Refine
Year of publication
Document Type
- Article (195) (remove)
Has Fulltext
- yes (195)
Is part of the Bibliography
- no (195)
Keywords
- Machine learning (4)
- Retirement (4)
- Artificial intelligence (3)
- Household finance (3)
- Ordoliberalism (3)
- Walter Eucken (3)
- machine learning (3)
- 401(k) plan (2)
- Aesthetics (2)
- Annuity (2)
Institute
- Wirtschaftswissenschaften (195) (remove)
Die Wettbewerbsfähigkeit der deutschen Wirtschaft steht vor gewaltigen Herausforderungen. Traditionell starke Sektoren wie die Automobilindustrie oder der Maschinenbau befinden sich angesichts disruptiver Veränderungen durch neue Technologien, den Kampf gegen den Klimawandel und veränderte regulatorische Rahmenbedingungen in einer Umbruchphase. Zahlreiche Industriezweige wandeln sich durch den Einsatz von Künstlicher Intelligenz zu „Smart Industries“. Gleichzeitig gewinnt die Kompetenz in Querschnittstechnologien wie Cloud Computing oder Cyber Security an Bedeutung, da diese den effektiven Einsatz von Künstlicher Intelligenz erst ermöglichen. Eine Analyse der Wettbewerbsposition der deutschen Wirtschaft zeigt auf, dass in manchen Zukunftsfeldern ein erheblicher Nachholbedarf besteht.
Auf die Fragen kommt es an: "Woher kommt der Mensch? wo will er hin? – und warum um alles in der Welt ist er da nicht geblieben?" Der Meister zirkulärer Sinnsuche hat als Fragender seine beste Rolle gefunden und damit den postheroischen Typus Mensch erschaffen, der in der Vieldeutigkeit der Welt erleichtert seinen Unfrieden findet: damit, dass Pazifisten Kriege verteidigen, dass die Außerparlamentarischen eine Partei gründen, dass die Konservativen die interessanteren Zeitungen machen und die Komik zur wirksamsten Waffe gegen Dummheit und Schmerz geworden ist. Matthias Beltz hat beiläufig mit Lebensweisheiten und assoziativ aufgetürmtem Scharfsinn nicht nur seine Fragen bewaffnet, in denen gewagte Antworten ihren vitalen Keim austreiben, sondern auch das Misstrauen gesät gegenüber politisch korrekten, nachgeplapperten und smarten Antworten. ...
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.
Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it comes to the “known unknown” or even the “unknown unknown.” While machine learning has been tested successfully in the regime of the “known,” heuristics typically provide better results for an active operational risk management (in the sense of forecasting). However, precursors in existing data can open a chance for machine learning to provide early warnings even for the regime of the “unknown unknown.”
A commentary on Commentary: Aesthetic Pleasure versus Aesthetic Interest: The Two Routes to Aesthetic Liking by Consoli, G. (2017). Front. Psychol. 8:1197. doi: 10.3389/fpsyg.2017.01197
In his commentary on the paper “Aesthetic Pleasure versus Aesthetic Interest: The Two Routes to Aesthetic Liking,” authored by Jan R. Landwehr and myself (Graf and Landwehr, 2017), Consoli (2017) deplores two aspects of our paper. First, an inadequate definition and operationalization of the key constructs aesthetic pleasure, aesthetic interest, and aesthetic liking, respectively aesthetic attractiveness. Second, the conclusions drawn from our empirical studies. While I acknowledge that one may have a different theoretical perspective on aesthetic perception and evaluation, it appears that Consoli's (2017) commentary does not even address the empirical data of our studies but only our theoretical assumptions and definitions. In the following, I will address Consoli's (2016, 2017) arguments in more detail, and I will corroborate our theoretical reasoning with the empirical data of our studies (Graf and Landwehr, 2017).....
Inhibitory interneurons govern virtually all computations in neocortical circuits and are in turn controlled by neuromodulation. While a detailed understanding of the distinct marker expression, physiology, and neuromodulator responses of different interneuron types exists for rodents and recent studies have highlighted the role of specific interneurons in converting rapid neuromodulatory signals into altered sensory processing during locomotion, attention, and associative learning, it remains little understood whether similar mechanisms exist in human neocortex. Here, we use whole-cell recordings combined with agonist application, transgenic mouse lines, in situ hybridization, and unbiased clustering to directly determine these features in human layer 1 interneurons (L1-INs). Our results indicate pronounced nicotinic recruitment of all L1-INs, whereas only a small subset co-expresses the ionotropic HTR3 receptor. In addition to human specializations, we observe two comparable physiologically and genetically distinct L1-IN types in both species, together indicating conserved rapid neuromodulation of human neocortical circuits through layer 1.
Although existing research has established that aesthetic pleasure and aesthetic interest are two distinct positive aesthetic responses, empirical research on aesthetic preferences usually considers only aesthetic liking to capture participants’ aesthetic response. This causes some fundamental contradictions in the literature; some studies find a positive relationship between easy-to-process stimulus characteristics and aesthetic liking, while others suggest a negative relationship. The present research addresses these empirical contradictions by investigating the dual character of aesthetic liking as manifested in both the pleasure and interest components. Based on the Pleasure-Interest Model of Aesthetic Liking (PIA Model; Graf and Landwehr, 2015), two studies investigated the formation of pleasure and interest and their relationship with aesthetic liking responses. Using abstract art as the stimuli, Study 1 employed a 3 (stimulus fluency: low, medium, high) × 2 (processing style: automatic, controlled) × 2 (aesthetic response: pleasure, interest) experimental design to examine the processing dynamics responsible for experiencing aesthetic pleasure versus aesthetic interest. We find that the effect of stimulus fluency on pleasure is mediated by a gut-level fluency experience. Stimulus fluency and interest, by contrast, are related through a process of disfluency reduction, such that disfluent stimuli that grow more fluent due to processing efforts become interesting. The second study employed product designs (bikes, chairs, and lamps) as stimuli and a 2 (fluency: low, high) × 2 (processing style: automatic, controlled) × 3 (product type: bike, chair, lamp) experimental design to examine pleasure and interest as mediators of the relationship between stimulus fluency and design attractiveness. With respect to lamps and chairs, the results suggest that the effect of stimulus fluency on attractiveness is fully mediated by aesthetic pleasure, especially in the automatic processing style. Conversely, disfluent product designs can enhance design attractiveness judgments due to interest when a controlled processing style is adopted.
The purpose of the data presented in this article is to use it in ex post estimations of interest rate decisions by the European Central Bank (ECB), as it is done by Bletzinger and Wieland (2017) [1]. The data is of quarterly frequency from 1999 Q1 until 2013 Q2 and consists of the ECB's policy rate, inflation rate, real output growth and potential output growth in the euro area. To account for forward-looking decision making in the interest rate rule, the data consists of expectations about future inflation and output dynamics. While potential output is constructed based on data from the European Commission's annual macro-economic database, inflation and real output growth are taken from two different sources both provided by the ECB: the Survey of Professional Forecasters and projections made by ECB staff. Careful attention was given to the publication date of the collected data to ensure a real-time dataset only consisting of information which was available to the decision makers at the time of the decision.