The Evolution of Overconfidence

Confidence is an essential ingredient of success in a wide range of domains ranging from job performance and mental health, to sports, business, and combat. Some authors have suggested that not just confidence but overconfidence-believing you are better than you are in reality-is advantageous because it serves to increase ambition, morale, resolve, persistence, or the credibility of bluffing, generating a self-fulfilling prophecy in which exaggerated confidence actually increases the probability of success. However, overconfidence also leads to faulty assessments, unrealistic expectations, and hazardous decisions, so it remains a puzzle how such a false belief could evolve or remain stable in a population of competing strategies that include accurate, unbiased beliefs. Here, we present an evolutionary model showing that, counter-intuitively, overconfidence maximizes individual fitness and populations will tend to become overconfident, as long as benefits from contested resources are sufficiently large compared to the cost of competition. In contrast, "rational" unbiased strategies are only stable under limited conditions. The fact that overconfident populations are evolutionarily stable in a wide range of environments may help to explain why overconfidence remains prevalent today, even if it contributes to hubris, market bubbles, financial collapses, policy failures, disasters, and costly wars.Comment: Supplementary Information include

Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders

Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for training. In this work, we introduce a novel multi-channel, multi-resolution convolutional auto-encoder neural network that works on raw time-domain signals to determine appropriate multi-resolution features for separating the singing-voice from stereo music. Our experimental results show that the proposed method can achieve multi-channel audio source separation without the need for hand-crafted features or any pre- or post-processing

Do Teachers’ Race, Gender, and Ethnicity Matter? Evidence From the National Education Longitudinal Study of 1988

Using data from the National Educational Longitudinal Study of 1988 (NELS), the authors find that the match between teachers\u27 race, gender, and ethnicity and those of their students had little association with how much the students learned, but in several instances it seems to have been a significant determinant of teachers\u27 subjective evaluations of their students. For example, test scores of white female students in mathematics and science did not increase more rapidly when the teacher was a white woman than when the teacher was a white man, but white female teachers evaluated their white female students more highly than did white male teachers