Polar Bear Population Forecasts: A Public-Policy Forecasting Audit

Armstrong, J. Scott; Green, Kesten C.; Soon, Willie

doi:10.1287/inte.1080.0383

Cited by 17 publications

(10 citation statements)

References 26 publications

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“…Fish and Wildlife Service researchers to prepare their forecasts that polar bears were endangered. The researchers refused to provide these data on the grounds that they were using them (Armstrong, Green, and Soon 2008).…”

Section: Provide Full Disclosure To Encourage Independent Audits and mentioning

confidence: 99%

“…Fish and Wildlife Service scientists overlooked the need for damping when they used only five years of historical data to forecast an immediate and strong reversal in the trend of the polar bear population. Moreover, they extended the forecast 50 years into the future (Armstrong, Green, and Soon 2008).…”

Section: Be Conservative When Forecasting Trends If the Forecast Horimentioning

confidence: 99%

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Golden Rule of Forecasting: Be Conservative

2015

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This paper proposes a unifying theory of forecasting in the form of a Golden Rule of Forecasting. The Golden Rule is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek all knowledge relevant to the problem, and use methods that have been validated for the situation. A checklist of 28 guidelines is provided to implement the Golden Rule. This article's review of research found 150 experimental comparisons; all supported the guidelines. The average error reduction from following a single guideline (compared to common practice) was 28 percent. The Golden Rule Checklist helps forecasters to forecast more accurately, especially when the situation is uncertain and complex, and when bias is likely. Nonexperts who know the Golden Rule can identify dubious forecasts quickly and inexpensively. To date, ignorance of research findings, bias, sophisticated statistical procedures, and the proliferation of big data have led forecasters to violate the Golden Rule. As a result, despite major advances in forecasting methods, evidence that forecasting practice has improved over the past half-century is lacking.

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Section: Provide Full Disclosure To Encourage Independent Audits and mentioning

confidence: 99%

Section: Be Conservative When Forecasting Trends If the Forecast Horimentioning

confidence: 99%

Golden Rule of Forecasting: Be Conservative

2015

Self Cite

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“…On this side, providing additional information and different approaches, such as fuzzy modelling, improves the accuracy (Kabak & Ülengin, 2010;Petrovic, Xie, & Burnham, 2006). In addition, in situations, where forecasters might be biased, it is important to obtain forecasts from experts with different biases (Armstrong, Green, & Soon, 2008).…”

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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“…If this situation occurs, the entire institution of wildlife comanagement may be damaged, an outcome ultimately in no-one's interest." Polar bears are controversial (see Armstrong et al 2008 andAmstrup et al 2009). The purpose of this volume is to present information that the authors do not think was considered in the U.S. government's listing process because it contradicted biological information or otherwise challenged the scientific evidence used.…”

mentioning

confidence: 99%