Armstrong, J.
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Professor of Marketing
Introduction
Professor Armstrong is internationally known for his pioneering work on forecasting methods. He is author of Long-Range Forecasting, the most frequently cited book on forecasting methods, and Principles of Forecasting, voted the "Favorite Book – First 25 Years" by researchers and practitioners associated with the International Institute of Forecasters. He is a co-founder of the Journal of Forecasting, the International Journal of Forecasting, the International Symposium on Forecasting, and forecastingprinciples.com. He is a co-developer of new methods including rule-based forecasting, causal forces for extrapolation, simulated interaction, and structured analogies.
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Publication Learner responsibility in management education, or ventures into forbidden research (with comments)(1983-04-01) Armstrong, J. ScottFormal education can be improved by transferring responsibility from the teacher to the learner. A simple approach to this is the time contract. Time contracts have been used successfully in nine quasi-experiments but, despite these successes, some educators see this as subversive research.Publication The natural learning project(1979) Armstrong, J. ScottIn “natural learning” the learner takes responsibility for learning. This responsibility applies to setting objectives, selecting active learning tasks, obtaining feedback, and making applications. Self-oriented skill training (SOS) provides a highly structured procedure to help the learner through the above four phases of natural learning. Of particular importance in SOS is the experiential exercise; this can put the learner through the unfreezing, change, and refreezing steps. The design of SOS is based on substantial empirical evidence. Results from five crude field experiments were consistent with the hypothesis that SOS increases the efficiency of learning. In a 6-month follow-up, participants using SOS reported 2.1 behavioral changes vs. 0.6 for those following a traditional approach to learning.Publication Automatic Identification of Time-Series Features for Rule-based Forecasting(2001-04-01) Adya, Monica; Collopy, Fred; Armstrong, J. Scott; Kennedy, MilesRule-based forecasting (RBF) is an expert system that uses features of time series to select and weight extrapolation techniques. Thus, it is dependent upon the identification of features of the time series. Judgmental coding of these features is expensive and the reliability of the ratings is modest. We developed and automated heuristics to detect six features that had previously been judgmentally identified in RBF: outliers, level shifts, change in basic trend, unstable recent trend, unusual last observation, and functional form. These heuristics rely on simple statistics such as first differences and regression estimates. In general, there was agreement between automated and judgmental codings for all features other than functional form. Heuristic coding was more sensitive than judgment and consequently, identified more series with a certain feature than judgmental coding. We compared forecast accuracy using automated codings with that using judgmental codings across 122 series. Forecasts were produced for six horizons, resulting in a total of 732 forecasts. Accuracy for 30% of the 122 annual time series was similar to that reported for RBF. For the remaining series, there were as many that did better with automated feature detection as there were that did worse. In other words, the use of automated feature detection heuristics reduced the costs of using RBF without negatively affecting forecast accuracy.Publication Review of Alfie Kohn, No Contest: The Case Against Competition(1988-10-01) Armstrong, J. ScottKohn's No Contest reviews empirical research on competition. In fact, much work has been done to determine whether competition is better than cooperation and some work has compared competition with doing the best for oneself. The research comes from many fields, but primarily from education, sports, the performing arts,and psychology. The results have been consistent, clear-cut, and surprising: competition typically results in less creativity, poorer performance, and reduced satisfaction.Publication Identification of Asymmetric Prediction Intervals through Causal Forces(2001-07-01) Armstrong, J. Scott; Collopy, FredWhen causal forces are specified, the expected direction of the trend can be compared with the trend based on extrapolation. Series in which the expected trend conflicts with the extrapolated trend are called contrary series. We hypothesized that contrary series would have asymmetric forecast errors, with larger errors in the direction of the expected trend. Using annual series that contained minimal information about causality, we examined 671 contrary forecasts. As expected, most (81%) of the errors were in the direction of the causal forces. Also as expected, the asymmetries were more likely for longer forecast horizons; for