Subjective confidence in forecasts: A response to fischhoff and MacGregor

Here we evaluate the generalizability of calibration studies which have used general knowledge questions, and argue that on conceptual, methodological and empirical grounds the results have limited applicability to judgemental forecasting. We also review evidence which suggests that judgemental forecast probabilities are influenced by variables such as the desirability, imminence, time period and perceived controllability of the event to be forecast. As these variables do not apply to judgement in the domain of general knowledge, a need for research recognizing and exploring the psychological processes underlying uncertainty about the future is apparent.     

How probable is probable? A numerical translation of verbal probability expressions

The reported experiment took place in a professional forecasting organization accustomed to giving verbal probability assessments (‘likely’, ‘probable’, etc.). It attempts to highlight the communication problems caused by verbal probability expressions and to offer possible solutions that are compatible with the forecasters overall perspective on their jobs Experts in the organization were first asked to give a numerical translation to 30 different verbal probability expressions most of which were taken from the organization's own published political forecasts. In a second part of the experiment the experts were given 15 paragraphs selected from the organization's political publications each of which contained at least one verbal expression of probability. Subjects were again asked to give a numerical translation to each verbal probability expression The results indicate that (a) there is a high variability in the interpretation of verbal probability expressions and (b) the variability is even higher in context. Possible reasons for the context effect are discussed and practical implications are suggested.     

Subjective confidence in forecasts

Forecasts have little value to decision makers unless it is known how much confidence to place in them. Those expressions of confidence have, in turn, little value unless forecasters are able to assess the limits of their own knowledge accurately Previous research has shown very robust patterns in the judgements of individuals who have not received special training in confidence assessment: Knowledge generally increases as confidence increases. However, it increases too swiftly, with a doubling of confidence being associated with perhaps a 50 per cent increase in knowledge. With all but the easiest of tasks, people tend to be overconfident regarding how much they know These results have typically been derived from studies of judgements of general knowledge. The present study found that they also pertained to confidence in forecasts. Indeed, the confidence-knowledge curves observed here were strikingly similar to those observed previously. The only deviation was the discovery that a substantial minority of judges never expressed complete confidence in any of their forecasts. These individuals also proved to be better assessors of the extent of their own knowledge Apparently confidence in forecasts is determined by processes similar to those that determine confidence in general knowledge. Decision makers can use forecasters assessments in a relative sense, in order to predict when they are more and less likely to be correct. However, they should be hesitant to take confidence assessments literally. Someone is more likely to be right when he or she is ‘certain’than when he or she is ‘fairly confident’; but there is no guarantee that the certain forecast will come true.     

Conditional distribution analyses of probabilistic forecasts

Probabilistic forecasts have good ‘external correspondence’ if events that are assigned probabilities close to 1 tend to occur frequently, whereas those assigned probabilities near 0 tend to occur rarely. This paper describes simple procedures for analysing external correspondence into meaningful components that might guide efforts to understand and improve forecasting performance. The procedures focus on differences between the judgements made by the forecaster when the target event occurs, as compared to when it does not. The illustrations involve a professional oddsmaker's predictions of baseball game outcomes, meteorologists' precipitation forecasts and physicians' diagnoses of pneumonia. The illustrations demonstrate the ability of the procedures to highlight important forecasting tendencies that are sometimes more difficult to discern by other means.     

The consistency,coherence and calibration of holistic,decomposed and recomposed judgemental probability forecasts

In this paper we make an empirical investigation of the relationship between the consistency, coherence and validity of probability judgements in a real-world forecasting context. Our results indicate that these measures of the adequacy of an individual's probability assessments are not closely related as we anticipated. Twenty-nine of our thirty-six subjects were better calibrated in point probabilities than in odds and our subjects were, in general more coherent using point probabilities than odds forecasts. Contrary to our expectations we found very little difference in forecasting response and performance between simple and compound holistic forecasts. This result is evidence against the ‘divide-and-conquer’ rationale underlying most applications of normative decision theory. In addition, our recompositions of marginal and conditional assessments into compound forecasts were no better calibrated or resolved than their holistic counterparts. These findings convey two implications for forecasting. First, untrained judgemental forecasters should use point probabilities in preference to odds. Second, judgemental forecasts of complex compound probabilities may be as well assessed holistically as they are using methods of decomposition and recomposition. In addition, our study provides a paradigm for further studies of the relationship between consistency, coherence and validity in judgemental probability forecasting.     

