Publications

Most of my papers are available in full text at ResearchGate.

Peer-reviewed     Book chapters/Editor reviewed     Monographs     Other writing

Peer-reviewed

  1. Graefe, A. (2017). The PollyVote’s long-term forecast for the 2017 German federal election. PS: Political Science & Politics50(3), forthcoming.
  2. Campbell, J. E., Norpoth, H., Abramowitz, A. I., Lewis-Beck, M. S., Tien, C., Campbell, J. E., Erikson, R. S., Wlezien, C., Lockerbie, B., Holbrook, T. M., Jerôme, B., Jerôme-Speziari, V., Graefe, A., Armstrong, J. S., Jones, R. J. J. & Cuzán, A. G. (2017). A Recap of the 2016 Election Forecasts. PS: Political Science & Politics, 50(2), 331-338.
  3. Graefe, A. (2017). Prediction market performance in the 2016 U.S. presidential election. Foresight – The International Journal of Applied Forecasting, 2017(45)38-42.
  4. Graefe, A. (2016). Issue-handling beats leadership: Issues and Leaders model predicts Clinton will defeat Trump. Research & Politics, DOI: 10.1177/2053168016679364.
  5. Graefe, A., Jones, R. J. J., Armstrong, J. S. & Cuzán, A. G. (2016). The PollyVote forecast for the 2016 American Presidential Election. PS: Political Science & Politics, 49(4), 687-690.
  6. Graefe, A., Haim, M., Haarmann, B. & Brosius, H.-B. (2016). Readers‘ perception of computer-written news: Credibility, expertise, and readability. Journalism, .
  7. Graefe, A. (2016). Forecasting proportional representation elections from non-representative expectation surveys. Electoral Studies, 42, 222-228.
  8. Armstrong, J. S., Du, R., Green, K. C., & Graefe, A. (2016). Predictive validity of evidence-based persuasion principles: An application of the index method. European Journal of Marketing, 50(1/2), 276-293.
  9. Green, K. C., Armstrong, J. S., Du, R., & Graefe, A. (2016). Persuasion principles index: Ready for pretesting advertisements. European Journal of Marketing, 50(1/2), 317-326.
  10. Graefe, A. (2015). Improving forecasts using equally weighted predictors. Journal of Business Research, 68(8), 1792-1799.
  11. Graefe, A. (2015). German election forecasting: Comparing and combining methods for 2013. German Politics, 24(2), 195-204.
  12. Graefe, A. (2015). Accuracy gains of adding vote expectation surveys to a combined forecast of US presidential election outcomes. Research & Politics, 2(1), 1-5.
  13. Graefe, A., Küchenhoff, H., Stierle, V. & Riedl, B. (2015). Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems. International Journal of Forecasting, 31(3), 943-951.
  14. Armstrong, J. S., Green, K. C., & Graefe, A. (2015). Golden Rule of Forecasting: Be Conservative. Journal of Business Research, 68(8), 1717-1731.
  15. Green, K. C., Armstrong, J. S., & Graefe, A. (2015). Golden Rule of Forecasting rearticulated: Forecast unto others as you would have them forecast unto you. Journal of Business Research, 68(8), 1768-1771.
  16. Graefe, A. (2014). Accuracy of vote expectation surveys in forecasting elections. Public Opinion Quarterly 78(S1), 204-232.
  17. Graefe, A., & Armstrong, J. S. (2014). Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue. Political Science Research and Methods, 2(1), 141-149.
  18. Graefe, A., Armstrong, J. S., Jones, R. J. J., & Cuzán, A. G. (2014). Combining forecasts: An application to elections. International Journal of Forecasting, 30(1), 43-54.
  19. Graefe, A., Armstrong, J. S., Jones, R. J. J., & Cuzán, A. G. (2014). Accuracy of combined forecasts for the 2012 Presidential Elections: The PollyVote. PS: Political Science & Politics, 47(2), 427-431.
  20. Graefe, A. (2013). Issue and leader voting in U.S. presidential elections. Electoral Studies, 32(4), 644-657.
  21. Graefe, A., & Armstrong, J. S. (2013). Forecasting elections from voters‘ perceptions of candidates‘ ability to handle issues. Journal of Behavioral Decision Making, 26(3), 295-303.
  22. Graefe, A., Armstrong, J. S., Jones, R. J. J., & Cuzán, A. G. (2013). Combined forecasts of the 2012 election: The PollyVote. Foresight – The International Journal of Applied Forecasting, 2013(28), 50-51.
  23. Graefe, A., & Armstrong, J. S. (2012). Predicting elections from the most important issue: A test of the take‐the‐best heuristic. Journal of Behavioral Decision Making, 25(1), 41-48.
  24. Graefe, A., Jones, R. J. J., Armstrong, J. S., & Cuzán, A. G. (2012). The PollyVote’s Year-Ahead Forecast of the 2012 US Presidential Election. Foresight: The International Journal of Applied Forecasting, 2012(24), 13-14.
  25. Graefe, A. (2011). Prediction markets and the „through of disillusionment.“ Foresight: The International Journal of Applied Forecasting, 2011(20), 43-46.
  26. Graefe, A., & Armstrong, J. S. (2011). Conditions under which index models are useful: Reply to bio-index commentaries. Journal of Business Research, 64(7), 693-695.
  27. Graefe, A., & Armstrong, J. S. (2011). Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task. International Journal of Forecasting, 27(1), 183-195.
  28. Armstrong, J. S., & Graefe, A. (2011). Predicting elections from biographical information about candidates: A test of the index method. Journal of Business Research, 64(7), 699-706.
  29. Graefe, A. (2010). Are prediction markets more accurate than simple surveys? Foresight – The International Journal of Applied Forecasting, 2010(19), 39-43.
  30. Graefe, A. (2010). Prediction markets for forecasting drug development. Foresight – The International Journal of Applied Forecasting, 2010(17), 8-12.
  31. Orwat, C., Rashid, A., Holtmann, C., Wolk, M., Scheermesser, M., Kosow, H., & Graefe, A. (2010). Adopting pervasive computing for routine use in healthcare. Pervasive Computing, IEEE, 9(2), 64-71.
  32. Graefe, A., Luckner, S., & Weinhardt, C. (2010). Prediction markets for foresight. Futures, 42(4), 394-404.
  33. Graefe, A., Armstrong, J. S., Cuzán, A. G., & Jones, R. J. J. (2009). Combined forecasts of the 2008 election: The Pollyvote. Foresight: The International Journal of Applied Forecasting, 2009(12), 41-42.
  34. Graefe, A. (2008). Prediction markets – Defining events and motivating participation. Foresight – The International Journal of Applied Forecasting, 2008(9), 30-32.
  35. Graefe, A., & Weinhardt, C. (2008). Long-term forecasting with prediction markets. A field experiment on applicability and expert confidence. Journal of Prediction Markets, 2(2), 71-91.
  36. Orwat, C., Graefe, A., & Faulwasser, T. (2008). Towards pervasive computing in health care – A literature review. BMC Medical Informatics and Decision Making, 8(26), 1-18.
  37. Graefe, A., & Orwat, C. (2007). Prediction Markets as a Mechanism for Public Engagement? A First Classification and Open Questions. International Journal of Technology, Knowledge and Society, 3(4), 137-142.
  38. Green, K. C., Armstrong, J. S., & Graefe, A. (2007). Methods to elicit forecasts from groups: Delphi and prediction markets compared. Foresight: The International Journal of Applied Forecasting, 2007(8), 17-20.

