Group-based forecasting?: A social psychological analysis

Kerr, Norbert L.; Tindale, R. Scott

doi:10.1016/j.ijforecast.2010.02.001

Cited by 112 publications

(87 citation statements)

References 91 publications

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“…It is not possible to know in advance whether the most confident or least confident person is more likely to be right. Kerr and Tindale (2011) suggest that in purely judgemental tasks (where there is no correct answer) groups tend to coalesce around the majority opinion. However, when the correctness of an answer can be justified, this increases the chance of a minority opinion prevailing.…”

Section: Allowing Experts To Interact Is Possibly a Good Ideamentioning

confidence: 99%

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Forecasts or fortune-telling: When are expert judgements of safety risk valid?

Rae

Alexander

2017

Safety Science

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Section: Allowing Experts To Interact Is Possibly a Good Ideamentioning

confidence: 99%

“…The research objective is to examine by observation how groups interact to achieve consensus (Kerr & Tindale, 2011). Methodological approaches range from controlled experiments to textual analysis of real-world meetings.…”

Section: Opinions Can Be Combined Socially or Mathematicallymentioning

confidence: 99%

Forecasts or fortune-telling: When are expert judgements of safety risk valid?

Rae

Alexander

2017

Safety Science

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“…In fact, Wright and Rowe (2011) report that group-based judgemental forecasting methodologies could lead to a global consensus. Kerr and Tindale (2011) argue that only aggregation methods facilitating information exchange between group members are likely to be beneficial over a statistical averaging of prior individual opinions. Such information exchange in structured group interactions provides the enabling conditions for group members to identify errors in the justifications of judgements.…”

Section: Forecasting Methods Integrating Judgemental and Mathematicalmentioning

confidence: 99%

A Collaborative Demand Forecasting Process with Event-Based Fuzzy Judgements

Cheikhrouhou

Marmier

Ayadi³

et al. 2013

SSRN Journal

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a b s t r a c tMathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes. Since forecasters have partial knowledge of the context and of future events, grouping and structuring the fragmented implicit knowledge, in order to be easily and fully integrated in final demand forecasts is the objective of this work. This paper presents a judgemental collaborative approach for demand forecasting in which the mathematical forecasts, considered as the basis, are adjusted by the structured and combined knowledge from different forecasters. The approach is based on the identification and classification of four types of particular events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence of the results. To validate the approach, two case studies were developed with forecasters from a plastic bag manufacturer and a distributor belonging to the food retailing industry. The results show that by structuring and combining the judgements of different forecasters to identify and assess future events, companies can experience a high improvement in demand forecast accuracy.

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“…The drawbacks in this case are limited participation levels and the need for selection of members, as well as planning processes that are more time consuming. Indeed, the use of panels has a range of problems associated with it, such as the panel composition, the interaction mode between panel members and, above all, the aggregation of panel responses into a form useful for the decision (Beinat, 1997;Kerr and Tindale, 2011).…”

Section: Challenge 7: How To Elicit Criteria Weights From Experts/pubmentioning

confidence: 99%

“…The advantage of online participation is the possibility of a wider and asynchronous involvement of both experts and non-experts. Its drawbacks are limited interaction between participants and the analyst, which miss the benefits of facilitated decision modeling (Franco and Montibeller, 2010), such as a facilitator that can debias decision makers' judgments (Montibeller and von Winterfeldt, 2015) and the advantages of face-to-face group decisionmaking (Kerr and Tindale, 2011); as well as dilution of expertise and power among the group, which might make it difficult to reach an agreement and implement the chosen solution (Phillips, 2007). The advantages of physical participation consist, instead, in enabling facilitated modelling and promoting interaction among participants.…”

mentioning

confidence: 99%