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.

콘크리트의 크리프와 건조수축에 의한 시간에 따른 프리스트레스 장기예측의 정확성은 프리스트레스트 큰크리트 교량과 같은 사회 기반 시설의 유지 관리 및 보수 결정 측면에서 매우 중요한 역할을 한다. 본 논문에서는 프리스트레스트 콘크리트 교량의 프리스트레스 장기예측의 불확실성 감소, 즉 예측의 정확성 향상을 위하여 현장 계측치를 이용하여 베이시안(Bayesian)통계기법을 도입하는 예측기법을 제안하였다. 베이시안 해석시 사전 확률분포는 콘크리트의 크리프와 건조수축의 확률 특성을 고려하여 나타내며, 우도 함수(likelihood function)는 현장에서의 계측치를 사용하여 나타내었다. 시간에 따른 구조적 거동 시스템으로부터의 지속적인 계측 기록은 베이시안 지식 기반에서의 확률분포를 업데이팅 하기 위하여 사용되며, 사후 확률분포는 사전확률분포와 우도 함수를 조합하여 획득한다. 실제로 가설되고 있는 프리스트레스트 콘크리트 박스 거더 교량으로부터 계측된 프리스트레스 힘의 수치 예제해석을 통하여 제안 기법의 적용성을 제시하였다.

구조물의 손상예측정확도를 모텔불확실성의 함수로 산정하는 방법론이 제시되었다. 먼저， 구조물의 손상발 생위치와 크기를 결정할 수 있는 알고리즘이 요약되고 모델불확실성과 손상발견정확도를 측정하는 방법들이 제시되었다. 다음으로， 실폰구조물의 손상발견정확도에 미치는 모델불확실성의 영향을 산정하는 방법론이 제 시되었다. 마지막으로， 한개의 진동모드가 측정된 Plate-Girder 교량올 사용하여 이같은 산정방법론의 적합 성이 예증되었다.

본 연구에서는 충주댐 유역에 대해 앙상블 유량예측기법의 강우-유출 모델 매개변수, 입력자료에 따른 불확실성 분석을 수행하였다. 앙상블 유량예측기법으로는 ESP (Ensemble Streamflow Prediction) 기법과 BAYES-ESP (Bayesian-ESP) 기법을 활용하였으며, 강우-유출 모델로는 ABCD를 활용하였다. 모델 매개변수에 따른 불확실성 분석은 GLUE (Generalized Likelihood Uncertainty Estimation) 기법을 적용하였으며, 입력자료에 따른 불확실성 분석은 유량예측 앙상블에 활용되는 기상시나리오의 기간에 따라 수행하였다. 연구결과 앙상블 유량예측 기법은 입력자료 보다 모델 매개변수의 영향을 크게 받았으며, 20년 이상의 관측 기상자료가 확보되었을 때 활용하는 것이 적절하였다. 또한 BAYES-ESP는 ESP에 비해 불확실성을 감소시킬 수 있는 것으로 나타났다. 본 연구는 불확실성 분석을 통해 앙상블 유량예측기법의 특징을 규명하고 오차의 원인을 분석하였다는 점에서 가치가 있다고 판단된다.

가뭄의 피해를 줄이기 위해서는 시기적절한 용수관리와 지역주민의 절수 유도가 필요하며, 이를 위해서는 가뭄의 현황 및 전망에 대한 정보가 무 엇보다 중요하다. 특히 생·공용수를 공급하는 다목적댐의 경우 저수량에 대한 향후 전망은 용수관리를 위한 가장 중요한 정보이다. 이에 본 연구에 서는 핵밀도함수를 활용하여 유입량의 불확실성을 고려한 확률론적 저수량 예측 모형을 구축하고, 그 적용성과 활용성을 분석하였다. 확률론적 저 수량 예측 모형은 현재의 저수량을 기준으로 시간의 변화에 따른 저수량을 확률적으로 예측할 수 있다. 이를 통해 현재의 가뭄상황에서 향후 저수 량의 변화 양상을 파악하여 중장기적인 대응이 가능하고 특정시점의 목표 저수량을 달성하기 위한 용수 비축량을 산정할 수 있어 용수관리에 관한 의사결정을 위한 도구로 활용이 가능할 것으로 판단된다.

SWAT (Soil and Water Assessment Tool) 모형의 적용성 검증을 위해서는 매개변수 민감도 분석 및 검·보정, 예측 불확실성 분석을 필요로 한다. 최근 SWAT 모형의 불확실성을 분석하기 위한 다양한 기법들이 개발되었는데, 본 연구는 충주댐 유역(6,581.1 km2)을 대상으로 유역출구점의 실측 일 유출량 자료(1998～2003)를 바탕으로 SWAT 모형의 유출관련 매개변수에 대한 불확실성 분석을 실시하였다. 이때 사용된 분석 기법으로는 SUFI2 (Sequential Uncertainty FItting algorithm ver.2), GLUE (Generalized Likelihood Uncertainty Estimation), ParaSol (Parameter Solution) 등을 적용하였다. 이러한 기법은 모두 SWAT-CUP (SWAT-Calibration Uncertainty Program; Abbaspour et al., 2007) 모형에 탑재되어있으며, 모형의 결과로써 검·보정, 매개변수의 민감도 분석, 각종 목적 함수 및 불확실성의 범위 등이 자동으로 산출되므로 모형의 사용자가 불확실성 평가 기법의 분석 및 비교를 손쉽게 할 수 있다. 그 결과 대표적인 목적 함수인 결정 계수(R2; Legates and McCabe, 1999)와 NS (Nash and Sutcliffe, 1970) 모형 효율은 모두 0.67에서 0.92 사이의 값을 나타내어 대체적으로 모의가 잘 이루어졌음을 알 수 있었다. 그러나 불확실성의 범위를 나타내는 지표인 p-factor 및 r-factor 에서는 평가 기법 별로 그 차이가 확연하게 드러났다. 여기서 p-factor는 불확실성 범위에 실측치가 포함되는 비율이며, r-factor는 불확실성의 상대적인 범위로 각각 1과 0에 가까울수록 모의 기법의 성능이 우수함을 의미한다. 세 가지 알고리듬 중에서 SUFI2의 p-factor가 약 0.79로 가장 높게 나타났으며, ParaSol의 r-factor가 0.03으로 가장 작게 나타났다. 본 연구의 결과는 SWAT 모형을 이용한 수문 모의에서 수문분석에 따른 예측결과의 불확실성을 정량적으로 평가함으로서, 모형의 적용성 평가 및 모의결과의 신뢰성 확보에 근거자료로 활용이 가능할 것으로 판단된다.

모형의 구조, 모델링에 사용되는 자료, 매개변수 등에 포함된 다양한 불확실성 원인들은 수문모의 및 예측결과에 있어 불확실성을 야기한다. 본 연구에서는 강우-유출 및 강우-유사유출 모의가 가능한 분포형 강우-유사-유출 모형을 용담댐 상류유역인 천천유역에 적용하여 수문곡선 및 유사량곡선의 재현성을 평가하고, 다중최적화기법인 MOSCEM을 이용하여 강우-유출 모듈, 강우-유사유출 모듈의 매개변수를 독립적으로 보정한 경우(Case I과 II), 그리고 두 모듈