Effects of difficulty on judgemental probability forecasting of control response efficacy

A judgemental control task was framed as a problem of medical decision making. The control parameter of a recursive system (i.e. a patient) was initially set so that output (i.e. a diagnostic index) fell outside a designated criterion range (corresponding to health). Subjects were told to bring the system's output into the designated range by resetting this control parameter (by specifying the dose of a drug). After each of these control responses, they made a probabilistic forecast that it would have the desired effect. It was found that these forecasts were more overconfident when the control task was more difficult but that the reason for this varied. When difficulty was manipulated across subjects, there was little evidence that lower control performance was associated with any lowering of the probabilistic forecasts. When difficulty was manipulated within subjects, they did lower their forecasts for more difficult task variants but did so insufficiently. In fact, relations between probabilistic forecasts of control response efficacy and proportion of those responses that were actually effective was linear with a slope of 0.44.     

A loose and separate certainty: Caves, Fuchs and Schack on quantum probability one

Carlton Caves, Fuchs, and Schack (2002) have recently appealed to an argument of mine (Stairs, 1983) to address a problem for their subjective Bayesian account of quantum probability. The difficulty is that on the face of it, quantum mechanical probabilities of one appear to be objective, but in that case, the Born Rule would yield a continuum of probabilities between zero and one. If so, we end up with objective probabilities strictly between zero and one. The authors claim that objective probabilities of one leads to a dilemma: give up locality or fall into contradiction. I argue that this conclusion depends on an overly strong interpretation of objectivism about quantum probabilities.     

A model of calibration for subjective probability distributions and its application to aggregation of judgements

A simple model is proposed of the statistical structure underlying the calibration of auditors' subjective probability distributions for account balances, and potentially for other unknown quantities. The model relates calibration curve shape to two parameters which represent over- or underconfidence and over- or underestimation. It is fitted to data from expert auditors. Different types of account appear to have different calibration characteristics. The model helps predict approximately the effects on calibration of aggregating individual subject distributions. Aggregation improves accuracy, but produces a strong tendency towards underconfidence. One aggregation method, predicting the best judgement in the group and using it as the group judgement, is found to be quite effective, much better than averaging the fractiles of individual distributions.     

Judgemental revision of sales forecasts: The relative performance of judgementally revised versus non-revised forecasts

The judgemental revision of sales forecasts is an issue which is receiving increasing attention in the forecasting literature. This paper compares the performance of forecasts after revision by managers with that of the forecasts which were accepted by them without revision. The data set consists of sales forecasting data from an industrial company, spanning six quarterly periods and relating to some 900 individual products. The findings show that, in general, the improvements made by managers bring the forecast errors of revised forecasts more into line with non-revised forecasts, but the change is often marginal, and the best result is equivalence between revised and non-revised forecasts.     

A comparative evaluation of objective and subjective weather forecasts in the united states

This paper is concerned primarily with the evaluation and comparison of objective and subjective weather forecasts. Operational forecasts of three weather elements are considered: (1) probability forecasts of precipitation occurrence, (2) categorical (i.e. non-probabilistic) forecasts of maximum and minimum temperatures and (3) categorical forecasts of cloud amount. The objective forecasts are prepared by numerical-statistical procedures, whereas the subjective forecasts are based on the judgements of individual forecasters. In formulating the latter, the forecasters consult information from a variety of sources, including the objective forecasts themselves. The precipitation probability forecasts are found to be both reliable and skilful, and evaluation of the temperature/cloud amount forecasts reveals that they are quite accurate/skilful. Comparison of the objective and subjective forecasts of precipitation occurrence indicates that the latter are generally more skilful than the former for shorter lead times (e.g. 12–24 hours), whereas the two types of forecasts are of approximately equal skill for longer lead times (e.g. 36–48 hours). Similar results are obtained for the maximum and minimum temperature forecasts. Objective cloud amount forecasts are more skilful than subjective cloud amount forecasts for all lead times. Examination of trends in performance over the last decade reveals that both types of forecasts for all three elements increased in skill (or accuracy) over the period, with improvements in objective forecasts equalling or exceeding improvements in subjective forecasts. The role and impact of the objective forecasts in the subjective weather forecasting process are discussed in some detail. The need to conduct controlled experiments and other studies of this process, with particular reference to the assimilation of information from different sources, is emphasized. Important characteristics of the forecasting system in meteorology are identified, and they are used to describe similarities and differences between weather forecasting and forecasting in other fields. Acquisition of some of these characteristics may be beneficial to other forecasting systems.     

Judging the probability that the next point in an observed time-series will be below,or above,a given value

We presented people with trended and untrended time series and asked them to estimate the probability that the next point would be below each of seven different reference values. The true probabilities that the point would be below these values were 0.01, 0.10, 0.25, 0.50, 0.75, 0.90 and 0.99. People overestimated probabilities of less than 0.50 and underestimated those of more than 0.50. Consequently, their subjective probability distributions were flatter than they should have been: people appeared to be under confident in their estimates of where the next point would lie. This bias was greater for the trended series. It was also greater in a second experiment in which people estimated the probability that the next item would be above the reference values. We discuss reasons for these effects and consider their implications for decision making.     

