On the relative importance of linear model and human judge(s) in combined forecasting

Seifert, Matthias; Hadida, Allègre L.

doi:10.1016/j.obhdp.2012.08.003

Cited by 17 publications

(25 citation statements)

References 48 publications

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“…Two distinct expert elicitation projects produced articles that analyzed over 100 forecasters. The first project (Seifert and Hadida, 2013) asked experts from music record labels to predict the success (rank) of pop singles. Record label experts were incentivized with a summary of their predictive accuracy, and an online platform collected predictions over a period of 12 weeks.…”

Section: Number Of Elicited Experts and Number Of Forecasts Madementioning

confidence: 99%

“…(Alvarado-Valencia et al, 2017;Graefe et al, 2014a;Borsuk, 2004;Brito and Griffiths, 2016a;Abramson et al, 1996;Mak et al, 1996) Ill-structured tasks When changes to an environment impact the probabilistic links between cues an expert receives and their effect (how these cues should should be interpreted). (Seifert and Hadida, 2013;Huang et al, 2016)…”

Section: Information Set Knowledge-basementioning

confidence: 99%

“…Expert forecasts are most readily found in finance, business, and marketing (Seifert and Hadida, 2013;Shin et al, 2013;Franses, 2011;Petrovic et al, 2006;Alvarado-Valencia et al, 2017;Song et al, 2013;Baecke et al, 2017,? ;Petrovic et al, 2006;Franses, 2011;Song et al, 2013;Alvarado-Valencia et al, 2017;Seifert and Hadida, 2013;Kabak andÜlengin, 2008). These fields focus on decision makers and their ability to make predictions from data that cannot easily be collected and fed to a statistical model.…”

Section: Introductionmentioning

confidence: 99%

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Aggregating expert-elicited data for prediction: A scoping review of statistical methods, experiments, and applications v1

McAndrew¹

2019

protocols.io

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Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse, rapidly changing, or unavailable, statistical models may not be able to make accurate predictions. Expert judgmental forecasts-models that combine expert-generated predictions into a single forecast-can make predictions when training data is limited by relying on expert intuition to take the place of concrete training data. Researchers have proposed a wide array of algorithms to combine expert predictions into a single forecast, but there is no consensus on an optimal aggregation model. This scoping review surveyed recent literature on aggregating expert-elicited predictions. We gathered common terminology, aggregation methods, and forecasting performance metrics, and offer guidance to strengthen future work that is growing at an accelerated pace.

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Section: Number Of Elicited Experts and Number Of Forecasts Madementioning

confidence: 99%

Section: Information Set Knowledge-basementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aggregating expert-elicited data for prediction: A scoping review of statistical methods, experiments, and applications v1

McAndrew¹

2019

protocols.io

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“…Whereas research on the general accuracy and appropriateness of judgmental forecasts has a long‐standing tradition, only few studies have attempted to decompose the forecasting context into different types of knowledge components in order to examine their effect on judgmental performance. Among these, Blattberg and Hoch (1990), Stewart et al (1997), and Seifert and Hadida (2013) have studied whether judgmental forecasts can add value beyond the predictions of linear models. However, these studies rest on the assumption that human judgment is capable of approximating the linear regression model of the environment fairly well.…”

Section: Introductionmentioning

confidence: 99%

“…This assumption appears questionable, at least in some forecasting contexts, when considering that the information processing capacity of forecasters is limited. In addition, while Lee and Siemsen (2015) as well as Seifert and Hadida (2013) acknowledge that differences in forecasting effectiveness may depend on task structure, a more systematic decomposition of the forecasting environment is required to fully understand how task characteristics fundamentally influence judgmental performance and how decision support systems should be designed to improve predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

Effective judgmental forecasting in the context of fashion products⋆

Seifert

Siemsen

Hadida

et al. 2015

J of Ops Management

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We study the conditions that influence judgmental forecasting effectiveness when predicting demand in the context of fashion products. Human judgment is of practical importance in this setting. Our goal is to investigate what type of decision support, in particular historical and/or contextual predictors, should be provided to human forecasters to improve their ability to detect and exploit linear and nonlinear cue-criterion relationships in the task environment. Using a field experiment on new product forecasts in the music industry, our analysis reveals that when forecasters are concerned with predictive accuracy and only managerial judgments are employed, providing both types of decision support data is beneficial. However, if judgmental forecasts are combined with a statistical forecast, restricting the decision support provided to human judges to contextual anchors is beneficial. We identify two novel interactions demonstrating that the exploitation of nonlinearities is easiest for human judgment if contextual data are present but historical data are absent. Thus, if the role of human judgment is to detect these nonlinearities (and the linearities are taken care of by some statistical model with which judgments are combined), then a restriction of the decision support provided would make sense. Implications for the theory and practice of building decision support models are discussed.Abstract. We study the conditions that influence judgmental forecasting effectiveness when predicting demand in the context of fashion products. Human judgment is of practical importance in this setting. Our goal is to investigate what type of decision support, in particular historical and/or contextual predictors, should be provided to human forecasters to improve their ability to detect and exploit linear and nonlinear cue-criterion relationships in the task environment. Using a field experiment on new product forecasts in the music industry, our analysis reveals that when forecasters are concerned with predictive accuracy and only managerial judgments are employed, providing both types of decision support data is beneficial. However, if judgmental forecasts are combined with a statistical forecast, restricting the decision support provided to human judges to contextual anchors is beneficial. We identify two novel interactions demonstrating that the exploitation of nonlinearities is easiest for human judgment if contextual data are present but historical data are absent. Thus, if the role of human judgment is to detect these nonlinearities (and the linearities are taken care of by some statistical model with which judgments are combined), then a restriction of the decision support provided would make sense.Implications for the theory and practice of building decision support models are discussed.

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