Copyright (c) 2010 All rights reserved. http://works.bepress.com/j_scott_armstrong Recent documents in J. Scott Armstrong en-us Wed, 28 Jul 2010 01:31:31 PDT 3600 http://works.bepress.com/j_scott_armstrong/170 http://works.bepress.com/j_scott_armstrong/170 Mon, 26 Jul 2010 13:28:10 PDT J. Scott Armstrong Strategic Planning http://works.bepress.com/j_scott_armstrong/169 http://works.bepress.com/j_scott_armstrong/169 Mon, 26 Jul 2010 13:20:41 PDT Competitor-oriented objectives, such as market-share targets, are promoted by academics and are commonly used by firms. A 1996 review of the evidence, summarized in this paper, found that competitor-oriented objectives reduced profitability. We describe new evidence from 12 studies, one of which is introduced in this paper. The new evidence supports the conclusion that competitor-oriented objectives are harmful, especially when managers receive information about competitors’ market shares. The evidence appears to have had little effect on managers’ decisions and on what is taught in business schools. J. Scott Armstrong Strategic Planning http://works.bepress.com/j_scott_armstrong/168 http://works.bepress.com/j_scott_armstrong/168 Mon, 26 Jul 2010 13:05:44 PDT The prevalence of faulty citations impedes the growth of scientific knowledge. Faulty citations include omissions of relevant papers, incorrect references, and quotation errors that misreport findings. We discuss key studies in these areas. We then examine citations to “Estimating nonresponse bias in mail surveys,” one of the most frequently cited papers from the Journal of Marketing Research, to illustrate these issues. This paper is especially useful in testing for quotation errors because it provides specific operational recommendations on adjusting for nonresponse bias; therefore, it allows us to determine whether the citing papers properly used the findings. By any number of measures, those doing survey research fail to cite this paper and, presumably, make inadequate adjustments for nonresponse bias. Furthermore, even when the paper was cited, 49 of the 50 studies that we examined reported its findings improperly. The inappropriate use of statistical-significance testing led researchers to conclude that nonresponse bias was not present in 76 percent of the studies in our sample. Only one of the studies in the sample made any adjustment for it. Judging from the original paper, we estimate that the study researchers should have predicted nonresponse bias and adjusted for 148 variables. In this case, the faulty citations seem to have arisen either because the authors did not read the original paper or because they did not fully understand its implications. To address the problem of omissions, we recommend that journals include a section on their websites to list all relevant papers that have been overlooked and show how the omitted paper relates to the published paper. In general, authors should routinely verify the accuracy of their sources by reading the cited papers. For substantive findings, they should attempt to contact the authors for confirmation or clarification of the results and methods. This would also provide them with the opportunity to enquire about other relevant references. Journal editors should require that authors sign statements that they have read the cited papers and, when appropriate, have attempted to verify the citations. J. Scott Armstrong Scientific Methods http://works.bepress.com/j_scott_armstrong/167 http://works.bepress.com/j_scott_armstrong/167 Mon, 26 Jul 2010 13:03:54 PDT We examined the frequency of replications published in the two leading forecasting journals, the International Journal of Forecasting (IJF) and the Journal of Forecasting (JoF). Replications in the IJF and JoF comprised 9.4% of the empirical papers. This compares with various areas of management science ranging from 2.2% in the Journal of Marketing Research to 18.1% in the American Economic Review. We also found that 36.2% of replications in forecasting journals provided full support, 44.7% partial support, and 19.1% no support for initial study findings. Given the importance of replications, we recommend steps to encourage replications, such as requiring full-disclosure of methods and data for all published papers, and inviting researchers to replicate specified important papers. J. Scott Armstrong Scientific Methods http://works.bepress.com/j_scott_armstrong/166 http://works.bepress.com/j_scott_armstrong/166 Mon, 26 Jul 2010 12:59:38 PDT J. Scott Armstrong Marketing http://works.bepress.com/j_scott_armstrong/165 http://works.bepress.com/j_scott_armstrong/165 Mon, 26 Jul 2010 12:57:38 PDT J. Scott Armstrong Marketing http://works.bepress.com/j_scott_armstrong/164 http://works.bepress.com/j_scott_armstrong/164 Mon, 26 Jul 2010 12:54:46 PDT J. Scott Armstrong Marketing http://works.bepress.com/j_scott_armstrong/163 http://works.bepress.com/j_scott_armstrong/163 Mon, 26 Jul 2010 12:52:57 PDT Our review of five experiments on the effects of mandatory disclaimers led us to conclude that (1) the information they provide is not important to consumers and (2) it is poorly understood by them. We conducted an experiment to assess the effects on decision making of including a mandatory disclaimer