Conservatism in human information processing

Edwards, Ward

doi:10.1017/cbo9780511809477.026

Cited by 641 publications

(538 citation statements)

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“…Although judgments do sometimes adhere to Bayesian principles remarkably well (Ajzen, 1977;Griffiths & Tenenbaum, 2006;Kersten, Mamassian, & Yuille, 2004), human judgment certainly does not obey Bayes's Law perfectly (Edwards, 1968;Grether, 1990;McKelvey & Page, 1990).…”

Section: A Disclaimermentioning

confidence: 99%

The trouble with overconfidence.

Moore

Healy

2008

Psychological Review

1,857

1,414

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The authors present a reconciliation of 3 distinct ways in which the research literature has defined overconfidence: (a) overestimation of one's actual performance, (b) overplacement of one's performance relative to others, and (c) excessive precision in one's beliefs. Experimental evidence shows that reversals of the first 2 (apparent underconfidence), when they occur, tend to be on different types of tasks. On difficult tasks, people overestimate their actual performances but also mistakenly believe that they are worse than others; on easy tasks, people underestimate their actual performances but mistakenly believe they are better than others. The authors offer a straightforward theory that can explain these inconsistencies. Overprecision appears to be more persistent than either of the other 2 types of overconfidence, but its presence reduces the magnitude of both overestimation and overplacement.

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Section: A Disclaimermentioning

confidence: 99%

The trouble with overconfidence.

Moore

Healy

2008

Psychological Review

1,857

1,414

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“…Unfortunately, while there is a vast psychology literature debating whether agents update according to Bayes Rule, it does not directly examine the effects of model uncertainty. The early literature actually found evidence of conservatism (Edwards (1968)). In what is perhaps the only analysis of the implications of model uncertainty for Bayesian updating, Navon (1978) argued that apparent conservatism could be explained if agents suspect the data are subject to measurement error or mean reversion.…”

Section: Where E(t) Is the Forecast Error (X −X) Given Choices Of K mentioning

confidence: 99%

Robustness and information processing

Kasa

2006

Review of Economic Dynamics

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Abstract. This paper considers a standard Kalman filtering problem subject to model uncertainty and information-processing constraints. It draws a connection between robust filtering (Hansen and Sargent (2002)) and Rational Inattention (Sims(2003)). Considered separately, robustness and Rational Inattention are shown to be observationally equivalent, in the sense that a higher filter gain can either be interpreted as an increased preference for robustness, or an increased ability to process information. However, it is more interesting to consider them jointly. In this case, it is argued that an increased preference for robustness can be interpreted as an increased demand for information processing, while Sims' model of Rational Inattention can be interpreted as placing a constraint on the available supply. This suggests that the way agents actually implement robust decision rules is by allocating some of their scarce information processing capacity to problems that are characterized by high degrees of model uncertainty and risk-sensitivity.JEL Classification #'s: C61, D81.

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“…Similarly, in research on probability revision it was the deviation between subjects' judgments and the "correct" answer derived from Bayes' theorem that became the explunandumnot subjects' judgments in and of themselves. Depending on the direction of the deviation, the explanandum was called "conservatism" (Edwards, 1968) or "base rate fallacy" (Bar-Hillel, 1984), and seen as an error of reasoning. Much of the research of the 1980s was concerned with identifying features of the contentsuch as its vividness, specificity, and causality -that would "facilitate" Bayesian reasoning.…”

Section: Research On Deductive and Probabilistic Reasoning: Some Commmentioning

confidence: 99%