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David Schindler

Researcher at Tilburg University

Publications -  20
Citations -  178

David Schindler is an academic researcher from Tilburg University. The author has contributed to research in topics: Computer science & Incentive. The author has an hindex of 6, co-authored 14 publications receiving 114 citations. Previous affiliations of David Schindler include Ludwig Maximilian University of Munich.

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Unleashing Animal Spirits - Self-Control and Overpricing in Experimental Asset Markets

TL;DR: In this article, the causal relationship between self-control and systematic overpricing on financial markets was investigated and it was shown that low individual self control could transmit into irrational exuberance in markets.
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Risk, time pressure, and selection effects

TL;DR: In this paper, the ability to cope with time pressure varies significantly across decision makers, leading to selected subgroups that differ in terms of their observed behaviors and personal traits, and measures of cognitive ability and intellectual efficiency jointly predict individuals' decision quality and ability to keep their decision strategy under time pressure.
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Shocking racial attitudes : Black G.I.s in Europe

TL;DR: The authors showed that the presence of African American soldiers in the UK during World War II reduced anti-minority prejudice, a result of the positive interactions which took place between soldiers and the local population.
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Risk, Time Pressure, and Selection Effects

TL;DR: In this article, the authors find that the ability to cope with time pressure varies significantly across decision makers, leading to selected subgroups that differ in terms of their observed behaviors and personal traits, and measures of cognitive ability and intellectual efficiency jointly predict individuals' decision quality and ability to keep their decision strategy under time pressure.
Journal ArticleDOI

The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central

TL;DR: This work provides a large-scale analysis of software usage and citation practices facilitated through an unprecedented knowledge graph of software mentions and affiliated metadata generated through supervised information extraction models trained on a unique gold standard corpus and applied to more than 3 million scientific articles.