Bayesian consensus forecasts of macroeconomic variables

Economists, like other forecasters, share knowledge, data and theories in common. Consequently, their forecast errors are likely to be highly dependent. This paper reports on an empirical study of 16 macroeconomic forecasters. Composite forecasts are computed using a sequential weighting scheme that takes dependence into account; these are compared to a simple average and median forecasts. A within-sample composite is also calculated. Both these methods perform significantly better than the average or median of the forecasts. This improvement in accuracy is apparently because the dependence between the forecasters' errors is so high that the optimal composite forecasts sometimes lie outside the range of the individual forecasts.     

The problem of confirmation in the Everett interpretation

I argue that the Oxford school Everett interpretation is internally incoherent, because we cannot claim that in an Everettian universe the kinds of reasoning we have used to arrive at our beliefs about quantum mechanics would lead us to form true beliefs. I show that in an Everettian context, the experimental evidence that we have available could not provide empirical confirmation for quantum mechanics, and moreover that we would not even be able to establish reference to the theoretical entities of quantum mechanics. I then consider a range of existing Everettian approaches to the probability problem and show that they do not succeed in overcoming this incoherence.     

The role of probability arguments in the history of science

The paper examines Wesley Salmon’s claim that the primary role of plausibility arguments in the history of science is to impose constraints on the prior probability of hypotheses (in the language of Bayesian confirmation theory). A detailed look at Copernicanism and Darwinism and, more briefly, Rutherford’s discovery of the atomic nucleus reveals a further and arguably more important role of plausibility arguments. It resides in the consideration of likelihoods, which state how likely a given hypothesis makes a given piece of evidence. In each case the likelihoods raise the probability of one of the competing hypotheses and diminish the credibility of its rival, and this may happen either on the basis of ‘old’ or ‘new’ evidence.     

Judgemental revision of sales forecasts: Effectiveness of forecast selection

When managers make revisions to sales forecasts initially generated by a rational quantitative model it is important that the particular forecasts selected for adjustment are those which would benefit most from the adjustment process (i.e. realize high errors). This study reports an empirical investigation on this issue, spanning six quarterly forecasting periods and incorporating forecasting data on over 850 products. The results show that the errors of the forecasts chosen for revision are, in general, higher than those which were not chosen. In addition, it is shown that managesrs tend to revise forecasts which are initially low, hence possibily introducing some degree of bias into the overall forecasts.     

Unbiasedness,efficiency and the combination of economic forecasts

This paper considers the problem of determining whether forecasts are unbiased and examines the implications this has for combining different forecasts. The practical issues of how economic forecasts might be combined are discussed. There is an empirical illustration of the procedures in which the properties of UK forecasts from the London Business School, the National Institute, the Henley Centre for Forecasting, Phillips and Drew and the OECD are examined.     

Judgemental revision of sales forecasts: A longitudinal eetension

When quantitative models are used for short-term multi-item sales forecasts it is possible that the managers who use such forecasts may disagree with at least some of the estimates obtained, and wish to change them so that they become more consistent with their own (subjective) evaluation of the marketplace. This study reports on an analysis of the effectiveness of judgemental revision of sales forecasts over six quarterly forecasting periods. The results give general support for the practice of forecast manipulation as a means of improving forecasting accuracy. It is also observed that the effectiveness of revision activity varies across different time periods.     

Experience in combining subjective and quantitative forecasts of open market rates

This paper presents a method of combining subjective information from open-market operators with results from a time-series forecasting model. Empirical results of forecasts for interest rates of bank reserves are presented.     

Chance,determinism and the classical theory of probability

This paper situates the metaphysical antinomy between chance and determinism in the historical context of some of the earliest developments in the mathematical theory of probability. Since Hacking's seminal work on the subject, it has been a widely held view that the classical theorists of probability were guilty of an unwitting equivocation between a subjective, or epistemic, interpretation of probability, on the one hand, and an objective, or statistical, interpretation, on the other. While there is some truth to this account, I argue that the tension at the heart of the classical theory of probability is not best understood in terms of the duality between subjective and objective interpretations of probability. Rather, the apparent paradox of chance and determinism, when viewed through the lens of the classical theory of probability, manifests itself in a much deeper ambivalence on the part of the classical probabilists as to the rational commensurability of causal and probabilistic reasoning.     

The accuracy of individual and group forecasts from business outlook surveys

This paper reports on the accuracy of quarterly multiperiod predictions of inflation, real growth, unemployment and percentage changes in nominal GNP and two of its more volatile components. The survey data are highly differentiated; they cover 79 professional forecasters (mostly economists, analysts and corporate executives). Combining corresponding predictions from different sources can result in significant gains; thus the group mean forecasts are on the average over time more accurate than most of the corresponding sets of individual forecasts. But there is also a moderate degree of consistency in the relative performance of a sufficient number of the survey members, as evidenced in positive rank correlations among ratios of the individual to group root mean square errors.