in a print advertisement for a dentist offering implant dentistry. A total of 317 subjects were recruited in mall intercepts and were shown a pair of advertisements. In all cases, one of the advertisements was for a dentist who had an implant dentistry credential and the other was for one without. In one-third of cases the advertisement for the dentist with credentials included a mandatory disclaimer: the Florida Statutory Disclaimer. (The FSD is intended to explain institutional arrangements). In a second third, there was no disclaimer. And in the final third, we included a disclaimer we thought would increase comprehension. The subjects were asked which of the two dentists they would recommend to a friend in need of implant dentistry services. More subjects were confused when they were exposed to the mandatory disclaimer, and more recommended the dentist who lacked credentials. Subjects with less formal education and women were particularly prone to confusion and to making inferior recommendations. The disclaimer led people to draw false and damaging inferences about the dentist who advertised his implant dentistry credentials. Our attempt to write a disclaimer that improved comprehension failed. We conclude that a mandatory disclaimer should only be used when there is experimental evidence that it will provide information that is important to consumers and is understood by them, and when providing this information does not unfairly harm those who advertise products or services. We were unable to find any cases where mandatory disclaimers met these criteria. J. Scott Armstrong Marketing http://works.bepress.com/j_scott_armstrong/162 http://works.bepress.com/j_scott_armstrong/162 Mon, 26 Jul 2010 12:47:53 PDT One hundred and thirty-nine principles are used to summarize knowledge about forecasting. They cover formulating a problem, obtaining information about it, selecting and applying methods, evaluating methods, and using forecasts. Each principle is described along with its purpose, the conditions under which it is relevant, and the strength and sources of evidence. A checklist of principles is provided to assist in auditing the forecasting process. An audit can help one to find ways to improve the forecasting process and to avoid legal liability for poor forecasting. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/161 http://works.bepress.com/j_scott_armstrong/161 Mon, 26 Jul 2010 12:46:53 PDT Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted methodological procedures, such as to prespecify criteria or to obtain a large sample of forecast errors. However, forecasters often violate such principles, even in academic studies. Some principles might be surprising, such as do not use R-square, do not use Mean Square Error, and do not use the within-sample fit of the model to select the most accurate time-series model. A checklist of 32 principles is provided to help in systematically evaluating forecasting methods. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/160 http://works.bepress.com/j_scott_armstrong/160 Mon, 26 Jul 2010 12:45:52 PDT To improve forecasting accuracy, combine forecasts derived from methods that differ substantially and draw from different sources of information. When feasible, use five or more methods. Use formal procedures to combine forecasts: An equal-weights rule offers a reasonable starting point, and a trimmed mean is desirable if you combine forecasts resulting from five or more methods. Use different weights if you have good domain knowledge or information on which method should be most accurate. Combining forecasts is especially useful when you are uncertain about the situation, uncertain about which method is most accurate, and when you want to avoid large errors. Compared with errors of the typical individual forecast, combining reduces errors. In 30 empirical comparisons, the reduction in ex ante errors for equally weighted combined forecasts averaged about 12.5% and ranged from 3 to 24 percent. Under ideal conditions, combined forecasts were sometimes more accurate than their most accurate components. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/159 http://works.bepress.com/j_scott_armstrong/159 Mon, 26 Jul 2010 12:44:26 PDT Accuracy, analogies, combined forecasts, conjoint analysis, cross-sectional data, econometric methods, experiments, expert systems, extrapolation, intentions, judgmental bootstrapping, policy analysis, role playing, rule-based forecasting, structured judgment, track records, and time-series data J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/158 http://works.bepress.com/j_scott_armstrong/158 Mon, 26 Jul 2010 12:43:07 PDT Expert systems use rules to represent experts’ reasoning in solving problems. The rules are based on knowledge about methods and the problem domain. To acquire knowledge for an expert system, one should rely on a variety of sources, such as textbooks, research papers, interviews, surveys, and protocol analysis. Protocol analysis is especially useful if the area to be modeled is complex or if experts lack an awareness of their processes. Expert systems should be easy to use, incorporate the best available knowledge, and reveal the reasoning behind the recommendations they make. In forecasting, the most promising applications of expert systems are to replace unaided judgment in cases requiring many forecasts, to model complex problems where data on the dependent variable are of poor quality, and to handle semi-structured problems. We found 15 comparisons of forecast validity involving expert systems. As expected, expert systems were more accurate than unaided judgment, six comparisons to one, with one tie. Expert systems were less accurate than judgmental bootstrapping in two comparisons with two ties. There was little evidence with which to compare expert systems and econometric models; expert systems were better in one study and tied in two. