Does Feedback Improve the Accuracy of Recurrent Judgmental Forecasts?

Remus, William; O'Conner, Marcus; Griggs, Kenneth A.

doi:10.1006/obhd.1996.0035

Cited by 62 publications

(49 citation statements)

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“…Such information can include how the forecaster reacts to certain cues or the behavior needed to improve the forecasts. Remus et al (1996) found no evidence that cognitive process feedback might be helpful (in addition to task properties feedback, see below for a description) and this is a confirmation of the results found by Balzer et al (1992) pertaining to probability forecasts. Lim et al (2005) showed that the effectiveness of this type of feedback might be improved by the way the feedback is presented, that is multimedia messages might be more effective than textual messages.…”

Section: Literature On Feedbacksupporting

confidence: 56%

“…Remus et al (1996) did not find evidence in their laboratory experiment that performance feedback improves forecasting practices as compared to outcome feedback. In contrast, pertaining to judgmental interval predictions and pertaining to probability forecasts, Bolger andÖnkal-Atay (2004) and Stone and Opel (2000) do find that performance feedback improves the forecasts.…”

Section: Literature On Feedbackmentioning

confidence: 87%

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Do Experts' SKU Forecasts Improve after Feedback?

Legerstee

Franses

2011

SSRN Journal

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Section: Literature On Feedbacksupporting

confidence: 56%

Section: Literature On Feedbackmentioning

confidence: 87%

Do Experts' SKU Forecasts Improve after Feedback?

Legerstee

Franses

2011

SSRN Journal

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“…(Balzer et al,1989) and this result has been found to apply in the forecasting context (Remus et al,1996). Indeed, outcome feedback may actually damage performance by drawing attention to the most recent observation (which will contain noise) and thereby accentuating the tendency to overweight this observation in the subsequent forecast.…”

Section: ) Providing Learning Facilitiesmentioning

confidence: 70%

The design features of forecasting support systems and their effectiveness

Fildes

Goodwin

Lawrence

2006

Decision Support Systems

109

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Forecasts play a key role in the management of the supply chain. In most organisations such forecasts form part of an information system on which other functions such as scheduling, resource planning and marketing depend. Forecast accuracy is, therefore, an important component in the delivery of an effective supply chain. Typically, the forecasts are produced by integrating managerial judgment with quantitative forecasts within a forecasting support system (FSS). However, there is much evidence that this integration is often carried out poorly with deleterious effects on accuracy. This study considers the role that a well-designed FSS might have in improving this situation. It integrates the literatures on forecasting and decision support to explain the causes of the problem and to identify design features of FSSs that might help to ameliorate it. An assessment is made of the extent to which currently available business forecasting packages, which are widely employed in supply chain management, possess these features.

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“…Moreover, when combined with cognitive or outcome feedback, task information feedback is more effective (Reilly & Doherty, 1992;Balzer, Sulsky, Hammer, & Sumner, 1992). However, sometimes Determinants of linear judgment 24 providing task information only may be sufficient (Reilly & Doherty, 1992;Remus, O'Connor, & Griggs, 1996).…”

Section: How Effective Is Learning? the Role Of Feedbackmentioning

confidence: 99%

Determinants of Linear Judgment: A Meta-Analysis of Lens Model Studies

2007

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The mathematical representation of Brunswik's 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 five decades. Specifically, we analyze statistics of the "lens model equation" (Tucker, 1964) associated with 259 different task environments obtained from 78 papers. In short, we find -on average -fairly high levels of judgmental achievement and note that people can achieve similar levels of cognitive performance in both noisy and predictable environments. Although overall performance varies little between laboratory and field studies, both differ in terms of components of performance and types of environments (numbers of cues and redundancy). An analysis of learning studies reveals that the most effective form of feedback is information about the task. We also analyze empirically when bootstrapping is more likely to occur. We conclude by indicating shortcomings of the kinds of studies conducted to date, limitations in the lens model methodology, and possibilities for future research.Keywords: judgment, lens model, linear models, learning, bootstrapping Determinants of linear judgment 3 Since the 1960s, many psychologists have used the framework of Brunswik's (1952) lens model to study processes where humans make predictions of specific criteria (see, e.g., Brehmer & Joyce, 1988;Cooksey, 1996;Hastie & Kameda, 2005). For example, a person might make a judgment (i.e., prediction) about another person's intelligence, about the likelihood of rain, whether a job candidate will be successful, and so on. In all these cases, the simple beauty of Brunswik's model lies in recognizing that both the person's judgment and the actual criterion predicted can be thought of as two separate functions of cues that are available in the environment. Thus, the accuracy of human judgment depends on the extent to which the function that describes it matches its environmental counterpart.But how good or accurate are people at making judgments and on what does this depend? These are important questions that have generated considerable controversy in the psychological literature (Cohen, 1981;Gigerenzer, 1996;Kahneman & Tversky, 1996). Whereas it is unlikely that these questions can be answered satisfactorily by any particular approach, an advantage of research conducted within the Brunswikian tradition is the use of a common methodology for formalizing the lens model. Thus, not only can researchers within this tradition communicate results within a common framework, it is possible to aggregate results quantitatively across many studies and make statements that reflect the accumulation of results. This is the purpose of the current paper in which we present a meta-analysis of studies conducted using the lens model over a period of five decades.The paper is organized as follows. We first describe the mathematical formulation of the lens model. Second, we specify how we identified and included particular studies Determinants of linear ju...

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Does Feedback Improve the Accuracy of Recurrent Judgmental Forecasts?

Cited by 62 publications

References 0 publications

Do Experts' SKU Forecasts Improve after Feedback?

Do Experts' SKU Forecasts Improve after Feedback?

The design features of forecasting support systems and their effectiveness

Determinants of Linear Judgment: A Meta-Analysis of Lens Model Studies

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