Do we measure overconfidence? A closer look at the interval production task

Langnickel, Ferdinand; Zeisberger, Stefan

doi:10.1016/j.jebo.2016.04.019

Cited by 24 publications

(12 citation statements)

References 38 publications

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“…Unfortunately, people’s intuitions about ranges seem unrelated to the degree of confidence. People typically produce identical intervals regardless of the assigned level of confidence (Langnickel and Zeisberger, 2016; Teigen and Filkuková, 2013), oblivious to the fact that a 90% confidence interval must be considerably larger (perhaps twice as large) than a 60% interval. When asked in retrospect how confident they are in their range estimates, people often report a lower degree of confidence even if they initially were required to be 90% sure.…”

Section: Conceptual and Empirical Backgroundmentioning

confidence: 99%

Entirely possible overruns: how people think and talk about probabilistic cost estimates

Teigen

Andersen

Alnes

et al. 2019

IJMPB

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Purpose The purpose of this paper is to examine people’s understanding and evaluation of uncertainty intervals produced by experts as part of a quality assurance procedure of large public projects. Design/methodology/approach Three samples of educated participants (employees in a large construction company, students attending courses in project management and judgment and decision making, and judges of district and appeal courts) answered questionnaires about cost estimates of a highway construction project, presented as a probability distribution. Findings The studies demonstrated additivity neglect of probabilities that are graphically displayed. People’s evaluations of the accuracy of interval estimates revealed a boundary (a “cliff”) effect, with a sharp drop in accuracy ratings for outcomes above an arbitrary maximum. Several common verbal phrases (what “can” happen, is “entirely possible” and “not surprising”) which might seem to indicate expected outcomes were regularly used to describe unlikely values near or at the top of the distribution (an extremity effect). Research limitations/implications All judgments concerned a single case and were made by participants who were not stakeholders in this specific project. Further studies should compare judgments aided by a graph with conditions where the graph is changed or absent. Practical implications Experts and project managers cannot assume that readers of cost estimates understand a well-defined uncertainty interval as intended. They should also be aware of effects created by describing uncertain estimates in words. Originality/value The studies show how inconsistencies in judgment affect the understanding and evaluation of uncertainty intervals by well-informed and educated samples tested in a maximally transparent situation. Readers of cost estimates seem to believe that precise estimates are feasible and yet that costs are usually underestimated.

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Section: Conceptual and Empirical Backgroundmentioning

confidence: 99%

Entirely possible overruns: how people think and talk about probabilistic cost estimates

Teigen

Andersen

Alnes

et al. 2019

IJMPB

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“…In within‐subjects designs where the same participants are asked to create 90% and 50% intervals for the same question, they seem to realize that the 90% interval must be wider, although not to the extent dictated by a normal probability distribution (Budescu & Du, ). When different groups are instructed to estimate 90 and 50% intervals, they do not appear to be sensitive to the assigned confidence level and produce intervals of the same width (Langnickel & Zeisberger, ; Teigen & Jørgensen, ), leading to overprecise 90% intervals but more well‐calibrated 50% intervals. Thus, overprecision can in part be attributed to a deficient understanding of probability distributions (Moore, Tenney, & Haran, ).…”

Section: Overprecision and The Preciseness Paradoxmentioning

confidence: 99%

Probabilities Associated with Precise and Vague Forecasts

Løhre

Teigen

2017

Behavioral Decision Making

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Forecasts of future outcomes, such as the consequences of climate change, are given with different degrees of precision. Logically, more precise forecasts (e.g., a temperature increase of 3-4°) have a smaller probability of capturing the actual outcome than less precise forecasts (e.g., a temperature increase of 2-6°). Nevertheless, people often trust precise forecasts more than vague forecasts, perhaps because precision is associated with knowledge and expertise. In five experiments, we ask whether people expect highly confident forecasts to be associated with wider or narrower outcome ranges than less confident forecasts (Experiments 1, 2, and 5), and, conversely, whether they expect precise forecasts to be issued with higher or lower confidence than vague forecasts (Experiments 3 and 4). The results revealed two distinct ways of thinking about confidence intervals, labeled distributional (wide intervals seen as more probable than narrow intervals) and associative (wide intervals seen as more uncertain than narrow intervals). Distributional responses occurred somewhat more often in within-subjects designs, where wide and narrow prediction intervals and high and low probability estimates can be directly compared, whereas separate evaluations (in between-subjects design) suggested associative responses to be slightly more frequent. These findings are relevant for experts communicating forecasts through confidence intervals.

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“…Subjects are instructed to state intervals such that their own confidence is between these stated bounds, subject to a confidence level that is requested by the experimenter (Langnickel & Zeisberger, 2016).…”

Section: Intervals Of Confidencementioning

confidence: 99%

Overconfidence and disposition effect in the stock market: A micro world based setting

Trejos

Deemen

Rodríguez

et al. 2019

Journal of Behavioral and Experimental Finance

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Modern financial theory relies on the rationality assumption of investors even though, evidence suggests that market investors are affected by behavioural biases such as overconfidence and disposition effect. Overconfident investors perceive situations better than what they actually are, while investors exhibiting disposition effect tend to dispose winner shares and keep loser ones. However there is not clear causal relationship between both biases. We contribute to the literature about overconfidence and its relationship with the disposition effect, using a simulation model often named micro world,representing an artificial financial stock market.Wepropose a methodology combining qualitative (QCA) and quantitative(Logistic Regression) techniques to correlate transactions' outcomes with investors' characteristics. Results suggest that overconfidence is explained by gender, career and education level, while age, nationality, and profits are not significant variables.We also confirm that investors exhibiting disposition effect are more prone to be overconfident.

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Do we measure overconfidence? A closer look at the interval production task

Cited by 24 publications

References 38 publications

Entirely possible overruns: how people think and talk about probabilistic cost estimates

Entirely possible overruns: how people think and talk about probabilistic cost estimates

Probabilities Associated with Precise and Vague Forecasts

Overconfidence and disposition effect in the stock market: A micro world based setting

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