Prof. Dr.

Andreas Graefe

Portrait Andreas Graefe

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.

Lehrgebiet:

Management

Zu Management:
Der Campus München in Nymphenburg, mit Blick auf die Marienkirche
Campus:

München

Zum Standort
Fakultät:

Fakultät Business, Design, Technologie

Publikationen

2021

Graefe, A.: Of Issues and Leaders: Forecasting the 2020 US Presidential Election

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2021

Armstrong, J., & Graefe, A.: The PollyVote Popular Vote Forecast for the 2020 US Presidential Election

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2020

Graefe, A., & Bohlken, N.: Automated Journalism: A Meta-Analysis of Readers’ Perceptions of Human-Written in Comparison to Automated News

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2019

Graefe, A.: Accuracy of German federal election forecasts, 2013 & 2017

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2019

Graefe, A. & Ellert, G.: Good referees are not nice. Personality effects on football referee decision-making

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2019

Graefe, A. & Ellert, G.: Influence of expertise and game time on football referee decisions

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2019

Graefe, A., Bauer, A. & Ellert, G.: When Referees See Red: Decision Accuracy of Football Referees

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2019

Graefe, 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

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2018

Haim, M., Graefe, A.: Automatisch interessant? Der Einfluss von Involvement auf die Wahrnehmung computergenerierter Texte

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2018

Haim, M., Graefe, A. & Brosius, H.-B.: Burst of the filter bubble? Effects of personalization on the diversity of Google News

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2018

Haim, M., Graefe, A., Brosius, H. B.: Wertschöpfung mithilfe von Algorithmen: Ansatzpunkte für die Veränderung von Geschäftsmodellen durch Computational Journalism

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2018

Graefe, A.: Predicting elections: Experts, polls, and fundamentals

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2018

Graefe, A. et al.: Readers’ perception of computer-generated news: Credibility, expertise, and readability

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2018

Green, K. C., Graefe, A. & Armstrong, J. S.: Testing the Predictive Validity of Multiple Regression Analysis

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2018

Haim, M. & Graefe, A.: Automatisierter Journalismus: Anwendungsbereiche, Formen und Qualität

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2018

Haim, M., Graefe, A. & Brosius, H.-B.: Ansatzpunkte für die Veränderung von Geschäftsmodellen durch Computational Journalism

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2017

Graefe, A. et al.: A Recap of the 2016 Election Forecasts

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2017

Graefe, A.: Political markets

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2017

Haim, M., Graefe, A.: Automated news: Better than expected?

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2017

Graefe, A., Armstrong, J. S., Jones, R. J. J., Cuzán, A. G.: Assessing the 2016 U.S. presidential election popular vote forecasts

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2017

Graefe, A.: The PollyVote’s long-term forecast for the 2017 German federal election

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2017

Graefe, A.: Prediction market performance in the 2016 U.S. presidential election

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2016

Graefe, A. et al.: Persuasion principles index: Ready for pretesting advertisement

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2016

Graefe, A.: Guide to Automated Journalism

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2016

Graefe, A.: Forecasting proportional representation elections from non-representative expectation surveys

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2016

Graefe, A. et al.: Predictive validity of evidence-based persuasion principles

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2016

Graefe, A. et al.: The PollyVote forecast for the 2016 American Presidential Election

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2016

Graefe, A.: Issue-handling beats leadership: Issues and Leaders model predicts Clinton will defeat Trump

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2015

Graefe, A.: German election forecasting: Comparing and combining methods for 2013

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2015

Graefe, A.: Improving forecasts using equally weighted predictors

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2015

Graefe, A. et al.: Aktuelles Stichwort: Computational Journalism

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2015

Armstrong, J. S., Green, K. C. & Graefe, A.: Golden Rule of Forecasting rearticulated: Forecast unto others as you would have them forecast unto you

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2015

Armstrong, J. S., Green, K. C. & Graefe, A.: Golden Rule of Forecasting: Be conservative

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2015

Graefe, A., Küchenhoff, H., Stierle, V. & Riedl, B.: Limitations of ensemble Bayesian model averaging for forecasting social science problems

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2015

Graefe, A.: Accuracy gains of adding vote expectation surveys to a combined forecast of US presidential election outcomes

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2014

Graefe, A. et al.: Accuracy of combined forecasts for the 2012 Presidential Elections: The PollyVote

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2014

Graefe, A. et al.: Combining forecasts: An application to elections

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2014

Graefe, A. & Armstrong, J. S.: Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue

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2014

Graefe, A.: Accuracy of vote expectation surveys in forecasting elections

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2013

Graefe, A., Green, K. C. & Armstrong, J. S.: Forecasting

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2013

Graefe, A. et al.: Combined forecasts of the 2012 election: The PollyVote

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2013

Graefe, A. & Armstrong, J. S.: Forecasting elections from voters' perceptions of candidates' ability to handle issues

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2013

Graefe, A.: Issue and leader voting in U.S. presidential elections

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