The present research examines the Uncertainty-Prediction Asymmetry (UPA) hypothesis, that low certainty incidental emotions, compared to their high certainty counterparts, lead to utility overprediction and to lower forecasting error. Introduction Cognitive appraisals of emotion have been included in the state-of-the-art theory of emotion and decision-making (Lerner & Keltner, 2000; Lerner, Li, Valdesolo, & Kassam, 2015). For instance, Tiedens & Linton (2001) discuss how happiness involves appraisals of high certainty, and sadness involves appraisals of low certainty. In terms of forecasting, systematic processing is generally considered to lead to less forecasting error compared to heuristic processing. Tiedens & Linton (2001) argue that, if accuracy is the ultimate goal the individual needs to rely on more thoughtful processes. Seeking a state of certainty is more cognitively engaging and requires more cognitive resources. But how do people predict future utilities in the first place? Theoretical background Kahneman & Thaler (2006) analyze forecasting as a two-step procedure, encompassing a current prediction as well as a future event. Breaking down the present and future situation allows researchers to assess accuracy and detect how errors occur. Kahneman & Snell (1992) report that people tend to underpredict future utilities. Typically, the experienced utility is higher (i.e. more liked or less disliked) compared to the earlier prediction. In the present paper we argue that emotional uncertainty leads to utility overprediction and thus reduces forecasting error. This hypothesis is in line with the Appraisal-Tendency Framework (ATF-overview in Lerner et al., 2015). According to the ATF, an emotion may trigger a cognitive predisposition to assess future events in line with the central appraisal dimensions that triggered that emotion. Such appraisals provide a perceptual schema for interpreting subsequent situations. In the context of the present research, the certainty-uncertainty cognitive appraisal is hypothesized to trigger a predisposition that affects the utility prediction mechanism and leads to utility overprediction. This hypothesis is also in line with the uncertainty intensification hypothesis (Bar-Anan, Wilson, & Gilbert, 2009), according to which the uncertainty of experienced emotions makes unpleasant events more unpleasant and pleasant events more pleasant. The present research examines an Uncertainty-Prediction Asymmetry (UPA) hypothesis. In three experimental studies we test the hypotheses that low certainty incidental emotions, compared to their high certainty counterparts, lead to utility overprediction (H1) and to lower forecasting error (H2). Emotional certainty, as an appraisal dimension of emotions, is expected to create a prediction asymmetry through its effect on both predicted utility and forecasting error. The mediating role of heuristic processing in the relationship between emotional certainty and forecasting error is also investigated. Experiment 1 The first experiment examines the hypothesis that low emotional certainty leads to utility overprediction (H1). Eighty postgraduate students were randomly assigned to a high emotional certainty (disgust) vs. a low emotional certainty (fear) condition. Emotion induction involved exposure to pretested video clips (see Han et al., 2012). Following this manipulation, the experimental utility (a small candy bar) was distributed and participants were encouraged to consume it (see Kahneman & Snell, 1992). They were then asked to report on 13-point scales how much they liked the utility and to predict how much they would like it in the future consumption occasion (a week later). The results revealed a significant difference in predicted utility between the high (M = 2.22, SD = 1.33) and low (M =3.65, SD = 1.37) emotional certainty conditions (F = 4.43, p = 0.04, partial eta squared = 0.10). Experiment 2 The second experiment includes a “future event”, that is measures of the utility that was originally predicted, in order to also estimate forecasting error. The experiment therefore tests if (a) the main effect of emotional uncertainty on predicted utility is confirmed (H1) and (b) there is a significant main effect of emotional uncertainty on forecasting error (H2). In addition, this experiment examines whether these effects are independent of the valence appraisal dimension of emotions. Given that Experiment 1 involved two negatively valenced emotions, emotional valence (positive vs. negative) was included in the experimental design. Seventy three postgraduate students participated in a five-consecutive-days experiment. During the first day, participants were randomly assigned to a fear (negative valence, low certainty), disgust (negative valence, high certainty), hope (positive valence, low certainty) or happiness (positive valence, high certainty) condition. Specifically, participants were asked to report an experience in which they had felt this particular emotion through an Autobiographical Emotional Memory Task (AEMT) (as in Smith & Ellsworth, 1985). Following this experimental manipulation, the experimental utility (a small chocolate bar) was distributed and they were again encouraged to consume. Subsequently, they were asked to rate how much they liked and how much they would like the utility on the fifth day. Depth of processing was assessed with four items (α=0.77), adjusted from Griffin et al. (2002). Specifically, these items measured the heuristic processing performed during the prediction