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/157 http://works.bepress.com/j_scott_armstrong/157 Mon, 26 Jul 2010 12:41:59 PDT Rule-Based Forecasting (RBF) is an expert system that uses judgment to develop and apply rules for combining extrapolations. The judgment comes from two sources, forecasting expertise and domain knowledge. Forecasting expertise is based on more than a half century of research. Domain knowledge is obtained in a structured way; one example of domain knowledge is managers= expectations about trends, which we call “causal forces.” Time series are described in terms of 28 conditions, which are used to assign weights to extrapolations. Empirical results on multiple sets of time series show that RBF produces more accurate forecasts than those from traditional extrapolation methods or equal-weights combined extrapolations. RBF is most useful when it is based on good domain knowledge, the domain knowledge is important, the series is well behaved (such that patterns can be identified), there is a strong trend in the data, and the forecast horizon is long. Under ideal conditions, the error for RBF’s forecasts were one-third less than those for equal-weights combining. When these conditions are absent, RBF neither improves nor harms forecast accuracy. Some of RBF’s rules can be used with traditional extrapolation procedures. In a series of studies, rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/156 http://works.bepress.com/j_scott_armstrong/156 Mon, 26 Jul 2010 12:40:17 PDT Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are: • In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past. • Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them. • In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received. • To assess uncertainty, make empirical estimates to establish prediction intervals. • Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/155 http://works.bepress.com/j_scott_armstrong/155 Mon, 26 Jul 2010 12:38:30 PDT Judgmental bootstrapping is a type of expert system. It translates an expert=s rules into a quantitative model by regressing the expert=s forecasts against the information that he used. Bootstrapping models apply an expert=s rules consistently, and many studies have shown that decisions and predictions from bootstrapping models are similar to those from the experts. Three studies showed that bootstrapping improved the quality of production decisions in companies. To date, research on forecasting with judgmental bootstrapping has been restricted primarily to cross-sectional data, not time-series data. Studies from psychology, education, personnel, marketing, and finance, showed that bootstrapping forecasts were more accurate than forecasts made by experts using unaided judgment. They were more accurate for eight of eleven comparisons, less accurate in one, and there were two ties. The gains in accuracy were generally substantial. Bootstrapping can be useful when historical data on the variable to be forecast are lacking or of poor quality; otherwise, econometric models should be used. Bootstrapping is most appropriate for complex situations, where judgments are unreliable, and where experts= judgments have some validity. When many forecasts are needed, bootstrapping is cost-effective. If experts differ greatly in expertise, bootstrapping can allow one to draw upon the forecasts made by the best experts. Bootstrapping aids learning; it can help to identify biases in the way experts make predictions, and it can reveal how the best experts make predictions. Finally, judgmental bootstrapping offers the possibility of conducting ?experiments@ when the historical data for causal variables have not varied over time. Thus, it can serve as a supplement for econometric models. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/154 http://works.bepress.com/j_scott_armstrong/154 Mon, 26 Jul 2010 12:36:16 PDT Role 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. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/153 http://works.bepress.com/j_scott_armstrong/153 Mon, 26 Jul 2010 12:32:18 PDT J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/152 http://works.bepress.com/j_scott_armstrong/152 Mon, 26 Jul 2010 12:30:16 PDT Using findings from empirically-based comparisons, Scott develops nine generalizations that can improve forecast accuracy. He finds that these are often ignored by organizations, so that attention to them offers substantial opportunities for gain. In this paper, Scott offers recommendations on how to structure a forecasting problem, how to tap managers’ knowledge, and how to select appropriate forecasting methods. