Outcome bias in self-evaluations: Quasi-experimental field evidence from Swiss driving license exams

Exploiting a quasi-experimental field setting, we examine whether people are outcome biased when self-evaluating their past decisions. Using data from Swiss driving license exams, we find that candidates who narrowly passed the theoretical driving exam are significantly less likely to pass the subsequent practical driving exam – which is taken several months after the theoretical exam – than those who narrowly failed. Those candidates who passed the theoretical exam on their first attempt receive more objections regarding their momentary, on-the-spot decisions in the practical exam, consistent with the idea that the underlying behavioral difference is worse preparation

Are Expectations Misled by Chance? Quasi-Experimental Evidence from Financial Analysts

We examine whether finance professionals deviate from Bayes’ theorem on the processing of nondiagnostic information when forecasting quarterly earnings. Using field data from sell-side financial analysts and employing a regression discontinuity design, we find that analysts whose forecasts have barely been met become increasingly optimistic relative to when their forecasts have barely been missed. This result is consistent with an update of analysts’ expectations after observing uninformative performance signals. Our results also suggest that this behavior leads to significantly worse forecasting accuracy in the subsequent quarter. We contribute to the literature by providing important field evidence of expectation formation under uninformative signals

On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation

Uncertainty estimation methods are expected to improve the understanding and quality of computer-assisted methods used in medical applications (e.g., neurosurgical interventions, radiotherapy planning), where automated medical image segmentation is crucial. In supervised machine learning, a common practice to generate ground truth label data is to merge observer annotations. However, as many medical image tasks show a high inter-observer variability resulting from factors such as image quality, different levels of user expertise and domain knowledge, little is known as to how inter-observer variability and commonly used fusion methods affect the estimation of uncertainty of automated image segmentation. In this paper we analyze the effect of common image label fusion techniques on uncertainty estimation, and propose to learn the uncertainty among observers. The results highlight the negative effect of fusion methods applied in deep learning, to obtain reliable estimates of segmentation uncertainty. Additionally, we show that the learned observers' uncertainty can be combined with current standard Monte Carlo dropout Bayesian neural networks to characterize uncertainty of model's parameters.Comment: Appears in Medical Image Computing and Computer Assisted Interventions (MICCAI), 201

Replication: Do coaches stick with what barely worked? Evidence of outcome bias in sports

We replicate the finding of Lefgren et al. (2015) showing that professional basketball coaches in the NBA discontinuously change their starting lineup more often after narrow losses than after narrow wins. This result is consistent with outcome bias because such narrow outcomes are conditionally uninformative. As our paper shows, this pattern is not restricted to the NBA; we also find evidence of outcome bias in the top women’s professional basketball league and college basketball. Finally, we show that outcome bias in coaching decisions generalizes to the National Football League (NFL). We conclude that outcome bias is credible and robust, although it has weakened over time in some instances

May Bad Luck Be Without You: The Effect of CEO Luck on Strategic Risk-taking

We investigate how luck, namely, changes in a firm's performance beyond the CEO's control, affects strategic risk-taking. Fusing upper echelons theory with insights from psychology and behavioral strategy research, we hypothesize that there is a positive association between luck and strategic risk-taking and that this effect is stronger for bad luck than for good luck. We further argue that these effects vary depending on whether CEOs have experienced negative events earlier in their professional careers. Measuring luck as the exogenous component of recent firm performance, we show empirically that CEOs react to bad luck by adopting more conservative risk-taking policies while showing no reactions to good luck. This effect predictably varies with the strength of bad luck signals, and it is stronger for CEOs who have experienced negative events during their professional careers. We contribute to the literature by providing the first evidence on the role of luck in corporate strategic risk-taking

Indocyanine-based near-infrared lymphography for real-time detection of lymphatics in a cat with multiple mast cell tumours

Case summary An 11-year-old female domestic shorthair cat was presented with cutaneous mast cell tumours (MCTs) localised at the right temporal region, the left buccal region and on the third digit of the right thoracic limb. Staging was negative and locoregional lymph nodes appeared normal, based on clinical findings. During surgery, real-time indocyanine green (ICG)-based lymphography was performed to detect the cutaneous draining pattern of all the primary MCTs. ICG was injected intracutaneously in four quadrants around each tumour, and a clear lymphogram was visible shortly after injection. Using near-infrared lymphography (NIR-L) for guidance, all lymphadenectomies were performed in 12 mins or less, with a maximal incision length of 3.5 cm. The smallest resected node was 0.9 cm in diameter. All MCTs were classified as low-grade cutaneous MCT. All four ICG-positive lymph nodes were considered premetastatic or metastatic. The only ICG-negative resected node was also negative for tumour cells. No complications related to NIR-L were recorded. Relevance and novel information This is the first description of NIR-L in a cat with MCT. Application was straightforward and ICG enrichment only occurred in the metastatic nodes, suggesting correct identification of lymphatic draining patterns. Of note, as previously described in dogs, we did detect nodal metastasis, despite low-grade primary tumours. The clinical relevance should be evaluated in future studies

Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation

Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlying model. Reliable uncertainties are particularly interesting for safety-critical computer-assisted applications in medicine, e.g., neurosurgical interventions and radiotherapy planning. We propose an uncertainty-driven sanity check for the identification of segmentation results that need particular expert review. Our method uses a fully-convolutional neural network and computes uncertainty estimates by the principle of Monte Carlo dropout. We evaluate the performance of the proposed method on a clinical dataset with 30 postoperative brain tumor images. The method can segment the highly inhomogeneous resection cavities accurately (Dice coefficients 0.792 $\pm$ 0.154). Furthermore, the proposed sanity check is able to detect the worst segmentation and three out of the four outliers. The results highlight the potential of using the additional information from the model's parameter uncertainty to validate the segmentation performance of a deep learning model.Comment: Appears in Medical Imaging with Deep Learning (MIDL), 201