Andreas Graefe ist diplomierter Volkswirt und Wirtschaftsinformatiker (Universität Regensburg) und promovierte an der Universität Karlsruhe (TH) in Wirtschaftswissenschaften. Nach seiner Promotion war bei Sky Deutschland tätig, hatte Forschungspositionen am Karlsruher Institut für Technologie (KIT) und der LMU München, und war Gastwissenschaftler an der University of Pennsylvania (Philadelphia) und der Columbia University (New York City). Seit 2015 ist er Professor für Management an der Hochschule Macromedia. Seine Forschungsschwerpunkte sind Forecasting, Decision-Making und Algorithmen.
Fakultät Business, Design, Technologie
Armstrong, J., & Graefe, A.: The PollyVote Popular Vote Forecast for the 2020 US Presidential Election
DetailsGraefe, A., & Bohlken, N.: Automated Journalism: A Meta-Analysis of Readers’ Perceptions of Human-Written in Comparison to Automated News
DetailsGraefe, A. & Ellert, G.: Good referees are not nice. Personality effects on football referee decision-making
DetailsGraefe, A. & Ellert, G.: Influence of expertise and game time on football referee decisions
DetailsGraefe, A., Bauer, A. & Ellert, G.: When Referees See Red: Decision Accuracy of Football Referees
DetailsGraefe, A., Green, K. C. & Armstrong, J. S.: Accuracy gains from conservative forecasting: Tests using variations of 19 econometric models to predict 154 elections in 10 countries
DetailsHaim, M., Graefe, A.: Automatisch interessant? Der Einfluss von Involvement auf die Wahrnehmung computergenerierter Texte
DetailsHaim, M., Graefe, A. & Brosius, H.-B.: Burst of the filter bubble? Effects of personalization on the diversity of Google News
DetailsHaim, M., Graefe, A., Brosius, H. B.: Wertschöpfung mithilfe von Algorithmen: Ansatzpunkte für die Veränderung von Geschäftsmodellen durch Computational Journalism
DetailsGraefe, A. et al.: Readers’ perception of computer-generated news: Credibility, expertise, and readability
DetailsGreen, K. C., Graefe, A. & Armstrong, J. S.: Testing the Predictive Validity of Multiple Regression Analysis
DetailsHaim, M. & Graefe, A.: Automatisierter Journalismus: Anwendungsbereiche, Formen und Qualität
DetailsHaim, M., Graefe, A. & Brosius, H.-B.: Ansatzpunkte für die Veränderung von Geschäftsmodellen durch Computational Journalism
DetailsGraefe, A., Armstrong, J. S., Jones, R. J. J., Cuzán, A. G.: Assessing the 2016 U.S. presidential election popular vote forecasts
DetailsGraefe, A.: Forecasting proportional representation elections from non-representative expectation surveys
DetailsGraefe, A.: Issue-handling beats leadership: Issues and Leaders model predicts Clinton will defeat Trump
DetailsArmstrong, J. S., Green, K. C. & Graefe, A.: Golden Rule of Forecasting rearticulated: Forecast unto others as you would have them forecast unto you
DetailsArmstrong, J. S., Green, K. C. & Graefe, A.: Golden Rule of Forecasting: Be conservative
DetailsGraefe, A., Küchenhoff, H., Stierle, V. & Riedl, B.: Limitations of ensemble Bayesian model averaging for forecasting social science problems
DetailsGraefe, A.: Accuracy gains of adding vote expectation surveys to a combined forecast of US presidential election outcomes
DetailsGraefe, A. et al.: Accuracy of combined forecasts for the 2012 Presidential Elections: The PollyVote
DetailsGraefe, A. & Armstrong, J. S.: Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue
DetailsGraefe, A. & Armstrong, J. S.: Forecasting elections from voters' perceptions of candidates' ability to handle issues
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