Paul Goodwin

ABSTRACT Bayes theorem is the normative method for revising probability forecasts using new information. However, for unaided forecasters its application can be difficult, effortful, opaque and even counter-intuitive. The study proposes two simple heuristics for approximating Bayes formula while yielding accurate decisions. Their performance was assessed where a decision is made on which of two events is most probable and where a choice is made between an option yielding an intermediate utility for something that is certain or for a gamble which will result in either a worse or better utility (“certainty or risk” decisions). For “most probable event” decisions the first heuristic always results in the correct decision when the reliability of the new information does not depend on which event will occur. In other cases, the second heuristic typically led to the correct decision for about 95% of “most probable event” decisions and 86% of “certainty or risk” decisions.

ABSTRACT I discuss evidence that supports several of the principles put forward in the paper by Armstrong, Green, and Graefe (AGG), but argue that the packaging of these principles as a single “golden rule”’ and the use of the term “conservative” may lead to misunderstandings. Additional work should be carried out to investigate the extent to which these principles should be applied to probability and interval forecasts. Finally, good reasons may support why “rational” forecasters behave in ways that are inconsistent with the guidelines AGG provide in their golden-rule checklist.

... Also the inserted questions literature (eg Glover 1989) and the text comprehension literature (eg McNamara, et al. ... text. Using the everyday task of programming a VCR, Duggan and Payne (2001) improved participants' retention of instructional information by prompting them to ...

... the latest information technology, with little attention to whether these new ... In this paper we investigate the psychology of a fairly standard forecasting task and then use this knowledge empirically to evaluate alternative computer-based de-cision support tools. ...

Abstract This paper considers the forecasting practice of a UK branch of a major international pharmaceutical company. The company uses a Forecasting Support System to prepare system forecasts, which are later'judgmentally'adjusted to produce a set of final ...

This book has had quite an impact since it was published in 2007. According to Wikipedia, it has sold over 270, 000 copies in its first year, was on the New York Times best-seller list for 17 week and had been translated into 27 languages. It is being reviewed here since I ...

ABSTRACT The successful introduction of new durable products plays an important part in helping companies to stay ahead of their competitors. Decisions relating to these products can be improved by the availability of reliable pre-launch forecasts of their adoption time series. However, producing such forecasts is a difficult, complex and challenging task, mainly because of the non-availability of past time series data relating to the product, and the multiple factors that can affect adoptions, such as customer heterogeneity, macroeconomic conditions following the product launch, and technological developments which may lead to the product’s premature obsolescence. This paper provides a critical review of the literature to examine what it can tell us about the relative effectiveness of three fundamental approaches to filling the data void : (i) management judgment, (ii) the analysis of judgments by potential customers, and (iii) formal models of the diffusion process. It then shows that the task of producing pre-launch time series forecasts of adoption levels involves a set of sub-tasks, which all involve either quantitative estimation or choice, and argues that the different natures of these tasks mean that the forecasts are unlikely to be accurate if a single method is employed. Nevertheless, formal models should be at the core of the forecasting process, rather than unstructured judgment. Gaps in the literature are identified, and the paper concludes by suggesting a research agenda so as to indicate where future research efforts might be employed most profitably.

ABSTRACT The importance of replication has been recognised across many scientific disciplines. Reproducibility is a necessary condition for replicability, because an inability to reproduce results implies that the methods have not been specified sufficiently, thus precluding replication. This paper describes how two independent teams of researchers attempted to reproduce the empirical findings of an important paper, “Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy” (Miller & Williams, 2003). The two teams proceeded systematically, reporting results both before and after receiving clarifications from the authors of the original study. The teams were able to approximately reproduce each other’s results, but not those of Miller and Williams. These discrepancies led to differences in the conclusions as to the conditions under which seasonal damping outperforms classical decomposition. The paper specifies the forecasting methods employed using a flowchart. It is argued that this approach to method documentation is complementary to the provision of computer code, as it is accessible to a broader audience of forecasting practitioners and researchers. The significance of this research lies not only in its lessons for seasonal forecasting but also, more generally, in its approach to the reproduction of forecasting research.

ABSTRACT Forecasting support systems (FSSs) have little value if users distrust the information and advice that they offer. Two experiments were used to investigate: (i) factors that influence the levels of users’ stated trust in advice provided by an FSS, when this advice is provided in the form of interval forecasts, (ii) the extent to which stated trust is associated with users’ modifications of the provided forecasts, and (iii) the consequences of these modifications for the calibration of the interval forecasts. Stated trust was influenced by the levels of noise in time series and whether a trend was present but was unaffected by the presence or absence of point forecasts. It was also higher when the intervals were framed as ‘best-case/worst-case’ forecasts and when the FSS provided explanations. Absence of trust was associated with a tendency to narrow the provided prediction intervals, which reduced their calibration.