process. Participants were contacted again on each of the remaining four days and were asked to consume the utility and to complete a short questionnaire (comprising ratings of the consumption experience and of the predicted utility on the fifth day). The results reported here involve only the data obtained on the first and final day of the experiment, and the forecasting error was estimated as the difference between the experienced utility of the last day and the predicted utility of the first day. In line with hypothesis H1, emotional certainty had a significant main effect on predicted utility (F = 6.18, p = 0.002, partial eta squared = 0.08). Specifically, predicted utility in the low emotional certainty condition was higher (M = 2.69, SD = 1.09), compared to that of the high certainty condition (M = 0.78, SD = 1.66). There was no significant interaction effect between certainty and valence. These findings provide further support for our H1 and indicate that emotional certainty influences utility prediction irrespective of the valence of incidental emotions. Moreover, a significant main effect of certainty on forecasting error was observed (F = 4.16, p = 0.045, partial eta squared = 0.06). Forecasting error was lower in the low certainty condition (M = 0.59, SD = 1.28) compared to the high certainty condition (M = 2.19, SD = 1.48). There was no significant interaction effect. Moreover, a mediation analysis revealed that heuristic processing mediated the effect of certainty on forecasting error (p**<0.05). Experiment 3 The previous two experiments indicate that the effects of incidental emotional states on predicted utility and forecasting error may be due to the certainty-appraisal dimension of these emotional states. A possible criticism and an inherent limitation of Experiments 1 and 2 might lie on the possibility that these effects are not independent of the other appraisal dimensions. This is related to a key methodological issue. In Experiments 1 and 2, the induced emotions were different in terms of certainty or uncertainty, but these emotions might have differed in other ways and across other appraisal dimensions as well. To eliminate this possibility and to strengthen our argument, we employ here a manipulation of the certainty appraisal of the same emotion. We therefore compare predicted utility and forecasting error in the same emotional state under conditions of low and high certainty. In Experiments 1 and 2 the emotions induced are strong representatives of each side of the certainty appraisal dimension. However, emotions located in the middle of this dimension provide an interesting opportunity since they might allow us to compare their effects when they are associated with lower or higher levels of certainty. In this experiment we have chosen to focus on the emotional state of sadness. Sadness was selected because it is near the middle of the certainty-uncertainty dimension (Smith & Ellsworth, 1985). Similar manipulations of sadness have been reported in the literature (Tiedens & Linton, 2001). Sixty postgraduate students were randomly assigned to a low vs. high certainty sadness condition. High certainty participants were asked to recall and report an experience or event in which they had felt high certainty sadness (i.e. during which they understood what was happening and could predict what was going to happen next), through an Autobiographical Emotional Memory Task (AEMT) as in Experiment 2. Similarly, low certainty participants were asked to recall and report an event or experience that had generated low certainty sadness. Following the experimental manipulation, the experimental utility (a small chocolate bar) was served. Participants were again encouraged to consume some of it and were asked to complete 13-point ratings of how much they liked it and how much they would like it in the future occasion (a week later). Eight items (α=0.81), adapted from Griffin et al. (2002), measured the heuristic processing performed during the prediction process. Participants also completed ten items adjusted from PANAS questionnaire (Watson et al., 1988). A week later, participants consumed the utility and completed a short questionnaire. The results revealed a significant main effect of certainty on the predicted utility (F = 4.00, p = 0.05, partial eta squared = 0.06). Predicted utility in the low certainty sadness condition was higher (M = 4.21, SD = 1.55) compared to that of the high certainty condition (M = 3.35, SD = 1.78). A significant main effect of certainty on forecasting error was also observed (F = 5.04, p = 0.03, partial eta squared = 0.10). Forecasting error in the low certainty condition (M = -0.10, SD = 1.65) was lower compared to that of the high certainty condition (M = 1.02, SD = 1.81). A mediation analysis revealed that heuristic processing again mediated the effect of certainty on forecasting error (p**<0.05). Conclusion The contribution of this research is mostly highlighted by the counter-intuitive findings that lower certainty emotions lead to judgment with higher accuracy, as well as to an overprediction of utilities, related to their certainty counterparts. Therefore, the current findings provide support for the proposed Uncertainty-Prediction dual Asymmetry (UPA) hypothesis. Future research needs to corroborate these findings, to clarify the mechanisms underlying the observed asymmetry and to identify boundary conditions.

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company’s profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.