Judgmental forecasts of time series affected by special events: does providing a statistical forecast improve accuracy?

Time series found in areas such as marketing and sales often have regular established patterns which are occasionally affected by exogenous influences, such as sales promotions. While statistical forecasting methods are adept at extrapolating regular patterns in series, judgmental forecasters have a potential advantage in that they can take into account the effect of these external influences, which may occur too infrequently for reliable statistical estimation. This suggests that a combination of statistical method and judgment is appropriate. An experiment was conducted to examine how judgmental forecasters make use of statistical time series forecasts when series are subject to sporadic special events. This was investigated under different conditions which were created by varying the complexity of the time series signal, the level of noise in the series, the salience of the cue, the predictive power of the cue information and the availability and presentation of the statistical forecast. Although the availability of a statistical forecast improved judgment under some conditions, the use the judgmental forecasters made of these forecasts was far from optimal. They changed the statistical forecasts when they were highly reliable and ignored them when they would have formed an ideal base‐line for adjustment. Copyright © 1999 John Wiley & Sons, Ltd.     

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so‐called trend‐damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent‐based model—the adaptive anchoring model (ADAM)—to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear‐regression time series model. Moreover, ADAM outperformed autoregressive‐integrated‐moving‐average (ARIMA) and exponential‐smoothing models, while neither of these models accounts for people's responses on the average estimation task.     

The relative influence of advice from human experts and statistical methods on forecast adjustments

Decision makers and forecasters often receive advice from different sources including human experts and statistical methods. This research examines, in the context of stock price forecasting, how the apparent source of the advice affects the attention that is paid to it when the mode of delivery of the advice is identical for both sources. In Study 1, two groups of participants were given the same advised point and interval forecasts. One group was told that these were the advice of a human expert and the other that they were generated by a statistical forecasting method. The participants were then asked to adjust forecasts they had previously made in light of this advice. While in both cases the advice led to improved point forecast accuracy and better calibration of the prediction intervals, the advice which apparently emanated from a statistical method was discounted much more severely. In Study 2, participants were provided with advice from two sources. When the participants were told that both sources were either human experts or both were statistical methods, the apparent statistical‐based advice had the same influence on the adjusted estimates as the advice that appeared to come from a human expert. However when the apparent sources of advice were different, much greater attention was paid to the advice that apparently came from a human expert. Theories of advice utilization are used to identify why the advice of a human expert is likely to be preferred to advice from a statistical method. Copyright © 2009 John Wiley & Sons, Ltd.     

Not as gloomy as we thought: reassessing how the public understands probability of precipitation forecasts

Prior research on probability of precipitation (PoP) forecasts showed that many people wrongfully believe that PoP forecasts are derived from a percentage of time, a percentage of a region or the strength of agreement among forecasters. We posit that the wording of interpretation selection tasks matters, because different response options are associated with different metacognitive feelings. We hypothesised that the incorrect PoP interpretations are more often chosen by participants because they are more fluent than the correct PoP interpretation. We assessed the role of fluency in correctness perception (Study 1) and reassessed PoP interpretations with a more fluent correct interpretation (Study 2). Fluency perception was positively related with perception of correctness. Furthermore, participants selected the correct fluent interpretation more often than the correct disfluent one. We have drawn a more optimistic picture of people’s PoP forecasts understanding than that shown before and have discussed the methodological and applied implications.     

Forecasting outcomes of the World Cup 2006 in football: Performance and confidence of bettors and laypeople

ObjectivesMotivated by a paucity of research on sports forecasting, this paper examines how well individuals with varying degrees of relevant knowledge predict football (soccer) and how much confidence they have in their predictions.Design and procedureThree groups of participants (110 art students, 81 sports students, and 85 bettors) representing three levels of knowledge (poor, moderate, and expert) performed five forecasting tasks with relation to the outcome of the first round of the World Cup 2006. The tasks were assumed to reflect different degrees of complexity. About half of the participants obtained relevant information (world rankings). The participants stated their confidence in connection to conducting those tasks.ResultsWhereas the groups had roughly similar levels of performance in three tasks (i.e., predicting the teams qualifying for the second round as well as two types of match statistics), the knowledgeable and expert participants were better at predicting scores and percentage of ball possession in the matches than the naïve participants. Relatively few participants performed better than the simple rule that followed world rankings. Regardless of task complexity, the knowledgeable and expert participants were found to be significantly more confident about their forecasts than the naïve participants. Access to information had limited influence on forecasting performance and confidence.ConclusionThe present study shows that the forecasting performance of football experts and laypeople varies dependent on task. For easy tasks, they tend to predict equally well, partly because laypeople might use well-adapted heuristics. Once the prediction tasks become more difficult, the experts may take advantage of their domain-specific skills and produce forecasts that excel those of the laypeople.     

