Unskilled and optimistic: Overconfident predictions despite calibrated knowledge of relative skill

Psychon Bull Rev (2013) 20:601–607 DOI 10.3758/s13423-013-0379-2 BRIEF REPORT Unskilled and optimistic: Overconfident predictions despite calibrated knowledge of relative skill Daniel J. Simons Published online: 24 January 2013 # Psychonomic Society, Inc. 2013 Abstract Those who are less skilled tend to overestimate their abilities more than do those who are more skilled—the so-called Dunning–Kruger effect. Less-skilled performers presumably have less of the knowledge needed to make informed guesses about their relative performance. If so, the Dunning–Kruger effect should vanish when participants do have access to information about their relative ability and performance. Competitive bridge players predicted their results for bridge sessions before playing and received feedback about their actual performance following each session. Despite knowing their own relative skill and showing unbiased memory for their performance, they made overconfident predictions consistent with a Dunning–Kruger effect. This bias persisted even though players received accurate feedback about their predictions after each session. The finding of a Dunning–Kruger effect despite knowledge of relative ability suggests that differential self-knowledge is not a necessary precondition for the Dunning–Kruger effect. At least in some cases, the effect might reflect a different form of irrational optimism. Keywords Social cognition . Decision making . Belief updating . Overconfidence In domains ranging from driving to intelligence to sense of humor, well over 50 % of people believe themselves to be above-median performers (see Dunning, Heath & Suls, 2004). Moreover, those with less skill tend to overestimate their abilities more than do those with more skill—the so-called Dunning–Kruger effect (Kruger & Dunning, 1999). Why are D. J. Simons (*) Department of Psychology, University of Illinois, 603 E. Daniel Street, Champaign, IL 61820, USA e-mail: people so poorly calibrated in their self-assessments, and why is this bias greater for poor performers? The traditional account (Kruger & Dunning, 1999) attributes the difference to metacognition: Due to gaps or distortions in their knowledge base, the unskilled inaccurately assess their own relative abilities. Because they tend not to realize how unskilled they actually are, they inflate their estimates of their own abilities. In contrast, skilled performers can better assess their own skill, so they are less overconfident in their relative self-assessments. Other factors, including regression to the mean (e.g., Krueger & Mueller, 2002) and task difficulty (e.g., Burson, Larrick & Klayman, 2006), can contribute to differences between good and poor performers, but the tendency for people as a group, and for the unskilled in particular, to exaggerate their own abilities persists (Dunning, 2011; Ehrlinger, Johnson, Banner, Dunning & Kruger, 2008). In the vast majority of studies documenting the Dunning– Kruger effect, participants have judged how well they were performing without an objective metric of their own performance or of that of their peers (for an exception, see Park & Santos-Pinto, 2010). The use of subjective ability domains (e.g., sense of humor; Kruger & Dunning, 1999) and the lack of information about relative standings might make these domains more subject to biases in self-assessment. Even for more objective domains (e.g., math ability), participants often lack access to information about themselves or their peers when making their relative judgments. In most studies, participants rate their performance on a single, just-completed measure, typically without feedback about relative performance. To my knowledge, only one published study has explored predictions about future performance by people who are fully aware of their own skill level (Park & Santos-Pinto, 2010). In that study, chess players (and poker players) predicted their final scores in a tournament before play began, and their expected results 602 exceeded the actual ones. Moreover, weaker players were more overconfident than better players (see also Chabris & Simons, 2010). Studies of subjective judgments in the absence of feedback conflate several reasons why people might overestimate their abilities. Accurate self-assessments require both memory/knowledge of their own skill and knowledge of the skill levels in the comparison group. Should either of these forms of information be lacking, those with a positive selfbias will tend to give inflated self-assessments. For example, people might be biased to remember more of their own successes than failures, even if they were calibrated in their memory for the successes and failures of their peers (see Helzer & Dunning, 2012, for a related argument). Alternatively, someone who accurately represents their own skill level might underestimate those of their peers. Even if people are perfectly calibrated in their knowledge of their own performance and of that of their peers, they might still be overconfident in evaluating their recent performance or in predicting their future performance: They might predict that they will “do better this time.” If levels of optimism varied with skill levels, differential selfknowledge would not be a necessary precondition for a Dunning–Kruger effect. Some forms of optimistic forecasting appear resistant to feedback. For example, football fans show persistent overconfidence when predicting their favored team’s outcome each week, despite receiving feedback about the accuracy of their predictions each time the team wins or loses (Massey, Simmons & Armor, 2011). Moreover, people tend to rely more on their aspirations than on their past performance when predicting their own future performance (e.g., Helzer & Dunning, 2012). Determining whether the Dunning–Kruger effect emerges only when the less skilled have relatively impoverished knowledge requires a task that would be subject to all of these possible sources of overconfidence. Ideally, it should be a task in which participants generate multiple judgments, so that it would be possible to separate distorted memory for past experiences from distorted assessments of current or future performance. If biased selfassessments arise from poor calibration, then providing people with performance feedback should lead to better calibration and less-biased self-assessments. Thus, a reduction in the relationship between skill and overconfidence following feedback would be consistent with the differential-self-knowledge explanation. However, if the bias results from overly optimistic predictions (Massey et al., 2011), it should persist even when people do know their own skill level; they would consistently expect to “do better this time” than they have done on average. To distinguish among these alternatives, I explored predictions made by competitive bridge players in a local Psychon Bull Rev (2013) 20:601–607 duplicate bridge club over a period of 2 months. The “m (...truncated)