Book reviews 429

priors is not always best in terms of parameter Judgment and Decision Making: An Interdiscip-variability, at least for the Nelson and Plosser linary Reader, Second edition, Terry Connolly,dataset. This idea about priors and their links to Hal R. Arkes and Kenneth R. Hammond (Eds.),uncertainty is useful and insightful, but this sort Cambridge University Press, 2000. Paperback:of analysis is not done anywhere else in the ISBN 0-521-62602-1, £24.95 ($34.95); Hard-book. back: ISBN: 0-521-62355-3, £60.00 ($84.95)

In conclusion, the book contains sufficientdetails to provide analytical inferences in dy- This text is a collection of forty papers (mostnamic models. It, however, cannot serve as a of them previously published in journals) thatuseful source book for forecasters because of aims to provide a general interdisciplinary intro-lack of detail and the complexities involved in duction to the field of judgment and decisionimplementing a particular model. Nevertheless, making. The book is published in collaborationit can serve as a useful textbook for advanced with the Society for Judgment and Decisionundergraduate or graduate courses in either time Making and the vast majority of the authors areseries analysis or econometrics. based in US universities. The first edition,

published in 1986, contained a number ofclassic papers like Tversky and Kahneman’s

References ‘Judgment under uncertainty: Heuristics andbiases’ and, as such, proved to be valuable

Box G. E. P., & Tiao, G. C. (1973). Bayesian Inference in resource for students and researchers. Many ofStatistical Analysis, New York: Wiley.

these papers are retained in this second edition,Carlin, B. P., & Louis, T. A. (1996). Bayes and Empiricalalthough more than three-quarters of the chap-Bayes Methods for Data Analysis, Chapman and Hall,

London. ters are new to the text. For readers of theGelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. International Journal of Forecasting the most

(1995). Bayesian Data Analysis, Chapman and Hall, interesting part of the book is likely to be theTexts in Statistical Science Series. London. new section devoted specifically to forecastingOrd, K. (1999). Review of Bayesian Forecasting and

and prediction.Dynamic Models. Second Edition, by M. West and P.J.The three papers in this section are excellent.Harrison. International Journal of Forecasting 15, 341–

342. Baruch Fischhoff’s ‘What forecasts (seem to)West, M., & Harrison, P. J. (1997). Bayesian Forecasting mean’ was originally published in this journal in

and Dynamic Models, Springer Series in Statistics. 1994. It deals with a neglected, but vital ques-Springer-Verlag, New York, Second Edition.

tion: how should we communicate our forecastsZellner, A. (1971). An Introduction to Bayesian Inferenceto make them meaningful to their intendedin Econometrics, New York: Wiley.users? Fischhoff uses a wide range of forecast-

Vinay Kanetkar ing contexts to elaborate the potential problems:Department of Consumer Studies ambiguity, irrelevance, immodesty (i.e. not ad-

University of Guelph mitting the limits of our knowledge), and im-Guelph, ONT N1G 2W1 poverishment (i.e. not addressing the broader

Canada context within which forecasts and associateddecisions are made).

PII : S0169-2070( 00 )00054-6 This is followed by Robyn Dawes’s ‘Properand improper linear models’, which was alsopublished in this journal in 1986. In this highly

430 Book reviews

readable paper, Dawes draws on examples from I have just two relatively minor reservationsa number of disparate fields to discuss the well about the text. The first is the age of many ofknown, though surprising, finding that simple the papers – nearly half were published beforeweighted means of predictor variables produce 1991. Excellent and widely cited as these papersmore accurate forecasts than experts who are are, one cannot help wondering whether thebasing their judgment on the same variables. reader would have been better served by new

The last paper in the section is Thomas chapters that report the state of the art. Second-Stewart and Cynthia Lusk’s ‘Seven components ly, there is almost no reference in the text to theof judgmental forecasting skill: Implications for now substantial literature on judgmental timeresearch and the improvement of forecasts’. series forecasting, despite the widespread use ofThis was first published in the Journal of such forecasts as a basis for decision making inForecasting in 1994. It uses Murphy’s de- businesses and other organisations. In fairness,composition and Brunswik’s lens model to it should be pointed out that the editors ack-decompose the mean squared error of judg- nowledge the impossibility of including any-mental forecasts into seven components, each thing more than a very tiny sample of the field.measuring a particular skill. The paper then That said, this new edition is most welcomediscusses how knowledge of these components and deserves to find a place on the desk of anymight be used to improve forecast accuracy. student or researcher working in the field of

Many chapters in other parts of the book will judgment and decision making. As the editorsalso be of interest to forecasters. For example, point out, the first edition was, by academicBenjamin Kleinmuntz’s ‘Why we still use our standards, something of a best seller. Thisheads instead of formulas’ echoes Dawes’s edition deserves to repeat that success.paper. It provides a comprehensive review ofthe reasons for people’s apparent aversion to the Paul Goodwinuse of mathematical or statistical combinations University of the West of Englandof data, in favour of their own judgment. This Bristoltheme will be all too familiar to judgmental United Kingdomforecasting researchers who have repeatedlyreported the propensity of people to replace

PII : S0169-2070( 00 )00055-8reliable statistical time series forecasts withtheir own much less reliable judgments.