Examining the trade‐off between confidence and optimism in future forecasts

Confident business forecasters are seen as more credible and competent (“confidence heuristic”). We explored a boundary condition of this effect by examining how individuals react to the trade‐off between confidence and optimism. Using hypothetical scenarios, we examined this trade‐off from the perspectives of judges (i.e., business owners who hired analysts to make sales predictions) and forecasters (i.e., the analysts hired to make predictions). Participants were assigned to the role of either judges or forecasters and were asked to rate 2 potential forecasts. In the “no trade‐off” condition, the 2 forecasts were aligned in optimism and confidence (the more confident forecast was also more optimistic); in the “trade‐off” condition, the more confident forecast was less optimistic. In Experiment 1, judges were more likely to positively evaluate confident forecasters when confident forecasters were the more (vs. less) optimistic ones. Experiment 2 demonstrated that forecasters were aware of judges' preferences for optimism and strategically relied on methods that resulted in more optimistic (but less reliable) predictions. Experiment 3 directly compared the perspectives of judges and forecasters, revealing that forecasters overestimated judges' preferences for optimism over confidence. The present studies show that forecasters and judges have different views of the trade‐off between confidence and optimism and that forecasters may unnecessarily sacrifice accuracy for optimism.     

On the relative importance of linear model and human judge(s) in combined forecasting

When and to what extent should forecasts rely on linear model or human judgment? The judgmental forecasting literature suggests that aggregating model and judge using a simple 50:50 split tends to outperform the two inputs alone. However, current research disregards the important role that the structure of the task, judges’ level of expertise, and the number of individuals providing a forecasting judgment may play. Ninety-two music industry professionals and 88 postgraduate students were recruited in a field experiment to predict chart entry positions of pop music singles in the UK and Germany. The results of a lens model analysis show how task structure and domain-specific expertise moderate the relative importance of model and judge. The study also delineates an upper boundary to which aggregating multiple judgments in model-expert combinations adds predictive accuracy. It is suggested that ignoring the characteristics of task and/or judge may lead to suboptimal forecasting performance.     

Age differences in affective forecasting and experienced emotion surrounding the 2008 US presidential election

In everyday life, people frequently make decisions based on tacit or explicit forecasts about the emotional consequences associated with the possible choices. We investigated age differences in such forecasts and their accuracy by surveying voters about their expected and, subsequently, their actual emotional responses to the 2008 US presidential election. A sample of 762 Democratic and Republican voters aged 20 to 80 years participated in a web-based study; 346 could be re-contacted two days after the election. Older adults forecasted lower increases in high-arousal emotions (e.g., excitement after winning; anger after losing) and larger increases in low-arousal emotions (e.g., sluggishness after losing) than younger adults. Age differences in actual responses to the election were consistent with forecasts, albeit less pervasive. Additionally, among supporters of the winning candidate, but not among supporters of the losing candidate, forecasting accuracy was enhanced with age, suggesting a positivity effect in affective forecasting. These results add to emerging findings about the role of valence and arousal in emotional ageing and demonstrate age differences in affective forecasting about a real-world event with an emotionally charged outcome.     

自闭症儿童对情景事件的情绪理解：基于内隐和外显的测量

该研究旨在采用生态效度更高的情绪情景事件以及内隐和外显两种测量方式考察自闭症儿童的情绪理解。选取自闭症和智商匹配的正常儿童各21名,以序列图片的方式呈现从动画片中截取的不同效价的情景事件,外显任务要求被试直接判断事件主角的情绪、内隐任务要求被试判断事件的一致性。结果发现：1.在外显任务中,自闭症儿童对正性和负性情绪的判断正确率均显著不如正常儿童,尤其是在负性情绪上存在明显的理解缺陷;2.在内隐任务中,正常儿童的正性负性情绪、自闭症儿童的正性情绪都表现出更弱的相对于中性情绪的理解效应;自闭症儿童的负性情绪理解则在两种任务中都接近于无。这些结果意味着自闭症儿童在外显和内隐任务中都存在负性情绪理解缺陷。     

自闭症儿童对情景事件的情绪理解：基于内隐和外显的测量

该研究旨在采用生态效度更高的情绪情景事件以及内隐和外显两种测量方式考察自闭症儿童的情绪理解。选取自闭症和智商匹配的正常儿童各21名,以序列图片的方式呈现从动画片中截取的不同效价的情景事件,外显任务要求被试直接判断事件主角的情绪、内隐任务要求被试判断事件的一致性。结果发现：1.在外显任务中,自闭症儿童对正性和负性情绪的判断正确率均显著不如正常儿童,尤其是在负性情绪上存在明显的理解缺陷;2.在内隐任务中,正常儿童的正性负性情绪、自闭症儿童的正性情绪都表现出更弱的相对于中性情绪的理解效应;自闭症儿童的负性情绪理解则在两种任务中都接近于无。这些结果意味着自闭症儿童在外显和内隐任务中都存在负性情绪理解缺陷。     