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/151 http://works.bepress.com/j_scott_armstrong/151 Mon, 26 Jul 2010 12:16:36 PDT In 2007, the Intergovernmental Panel on Climate Change’s Working Group One, apanel of experts established by the World Meteorological Organization and theUnited Nations Environment Programme, issued its Fourth Assessment Report.The Report included predictions of dramatic increases in average worldtemperatures over the next 92 years and serious harm resulting from the predictedtemperature increases. Using forecasting principles as our guide we asked: Arethese forecasts a good basis for developing public policy? Our answer is “no”.To provide forecasts of climate change that are useful for policy-making, onewould need to forecast (1) global temperature, (2) the effects of any temperaturechanges, and (3) the effects of feasible alternative policies. Proper forecasts of allthree are necessary for rational policy making.The IPCC WG1 Report was regarded as providing the most credible long-termforecasts of global average temperatures by 31 of the 51 scientists and others involvedin forecasting climate change who responded to our survey. We found no referencesin the 1056-page Report to the primary sources of information on forecasting methodsdespite the fact these are conveniently available in books, articles, and websites. Weaudited the forecasting processes described in Chapter 8 of the IPCC’s WG1 Reportto assess the extent to which they complied with forecasting principles. We foundenough information to make judgments on 89 out of a total of 140 forecastingprinciples. The forecasting procedures that were described violated 72 principles.Many of the violations were, by themselves, critical.The forecasts in the Report were not the outcome of scientific procedures. Ineffect, they were the opinions of scientists transformed by mathematics andobscured by complex writing. Research on forecasting has shown that experts’predictions are not useful in situations involving uncertainly and complexity. Wehave been unable to identify any scientific forecasts of global warming. Claims thatthe Earth will get warmer have no more credence than saying that it will get colder. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/150 http://works.bepress.com/j_scott_armstrong/150 Mon, 26 Jul 2010 12:14:31 PDT J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/149 http://works.bepress.com/j_scott_armstrong/149 Mon, 26 Jul 2010 12:12:47 PDT Traditional groups meetings are an inefficient and ineffective method for making forecasts and decisions. We compare two structured alternatives to traditional meetings: the Delphi technique and prediction markets. Delphi is relatively simple and cheap to implement and has been adopted for diverse applications in business and government since its origins in the 1950s. It can be used for nearly any forecasting, estimation, or decision making problem not barred by complexity or ignorance. While prediction markets were used more than a century ago, their popularity waned until more recent times. Prediction markets can be run continuously, and they motivate participation and participants to reveal their true beliefs. On the other hand, they need many participants and clear outcomes in order to determine pay-offs. Moreover, translating knowledge into a price is not intuitive to everyone and constructing contracts that will provide a useful forecast may not be possible for some problems. It is difficult to maintain confidentiality with markets and they are vulnerable to manipulation. Delphi is designed to reveal panelists’ knowledge and opinions via their forecasts and the reasoning they provide. This format allows testing of knowledge and learning by panelists as they refine their forecasts but may also lead to conformity due to group pressure. The reasoning provided as an output of the Delphi process is likely to be reassuring to forecast users who are uncomfortable with the “black box” nature of prediction markets. We consider that, half a century after its original development, Delphi is under-utilized. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/148 http://works.bepress.com/j_scott_armstrong/148 Mon, 26 Jul 2010 12:10:54 PDT In this year’s presidential election, as in 2004, the Pollyvote applied the evidence-based principle of combining all credible forecasts (Armstrong, 2001) to predict the election outcome. Pollyvote is calculated by averaging within and across four components, all weighted equally, to forecast the incumbent party’s share of the two-party vote. The components were updated on a daily basis, or whenever new data became available, and included: • Combined trial-heat polls (using the RCP poll average from realclearpolitics.com) • A seven-day rolling average of the vote-share contract prices on the Iowa Electronic Market (IEM) • 16 quantitative models • A survey of experts on American politics J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/147 http://works.bepress.com/j_scott_armstrong/147 Fri, 23 Jul 2010 13:42:38 PDT We conducted laboratory experiments to analyze the accuracy of three structured approaches (nominal groups, Delphi, and prediction