six-year-ahead forecasts, 89% of the forecasts were in the expected direction. The asymmetries were often substantial. Contrary series should be flagged and treated separately when prediction intervals are estimated, perhaps by shifting the interval in the direction of the causal forces.Publication Evidence-Based Forecasting for Climate Change(2013-02-01) Green, Kesten C; Soon, Willie; Armstrong, J. ScottFollowing Green, Armstrong and Soon’s (IJF 2009) (GAS) naïve extrapolation, Fildes and Kourentzes (IJF 2011) (F&K) found that each of six more-sophisticated, but inexpensive, extrapolation models provided forecasts of global mean temperature for the 20 years to 2007 that were more accurate than the “business as usual” projections provided by the complex and expensive “General Circulation Models” used by the U.N.’s Intergovernmental Panel on Climate Change (IPCC). Their average trend forecast was .007°C per year, and diminishing; less than a quarter of the IPCC’s .030°C projection. F&K extended previous research by combining forecasts from evidence-based short-term forecasting methods. To further extend this work, we suggest researchers: (1) reconsider causal forces; (2) validate with more and longer-term forecasts; (3) adjust validation data for known biases and use alternative data; and (4) damp forecasted trends to compensate for the complexity and uncertainty of the situation. We have made a start in following these suggestions and found that: (1) uncertainty about causal forces is such that they should be avoided in climate forecasting models; (2) long term forecasts should be validated using all available data and much longer series that include representative variations in trend; (3) when tested against temperature data collected by satellite, naïve forecasts are more accurate than F&K’s longer-term (11-20 year) forecasts; and (4) progressive damping improves the accuracy of F&K’s forecasts. In sum, while forecasting a trend may improve the accuracy of forecasts for a few years into the future, improvements rapidly disappear as the forecast horizon lengthens beyond ten years. We conclude that predictions of dangerous manmade global warming and of benefits from climate policies fail to meet the standards of evidence-based forecasting and are not a proper basis for policy decisions.Publication Assessing Game Theory, Role Playing, and Unaided Judgment(2002-05-28) Armstrong, J. ScottGreen's study [Int. J. Forecasting (forthcoming)] on the accuracy of forecasting methods for conflicts does well against traditional scientific criteria. Moreover, it is useful, as it examines actual problems by comparing forecasting methods as they would be used in practice. Some biases exist in the design of the study and they favor game theory. As a result, the accuracy gain of game theory over unaided judgment may be illusory, and the advantage of role playing over game theory is likely to be greater than the 44% error reduction found by Green. The improved accuracy of role playing over game theory was consistent across situations. For those cases that simulated interactions among people with conflicting roles, game theory was no better than chance (28% correct), whereas role-playing was correct in 61% of the predictions.Publication Statistical Significance Tests are Unnecessary Even When Properly Done and Properly Interpreted: Reply to Commentaries(2007-04-01) Armstrong, J. ScottThe three commentators on my paper agree that statistical tests are often improperly used by researchers and that even when properly used, readers misinterpret them. These points have been well established by empirical studies. However, two of the commentators do not agree with my major point that significance tests are unnecessary even when properly used and interpreted.Publication Natural Learning in Higher Education(2011-01-01) Armstrong, J. ScottPublication Role Playing: A Method to Forecast Decisions(2001-01-01) Armstrong, J. ScottRole playing can be used to forecast decisions, such as “how will our competitors respond if we lower our prices?” In role playing, an administrator asks people to play roles and uses their “decisions” as forecasts. Such an exercise can produce a realistic simulation of the interactions among conflicting groups. The role play should match the actual situation in key respects, such as the role-players should be somewhat similar to those being represented in the actual situations, and role-players should read instructions for their roles before reading about the situation. Role playing is most effective for predictions when two conflicting parties respond to large changes. A review of the evidence showed that role playing was effective in matching results for seven of eight experiments. In five actual situations, role playing was correct for 56 percent of 143 predictions, while unaided expert opinions were correct for 16 percent of 172 predictions. Role-playing has also been used successfully to forecast outcomes in three studies. Successful uses of role playing have been claimed in the military, law, and business.