Age differences in affective forecasting and experienced emotion surrounding the 2008 US presidential election

In everyday life, people frequently make decisions based on tacit or explicit forecasts about the emotional consequences associated with the possible choices. We investigated age differences in such forecasts and their accuracy by surveying voters about their expected and, subsequently, their actual emotional responses to the 2008 US presidential election. A sample of 762 Democratic and Republican voters aged 20 to 80 years participated in a web-based study; 346 could be re-contacted two days after the election. Older adults forecasted lower increases in high-arousal emotions (e.g., excitement after winning; anger after losing) and larger increases in low-arousal emotions (e.g., sluggishness after losing) than younger adults. Age differences in actual responses to the election were consistent with forecasts, albeit less pervasive. Additionally, among supporters of the winning candidate, but not among supporters of the losing candidate, forecasting accuracy was enhanced with age, suggesting a positivity effect in affective forecasting. These results add to emerging findings about the role of valence and arousal in emotional ageing and demonstrate age differences in affective forecasting about a real-world event with an emotionally charged outcome.     

Professional vs. amateur judgment accuracy: The case of foreign exchange rates

Highly knowledgeable people often fail to achieve highly accurate judgments, a phenomenon sometimes called the “process-performance paradox.” The present research tested for this paradox in foreign exchange (FX) rate forecasting. Forty professional and 57 sophisticated amateur forecasters made one-day and one-week-ahead FX predictions in deterministic and probabilistic formats. Among the conclusions indicated by the results are: (a) professional accuracy usually surpasses amateur accuracy, although many amateurs outperform many professionals; (b) professionals appear to achieve high proficiency via heavy reliance on predictive information (unlike what has been observed before, e.g., for stock prices); (c) forecast format strongly affects judgment accuracy and processes; and (d) apparent overconfidence can transform itself into underconfidence depending on when and how forecasters must articulate their confidence.     

The Effects of Experience and a Decision Aid on the Slope,Scatter, and Bias of Earnings Forecasts

This study examines the joint effects of experience and a statistical decision aid on several dimensions of forecasting accuracy. Undergraduate business students, graduate business students, and experienced financial analysts made probabilistic earnings forecasts for 16 firms, either with or without access to a decision aid. Covariance decomposition was used to partition the overall accuracy score into the following components: bias, scatter, and slope. Consistent with prior research, the absolute accuracy of subjects was quite poor, even worse than a uniform forecaster. In contrast to previous results which documented an inverted experience–accuracy effect, experience had a positive impact on accuracy in the current study. This difference in results may be due, at least in part, to the different conditions under which subjects performed the earnings forecasting task. The covariance analysis showed that experienced financial analysts and MBA students had significantly better slope and lower scatter than undergraduate students. The financial analysts were also more biased than either of the student groups, yet still achieved the highest overall accuracy. The decision aid improved forecasting accuracy by increasing the slope of the analysts’ forecasts, while decreasing the scatter in the graduate and undergraduate students’ forecasts.     

判断预测的心理偏差及产生机制

心理学对判断预测的研究主要关注预测的偏差及产生机制。判断预测中的偏差有两类：偏见和不一致。偏见主要表现：趋势阻尼;提高效应;给预测结果增加随机噪音。不一致包括：信息获得导致的不一致;信息加工导致的不一致。偏差的出现一是与信息加工过程有关,二是与序列的特征与呈现方式有关。研究发现提供反馈、分解、组合和建议采纳等策略可以增进判断预测的准确性。     

Empathic forecasting: How do we predict other people's feelings?

When making affective forecasts, people commit the impact bias. They overestimate the impact an emotional event has on their affective experience. In three studies we show that people also commit the impact bias when making empathic forecasts, affective forecasts for someone else. They overestimate the impact an emotional event has on someone else's affective experience (Study 1), they do so for friends and strangers (Study 2), and they do so when other sources of information are available (Study 3). Empathic forecasting accuracy, the correlation between one person's empathic forecast and another person's actual affective experience, was lower than between-person forecasting correspondence, the correlation between one person's empathic forecast and another person's affective forecast. Empathic forecasts do not capture other people's actual experience very well but are similar to what other people forecast for themselves. This may enhance understanding between people.     

