A Meta-Analysis of Judgment Achievement as Defined by the Lens Model Equation

Kaufmann, Esther; Athanasou, James A.

doi:10.1024/1421-0185.68.2.99

Cited by 25 publications

(30 citation statements)

References 60 publications

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“…The results of the more recent analysis by Karelaia and Hogarth [ 8 ] were in line with Camerer [ 6 ], although the authors pointed out the high heterogeneity of the success of bootstrapping across tasks and highlighted the need to identify the task and judge characteristics that favor bootstrapping. Previous reviews on lens model indices indicated wide heterogeneity (see [ 20 ]) and implied domain differences in lens model statistics (see [ 21 , 11 ]), suggesting that judgment achievement is different in different decision domains (e.g., medicine, business, education, psychology) and in turn implying that the success of bootstrapping models is also domain-dependent. Indeed, these preliminary results suggesting that the success of bootstrapping was domain-dependent highlight the need for more detailed analysis.…”

Section: Introductionmentioning

confidence: 99%

The Success of Linear Bootstrapping Models: Decision Domain-, Expertise-, and Criterion-Specific Meta-Analysis

Kaufmann

Wittmann

2016

PLoS ONE

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The success of bootstrapping or replacing a human judge with a model (e.g., an equation) has been demonstrated in Paul Meehl’s (1954) seminal work and bolstered by the results of several meta-analyses. To date, however, analyses considering different types of meta-analyses as well as the potential dependence of bootstrapping success on the decision domain, the level of expertise of the human judge, and the criterion for what constitutes an accurate decision have been missing from the literature. In this study, we addressed these research gaps by conducting a meta-analysis of lens model studies. We compared the results of a traditional (bare-bones) meta-analysis with findings of a meta-analysis of the success of bootstrap models corrected for various methodological artifacts. In line with previous studies, we found that bootstrapping was more successful than human judgment. Furthermore, bootstrapping was more successful in studies with an objective decision criterion than in studies with subjective or test score criteria. We did not find clear evidence that the success of bootstrapping depended on the decision domain (e.g., education or medicine) or on the judge’s level of expertise (novice or expert). Correction of methodological artifacts increased the estimated success of bootstrapping, suggesting that previous analyses without artifact correction (i.e., traditional meta-analyses) may have underestimated the value of bootstrapping models.

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Section: Introductionmentioning

confidence: 99%

The Success of Linear Bootstrapping Models: Decision Domain-, Expertise-, and Criterion-Specific Meta-Analysis

Kaufmann

Wittmann

2016

PLoS ONE

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“…It is important to note that, on completing our analysis, we became aware of another recent meta-analysis of lens model studies conducted byKaufmann and Athanasou (2007). The scope of their work, however, is more limited than ours and their criteria for including studies in the analysis are different.…”

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confidence: 97%

Determinants of linear judgment: A meta-analysis of lens model studies.

Karelaia

Hogarth

2008

Psychological Bulletin

275

156

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The mathematical representation of E. Brunswik's (1952) lens model has been used extensively to study human judgment and provides a unique opportunity to conduct a meta-analysis of studies that covers roughly 5 decades. Specifically, the authors analyzed statistics of the "lens model equation" (L. R. Tucker, 1964) associated with 249 different task environments obtained from 86 articles. On average, fairly high levels of judgmental achievement were found, and people were seen to be capable of achieving similar levels of cognitive performance in noisy and predictable environments. Further, the effects of task characteristics that influence judgment (numbers and types of cues, inter-cue redundancy, function forms and cue weights in the ecology, laboratory versus field studies, and experience with the task) were identified and estimated. A detailed analysis of learning studies revealed that the most effective form of feedback was information about the task. The authors also analyzed empirically under what conditions the application of bootstrapping--or replacing judges by their linear models--is advantageous. Finally, the authors note shortcomings of the kinds of studies conducted to date, limitations in the lens model methodology, and possibilities for future research.

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“…We do not deal in detail with the multiple regression analysis-based applications of the Lens Model in social judgment theory (Gifford 1994;Hertwig and Bond 2011;Karelaia and Hogarth 2008;Kaufmann and Athanasou 2009), interpersonal conflict (Dhami and Olsson 2008), or other fields, although the regression model is included as an example in the evaluation section (Fig. 10).…”

Section: The Lens Model Figurementioning

confidence: 99%

Managing complexity: from visual perception to sustainable transitions—contributions of Brunswik’s Theory of Probabilistic Functionalism

Scholz

2017

Environ Syst Decis

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Coping with the multitude of information, relationships, and dynamics of the biotic and abiotic environment is a fundamental prerequisite for the survival of any organismic system. This paper discusses what contribution the Theory of Probabilistic Functionalism (TPF) of Egon Brunswik (1903Brunswik ( -1955, which was originally developed for visual perception (including certain cognitive processes) and later for judgment, may provide today. The present paper elaborates that the principles of TPF go beyond the common weighting and regression analysisbased model of information processing that has been associated with the Brunswikian Lens Model. We argue that Brunswik's TPF rather provides basic principles of how organisms interact with complex environmental systems when processing cues (instead of information) and thus are able to produce evolutionarily stable representations of and judgments about the environment. TPF was formulated with no references to physiological processes. The present paper aims to demonstrate how well these principles correspond with current biophysical and neurophysiological findings, models, and simulations of sensation. We then discuss in what ways planning groups may be seen as organisms and how groups resemble and differ from (biological) organisms on the level of the individual and below. Based on this, we suggest how the principles of TPF can be used to describe planning groups' activities when constructing planning variants or scenarios for sustainable transitioning. We illustrate the ways in which (under ideal constraints that may be provided in ideal transdisciplinary processes) planning groups follow principles such as vicarious mediation. Here, we reflect on the ways decision theoretic tools (such as Formative Scenario Analysis and Multi-Attribute Decision Analysis) can serve to construct robust (i.e., ''evolutionarily stable'') orientations for the future. It is difficult to validate big theories such as TPF. Thus, special attention is paid to the question of how strategies of validation (according to normal scientific principles) for different principles and TPF as such can be developed. We conclude that (in the context of sustainable transitioning) TPF can be utilized from a descriptive, prescriptive, and normative perspective. All three perspectives call for different strategies of validation.

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A Meta-Analysis of Judgment Achievement as Defined by the Lens Model Equation

Cited by 25 publications

References 60 publications

The Success of Linear Bootstrapping Models: Decision Domain-, Expertise-, and Criterion-Specific Meta-Analysis

The Success of Linear Bootstrapping Models: Decision Domain-, Expertise-, and Criterion-Specific Meta-Analysis

Determinants of linear judgment: A meta-analysis of lens model studies.

Managing complexity: from visual perception to sustainable transitions—contributions of Brunswik’s Theory of Probabilistic Functionalism

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