markets) compared to traditional face-to-face meetings (FTF). We recruited 227 participants (11 groups per method) that had to solve a quantitative judgment task that did not involve distributed knowledge. This task consisted of ten factual questions, which required percentage estimates. While, overall, we did not find statistically significant differences in accuracy between the four methods, the results differed somewhat at the individual question level. Delphi was as accurate as FTF for eight questions and outperformed FTF for two questions. By comparison, prediction markets were unable to outperform FTF for any of the ten questions but were inferior for three questions. The relative performance of nominal groups and FTF was mixed and differences were small. We also compared the results from the three structured approaches to prior individual estimates and staticized groups. The three structured approaches were more accurate than participants’ prior individual estimates. Delphi was also more accurate than staticized groups. Nominal groups and prediction markets provided little additional value compared to a simple average of forecast. In addition, we examined participants’ perceptions of the group and the group process. Participants rated personal communication more favorable than computer-mediated interaction. Group interaction in FTF and nominal groups was perceived as highly cooperative and effective. Prediction markets were rated least favorable. Prediction market participants were least satisfied with the group process and perceived their method as most difficult. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/146 http://works.bepress.com/j_scott_armstrong/146 Fri, 23 Jul 2010 13:39:02 PDT Policymakers need to know whether prediction is possible and if so whether any proposed forecasting method will provide forecasts that are substantively more accurate than those from the relevant benchmark method. Inspection of global temperature data suggests that it is subject to irregular variations on all relevant time scales and that variations during the late 1900s were not unusual. In such a situation, a “no change” extrapolation is an appropriate benchmark forecasting method. We used the U.K. Met Office Hadley Centre’s annual average thermometer data from 1850 through 2007 to examine the performance of the benchmark method. The accuracy of forecasts from the benchmark is such that even perfect forecasts would be unlikely to help policymakers. For example, mean absolute errors for 20- and 50-year horizons were 0.18°C and 0.24°C. We nevertheless demonstrate the use of benchmarking with the example of the Intergovernmental Panel on Climate Change’s 1992 linear projection of long-term warming at a rate of 0.03°C-per-year. The small sample of errors from ex ante projections at 0.03°C-per-year for 1992 through 2008 was practically indistinguishable from the benchmark errors. Validation for long-term forecasting, however, requires a much longer horizon. Again using the IPCC warming rate for our demonstration, we projected the rate successively over a period analogous to that envisaged in their scenario of exponential CO2 growth—the years 1851 to 1975. The errors from the projections were more than seven times greater than the errors from the benchmark method. Relative errors were larger for longer forecast horizons. Our validation exercise illustrates the importance of determining whether it is possible to obtain forecasts that are more useful than those from a simple benchmark before making expensive policy decisions. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/145 http://works.bepress.com/j_scott_armstrong/145 Fri, 23 Jul 2010 13:35:38 PDT To forecast decisions in conflict situations, experts are often advised to figuratively stand in the other person’s shoes. We refer to this as “role thinking” because, in practice, the advice is to think about how other protagonists will view the situation in order to predict their decisions. We tested the effect of role thinking on forecast accuracy. We obtained 101 role-thinking forecasts of the decisions that would be made in nine diverse conflicts from 27 Naval postgraduate students (experts) and 107 rolethinking forecasts from 103 second-year organizational behavior students (novices). The accuracy of the novices’ forecasts was 33% and the experts’ 31%; both were little different from chance (guessing), which was 28%. The lack of improvement in accuracy from role thinking strengthens the finding from earlier research that it is not sufficient to think hard about a situation in order to predict the decisions groups of people will make when they are in conflict. It is useful instead to ask groups of role players to simulate the situation. When groups of novice participants adopted the roles of protagonists in the aforementioned nine conflicts and interacted with each other, their group decisions predicted the actual decisions with an accuracy of 60%. