Die Psycho-Logik der Akzeptanz von Wirtschafts- und Sozialreformen

In der Ökonomie und der Politik gibt es eine Vielzahl an Wirtschafts- und Sozialreformen und -maßnahmen, die in der Diskussion stehen. Jedoch beschäftigen sich nicht nur Politiker und Ökonomen mit möglichen Implikationen und Konsequenzen verschiedener Reformmaßnahmen; auch ökonomische Laien, die nicht über ausgewiesenes ökonomisches Fachwissen verfügen, entwickeln Meinungen zu politischen Reformmaßnahmen und haben Vorstellungen darüber, wie "die Wirtschaft" funktioniert. Das Ziel dieser Arbeit ist herauszufinden, wie verschiedene Gruppen der Bevölkerung unterschiedliche Maßnahmen beurteilen, wie sie den Staat, andere Institutionen und die Arbeits- und Lebensbedingungen wahrnehmen und zu analysieren, welchen impliziten Theorien sie hierbei folgen. Es wurde untersucht, wie ökonomische Laien (N=1141 Bürger Deutschlands) und ökonomische Experten (N=80 Professoren ökonomischer Universitätslehrstühle)verschiedene politische Reformmaßnahmen beurteilen. Die Ergebnisse zeigen substantielle Unterschiede zwischen den beiden Gruppen auf. Ökonomen beurteilen eine Reformmaßnahme dann als gut, wenn sie bestimmten ökonomischen Zielen dienlich zu sein scheint (z.B. wenn sie die Arbeitslosigkeit zu senken in der Lage ist oder Wirtschaftswachstum stimulieren kann). Auf der anderen Seite konzentrieren sich ökonomische Laien auf die Frage, ob die einzelne Reformmaßnahme fair und gerecht erscheint. Es kann gezeigt werden, dass Laien in diesen Urteilen beeinflusst werden durch eine Reihe an kognitiven Verzerrungen und bestimmten moralischen Prinzipien folgen(z.B. fixed-pie bias, status quo bias, do-no-harm Heuristik, etc.). Verschiedene Gründe für diese unterschiedlichen Perspektiven von Ökonomen und Nicht-Ökonomen werden diskutiert

Unterschiede im Denken zwischen Ökonomen und Laien - Erklärungsansätze zur Verbesserung der wirtschaftspolitischen Beratung

Recent findings in Economic Psychology on the thinking and judgment of economic laymen can help to improve economic policy advice. First, based on psychological theories and empirical findings, the notion of the Homo oeconomicus is challenged. The economic situation is judged by laymen using implicit theories and biases, which are not in line with neoclassical economics. Then, the relevance of these theories and heuristics is shown empirically by the systematically different opinions of economists and non-economists on economic policy measures like minimum wages and maximum salaries. Conclusions for policy advisors complement the analysis. Copyright 2009 die Autoren Journal compilation 2009, Verein für Socialpolitik und Blackwell Publishing Ltd.

Efficiency versus fairness: The evaluation of labor market policies by economists and laypeople

The present study examines the criteria used by economic laypeople (N = 380 German citizens) and economists (N = 80 professors or postgraduates in economics) in judging reform measures as illustrated by policies of governmental labor market intervention policies. Results reveal substantial differences between economists and laypeople. Most laypersons favored labor market interventions and regarded them as both, efficient and fair. In contrast, most economists opposed labor market interventions and regarded them as both, inefficient and unfair. Using structural equation modelling we further show that the relative importance of efficiency and fairness differs between both groups. While economists base the acceptance of a certain reform measure mainly on its perceived efficiency, laypersons mainly focus on its perceived fairness. Theoretical and practical implications of our results are discussed.Fairness Efficiency Reform measure Cognitive biases Moral principles

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd