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Hindsight is not equal to foresight: The effect of outcome knowledge on judgment under uncertainty

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Abstract

Notes that a major difference between historical and nonhistorical judgment is that the historical judge typically knows how things turned out. 3 experiments are described with a total of 479 college students. In Exp I, receipt of such outcome knowledge was found to increase the postdicted likelihood of reported events and change the perceived relevance of event-descriptive data, regardless of the likelihood of the outcome and the truth of the report. Ss were, however, largely unaware of the effect that outcome knowledge had on their perceptions. As a result, they overestimated what they would have known without outcome knowledge (Exp II), as well as what others (Exp III) actually did know without outcome knowledge. It is argued that this lack of awareness can seriously restrict one's ability to judge or learn from the past. (16 ref)
... First, when asked to recall their original estimates, people's responses tend to be closer to the actual value than their original estimates were. This well-established phenomenon is known as hindsight bias (e.g., Christensen-Szalanski & Willham, 1991;Groß & Pachur, 2019;Hawkins & Hastie, 1990), and was initially introduced by Fischhoff (1975). It has been shown for estimates of realworld quantities (e.g., lengths of rivers, heights of towers; see Bayen et al., 2006;Bernstein et al., 2011;Erdfelder & Buchner, 1998;Groß & Bayen, 2015;Pohl & Hell, 1996), but also for two-alternative-choice tasks, judgments of event outcomes (e.g., historical and medical), and other tasks and materials (see Pohl, 2007, for an overview). ...
... In this article, we focused on hindsight bias in the context of realworld quantitative estimation, where metric knowledge plays an important role for judgment performance and can be updated as a result of exposure to numerical facts. However, hindsight bias has also been observed in domains and tasks where metric knowledge plays no central role, for instance, when judging event probabilities (e.g., Fischhoff, 1975), or identifying visual stimuli (e.g., Giroux et al., 2022). Does this mean that knowledge updating can be expected to be less relevant in these other contexts? ...
... Historically, it has been argued that hindsight bias-reflecting a lack of awareness of one's limited prior knowledge-restricts people's ability to learn (Fischhoff, 1975). Our research shows that, on ...
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... Investigations of outcome bias (building on Fischhoff, 1975) cover a wide range of fields including not only financial, but also ethical, judicial or medical decisions (e.g., Arkes et al., 1981;Kamin & Rachlinski, 1995;Gino et al., 2010). Hershey, 1988, 1992) were the first to conceptualize the effects of outcomes on evaluations. ...
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... This can be problematic because humans show cognitive biases that can lead to erroneous inferences (Tversky & Kahneman, 1974). For example, humans see coherent patterns in randomness (Brugger, 2001), convince themselves of the validity of prior expectations ("I knew it"; Nickerson, 1998), and perceive events as being plausible in hindsight ("I knew it all along"; Fischhoff, 1975). In conjunction with an academic incentive system that rewards certain discovery processes more than others (Koole & Lakens, 2012;Sterling, 1959), we often find ourselves exploring many possible analytic pipelines but reporting only a selected few. ...
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Chapter
This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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