Who's the Best? A Relativistic View of Expertise

The dictionary and the expert performance approach view an expert as one who, after sufficient training and experience in a domain, can perform the requisite tasks above a threshold level. In contrast, we argue for a performance‐based approach that implies expertise is a continuum; the experts are the best performers. Most tasks in which expertise can be demonstrated have an underlying core of judgment, including domains in which the tasks call for judgment to be overlain with performance, prediction, or instruction. To evaluate judgment, we employ the metaphor of the judge as a measuring instrument. Like an instrument, expert judgment according to the performance‐based approach has three key properties: discrimination, consistency, and validity. Validity requires ground truth and is usually difficult to establish; but the other two properties are readily observable, and they are combined in the Cochran–Weiss–Shanteau index. Copyright © 2014 John Wiley & Sons, Ltd.     

Do Maximizers Predict Better than Satisficers?

We examine the relationship between maximizing (i.e., seeking the best) versus satisficing (i.e., seeking the good enough) tendencies and forecasting ability in a real‐world prediction task: forecasting the outcomes of the 2010 FIFA World Cup. In Studies 1 and 2, participants gave probabilistic forecasts for the outcomes of the tournament and completed a measure of maximizing tendencies. We found that although maximizers expected themselves to outperform others much more than satisficers, they actually forecasted more poorly. Hence, on net, they were more overconfident about their relative performance. Decompositional analyses of overall accuracy revealed that differences in forecasting abilities were primarily driven by maximizers' tendency to give more noisy estimates. In Study 3, participants played a betting task where they could choose between safe and uncertain gambles linked to World Cup outcomes. Again, maximizers did more poorly and earned less, because of greater noise in their choice‐based responses. Copyright © 2011 John Wiley & Sons, Ltd.     

Consuming experience: Why affective forecasters overestimate comparative value

The hedonic value of an outcome can be influenced by the alternatives to which it is compared, which is why people expect to be happier with outcomes that maximize comparative value (e.g., the best of several mediocre alternatives) than with outcomes that maximize absolute value (e.g., the worst of several excellent alternatives). The results of five experiments suggest that affective forecasters overestimate the importance of comparative value because forecasters do not realize that comparison requires cognitive resources, and that experiences consume more cognitive resources than do forecasts. In other words, because forecasters overestimate the extent to which they will be able to think about what they did not get while experiencing what they got.     

Affective forecasting and self-rated symptoms of depression, anxiety, and hypomania: evidence for a dysphoric forecasting bias

Emerging research has examined individual differences in affective forecasting; however, we are aware of no published study to date linking psychopathology symptoms to affective forecasting problems. Pitting cognitive theory against depressive realism theory, we examined whether dysphoria was associated with negatively biased affective forecasts or greater accuracy. Participants (n=325) supplied predicted and actual emotional reactions for three days surrounding an emotionally evocative relational event, Valentine's Day. Predictions were made a month prior to the holiday. Consistent with cognitive theory, we found evidence for a dysphoric forecasting bias-the tendency of individuals in dysphoric states to overpredict negative emotional reactions to future events. The dysphoric forecasting bias was robust across ratings of positive and negative affect, forecasts for pleasant and unpleasant scenarios, continuous and categorical operationalisations of dysphoria, and three time points of observation. Similar biases were not observed in analyses examining the independent effects of anxiety and hypomania. Findings provide empirical evidence for the long-assumed influence of depressive symptoms on future expectations. The present investigation has implications for affective forecasting studies examining information-processing constructs, decision making, and broader domains of psychopathology.     

Affective forecasting and self-rated symptoms of depression,anxiety, and hypomania: Evidence for a dysphoric forecasting bias

Emerging research has examined individual differences in affective forecasting; however, we are aware of no published study to date linking psychopathology symptoms to affective forecasting problems. Pitting cognitive theory against depressive realism theory, we examined whether dysphoria was associated with negatively biased affective forecasts or greater accuracy. Participants (n=325) supplied predicted and actual emotional reactions for three days surrounding an emotionally evocative relational event, Valentine's Day. Predictions were made a month prior to the holiday. Consistent with cognitive theory, we found evidence for a dysphoric forecasting bias—the tendency of individuals in dysphoric states to overpredict negative emotional reactions to future events. The dysphoric forecasting bias was robust across ratings of positive and negative affect, forecasts for pleasant and unpleasant scenarios, continuous and categorical operationalisations of dysphoria, and three time points of observation. Similar biases were not observed in analyses examining the independent effects of anxiety and hypomania. Findings provide empirical evidence for the long-assumed influence of depressive symptoms on future expectations. The present investigation has implications for affective forecasting studies examining information-processing constructs, decision making, and broader domains of psychopathology.