledge, and this effect was driven by their subjective confidence.
ArkesH. R.ChristensenC.LaiC.BlumerC. (1987). Two methods of reducing overconfidence. Organizational Behavior and Human Decision Processes, 39, 133–144. doi:10.1016/0749-5978(87)90049-5
2.
BlockR. A.HarperD. R. (1991). Overconfidence in estimation: Testing the anchoring-and-adjustment hypothesis. Organizational Behavior and Human Decision Processes, 49, 188–207. doi:10.1016/0749-5978(91)90048-x
3.
BolgerF.HarveyN. (1995). Judging the probability that the next point in an observed time-series will be below, or above, a given value. Journal of Forecasting, 14, 597–607. doi:10.1002/for.3980140705
4.
Christensen-SzalanskiJ. J.BushyheadJ. B. (1981). Physicians’ use of probabilistic information in a real clinical setting. Journal of Experimental Psychology: Human Perception and Performance, 7, 928–935.
5.
CosmidesL.ToobyJ. (1996). Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty. Cognition, 58, 1–73.
6.
DanielK. D.HirshleiferD.SubrahmanyamA. (2001). Overconfidence, arbitrage, and equilibrium asset pricing. Journal of Finance, 56, 921–965.
7.
DawesR. M.MulfordM. (1996). The false consensus effect and overconfidence: Flaws in judgment or flaws in how we study judgment?Organizational Behavior and Human Decision Processes, 65, 201–211.
8.
De BondtW. F. M.ThalerR. H. (1995). Financial decision-making in markets and firms: A behavioral perspective. In JarrowR.MaksimovicV.ZiembaW. T. (Eds.), Handbooks in operations research and management science (Vol. 9, pp. 385–410). Amsterdam, The Netherlands: Elsevier.
9.
GigerenzerG. (1991). How to make cognitive illusions disappear: Beyond “heuristics and biases.” In StroebeW.HewstoneM. (Eds.), European review of social psychology (Vol. 2, pp. 83–115). Chichester, England: Wiley.
10.
GoodwinP. (2005). Providing support for decisions based on time series information under conditions of asymmetric loss. European Journal of Operational Research, 163, 388–402. doi:10.1016/j.ejor.2003.10.039
11.
GrangerC. W. J. (1969). Prediction with a generalized cost of error function. Operational Research Quarterly, 20, 199–207.
12.
HaranU.MooreD. A.MorewedgeC. K. (2010). A simple remedy for overprecision in judgment. Judgment and Decision Making, 5, 467–476.
13.
HarveyN. (1997). Confidence in judgment. Trends in Cognitive Sciences, 1, 78–82. doi:10.1016/s1364-6613(97)01014-0
14.
HarveyN.FischerI. (1997). Taking advice: Accepting help, improving judgment, and sharing responsibility. Organizational Behavior and Human Decision Processes, 70, 117–133.
15.
KahnemanD. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus, and Giroux.
16.
LawrenceM.O’ConnorM. (2005). Judgmental forecasting in the presence of loss functions. International Journal of Forecasting, 21, 3–14. doi:10.1016/j.ijforecast.2004.02.003
17.
MamassianP. (2008). Overconfidence in an objective anticipatory motor task. Psychological Science, 19, 601–606. doi:10.1111/j.1467-9280.2008.02129.x
18.
McKenzieC. R. M.LierschM. J.YanivI. (2008). Overconfidence in interval estimates: What does expertise buy you?Organizational Behavior and Human Deci-sion Processes, 107, 179–191. doi:10.1016/j.obhdp.2008.02.007
19.
MooreD. A.HealyP. J. (2008). The trouble with overconfidence. Psychological Review, 115, 502–517. doi:10.1037/0033-295X.115.2.502
20.
RussoJ. E.SchoemakerP. J. H. (1992). Managing overconfidence. Sloan Management Review, 33, 7–17.
21.
SollJ. B.KlaymanJ. (2004). Overconfidence in interval estimates. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 299–314.
22.
StoneE. R.OpelR. B. (2000). Training to improve calibration and discrimination: The effects of performance and environmental feedback. Organizational Behavior and Human Decision Processes, 83, 282–309. doi:10.1006/obhd.2000.2910
23.
TverskyA.KahnemanD. (1993). Probabilistic reasoning. In GoldmanA. I. (Ed.), Readings in philosophy and cognitive science (pp. 43–68). Cambridge, MA: MIT Press.
24.
WeberE. U. (1994). From subjective probabilities to decision weights: The effect of asymmetric loss functions on the evaluation of uncertain outcomes and events. Psychological Bulletin, 115, 228–242. doi:10.1037/0033-2909.115.2.228
25.
WinmanA.HanssonP.JuslinP. (2004). Subjective probability intervals: How to reduce overconfidence by interval evaluation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 1167–1175. doi:10.1037/0278-7393.30.6.1167
