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The wisdom-of-crowds effect describes how aggregating judgments of multiple individuals can lead to a more accurate judgment than that of the typical—or even best—individual. However, what if there are no other individuals’ judgments at one’s disposal? We investigated when an individual can avail theirself of the wisdom of their “inner crowd” to improve the quality of their confidence judgments by either (a) averaging their two confidence judgments or (b) selecting the higher of the two (i.e., maximizing) in two-alternative choice tasks. In a simulation analysis based on a signal detection model of confidence, we investigated how the “kindness” versus “wickedness” of items (i.e., the degree to which the majority of people chooses the correct or wrong answer) affect the performance of averaging and maximizing. Simulation and analytical results show that irrespective of the type of item, averaging consistently improves confidence judgments, but maximizing is risky: It outperformed averaging only once items were answered correctly 60% of the time or more—a result, which has not been established in prior work. We investigated the relevance of these effects in three empirical datasets since a person’s actual confidence judgments are redundant (median correlations ranged between .5 and .85). Averaging two confidence judgments from the same person was superior to maximizing, with Cohen’s d’s effect sizes ranging from 0.67–1.44. As people typically have no insight about the wickedness of the individual item, our results suggest that averaging—due to its robustness—should be the default strategy to harness one’s conflicting confidence judgments.

How the “wisdom of the inner crowd” can boost accuracy of confidence judgments.