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/144 http://works.bepress.com/j_scott_armstrong/144 Fri, 23 Jul 2010 13:32:52 PDT J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/143 http://works.bepress.com/j_scott_armstrong/143 Fri, 23 Jul 2010 13:29:09 PDT We used 59 biographical variables to create a “bio-index” for forecasting U.S. presidential elections. The bio-index method counts the number of variables for which a candidate rates favourably, and the forecast is that the candidate with the highest score would win the popular vote. The bio-index relies on different information and includes more variables than traditional econometric election forecasting models. The method can be used in combination with simple linear regression to estimate a relationship between the index score of the candidate of the incumbent party and his share of the popular vote. The study tested the model for the 29 U.S. presidential elections from 1896 to 2008. The model‟s forecasts, calculated by cross-validation, correctly predicted the popular vote winner for 27 of the 29 elections; this performance compares favourably to forecasts from polls (15 out of 19), prediction markets (22 out of 26), and three econometric models (12 to 13 out of 15 to 16). Out-of-sample forecasts of the two-party popular vote for the four elections from 1996 to 2008 yielded a forecast error almost as low as the best of seven econometric models. The model can help parties to select the candidates running for office, and it can help to improve on the accuracy of election forecasting, especially for longer-term forecasts. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/142 http://works.bepress.com/j_scott_armstrong/142 Fri, 23 Jul 2010 13:27:00 PDT We used the take-the-best heuristic to develop a model to forecast the popular two party vote shares in U.S. presidential elections. The model draws upon information about how voters expect the candidates to deal with the most important issue facing the country. We used cross-validation to calculate a total of 1,000 out-of-sample forecasts, one for each of the last 100 days of the ten U.S. presidential elections from 1972 to 2008. Ninety-seven percent of forecasts correctly predicted the winner of the popular vote. The model forecasts were competitive compared to forecasts from methods that incorporate substantially more information (e.g., econometric models and the Iowa Electronic Markets). The purpose of the model is to provide fast advice on which issues candidates should stress in their campaign. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/141 http://works.bepress.com/j_scott_armstrong/141 Fri, 23 Jul 2010 13:22:17 PDT Ideally, presidential elections should be decided based on how the candidates would handle issues facing the country. If so, knowledge about the voters' perception of the candidates should help to forecast election outcomes. Our model, named PollyIssues, provides a forecast of the winner of the popular vote in U.S. Presidential Elections. It is based on the voters' overall perception of which candidate will do the best job in handling the issues facing the country. PollyIssues correctly picked the winner for nine of the last ten elections from 1972 to 2008, with one tie. In addition, it provided an idea of the margin of victory. In predicting the two-party vote percentages for the last three elections from 2000 to 2008, its out-of-sample forecasts outperformed those derived from well-established econometric models. J. Scott Armstrong Forecasting http://works.bepress.com/j_scott_armstrong/140 http://works.bepress.com/j_scott_armstrong/140 Fri, 23 Jul 2010 13:16:17 PDT We summarize evidence showing that the global warming alarm movement has more of the character of a political movement than that of a scientific controversy. We then make forecasts of the effects and outcomes of this movement using a structured analysis of analogous situations—a method that has been shown to produce accurate forecasts for conflict situations. This paper summarizes the current status of this “structured analogies project.”We searched the literature and asked diverse experts to identify phenomena that could be characterized as alarms warning of future disasters that were endorsed by scientists, politicians, and the media, and that were accompanied by calls for strong action. The search yielded 71 possible analogies. We examined objective accounts to screen the possible analogies and found that 26 met all criteria. We coded each for forecasting procedures used, the accuracy of the forecasts, the types of actions called for, and the effects of actions implemented.Our preliminary findings are that analogous alarms were presented as “scientific,” but none were based on scientific forecasting procedures. Every alarming forecast proved to be false; the predicted adverse effects either did not occur or were minor. Costly government policies remained in place long after the predicted disasters failed to materialize. The government policies failed to prevent ill effects.The findings appear to be insensitive to which analogies are included. The structured analogies approach suggests that the current global warming alarm is simply the latest example of a common social phenomenon: an alarm based on unscientific forecasts of a calamity. We conclude that the global warming alarm will fade, but not before much additional harm is done by governments and individuals making inferior decisions on the basis of unscientific forecasts. J. Scott Armstrong Forecasting