Book chapters/Editor reviewed

  1. Graefe, A., Armstrong, J. S., Jones, R. J. J. & Cuzán, A. G. (2017). Assessing the 2016 U.S. presidential election popular vote forecasts. In A. Cavari, R. Powell & K. Mayer (Eds.), The 2016 Presidential Election: The causes and consequences of an electoral earthquake. Lanham, MD: Lexington Books.
  2. Graefe, A. (2017). Political markets. In K. Arzheimer, J. Evans & M. S. Lewis-Beck (Eds.), The SAGE Handbook of Electoral Behaviour, Volume 2 (pp. 861-882). London: Sage.
  3. Haim, M. & Graefe, A. (2017). Automatisierter Journalismus: Anwendungsbereiche, Formen und Qualität. In C. Nuernbergk & C. Neuberger (Eds.), Journalismus im Internet: Profession – Partizipation – Technisierung (in press).
  4. Berger, M., Haim, M., Graefe, A., Brosius, H.-B., & Hess, T. (2015). Aktuelles Stichwort: Computational Journalism. Medienwirtschaft, 12(1), 20-23.
  5. Graefe, A., Green, K. C., & Armstrong, J. S. (2013). Forecasting. In S. I. Gass & M. C. Fu (Eds.), Encyclopedia of Operations Research and Management Science (3 ed., pp. 539-604). New York: Springer.
  6. Graefe, A. (2011). Prediction market accuracy for business forecasting. In L. Vaughan Williams (Ed.), Prediction Markets (pp. 87-95). New York: Routledge
  7. Green, K. C., Graefe, A., & Armstrong, J. S. (2011). Forecasting principles. In M. Lovric (Ed.), International Encyclopedia of Statistical Science (pp. 527-534). Berlin: Springer.
  8. Graefe, A., Orwat, C., & Faulwasser, T. (2008). Der Umgang mit Barrieren bei der Einführung von Pervasive Computing. Technikfolgenabschätzung – Theorie und Praxis, 17(1), 13-19.
  9. Graefe, A. (2007). Folgenabschätzung durch Prognosemärkte. Technikfolgenabschätzung – Theorie und Praxis, 16(2), 66-73.
  10. Graefe, A. (2007). Forecasting mit Prognosemärkten. In A. Bora, S. Bröchler & M. Decker (Eds.), Technology Assessment in der Weltgesellschaft (pp. 439-444). Berlin: Edition Sigma
  11. Graefe, A., Griewing, B., Holtmann, C., Rashid, A., & Scheermesser, M. (2006). Pervasive Computing im Gesundheitswesen: Technologische, gesellschaftliche und medizin-ökonomische Zusammenhänge. Krankenhaus IT-Journal, 2006(2), 44-47.
  12. Holtmann, C., Rashid, A., Graefe, A., Griewing, B., & Weinhardt, C. (2006). Time is brain. Analyse der Rettungskette im Schlaganfall. In T. Eymann & A. Koop (Eds.), Proceedings zum 5. Workshop der GMDS Arbeitsgruppe Mobiles Computing in der Medizin (pp. 52-67). Frankfurt: Shaker.

Monographs

  1. Graefe, Andreas (2016). Guide to Automated Journalism. Tow Center for Digital Journalism, Columbia Journalism School, New York City.
  2. Graefe, Andreas (2009). Prediction Markets versus Alternative Methods. Empirical Tests of Accuracy and Acceptability. Doctoral dissertation, University of Karlsruhe.

Other writing

  1. Graefe, Andreas (2016). Don’t trust a single forecast. The consensus all year has been that Clinton will win. Washington Post, Monkey Cage, September 22, 2016.
  2. Graefe, Andreas (2016). How to improve polling? Ask voters who will win. The Hill, September 7, 2016.
  3. Graefe, Andreas (2016). Get better predictions by combining diverse forecasts. The Conversation, August 23, 2016.
  4. Graefe, Andreas (2016). Political scientists predict Clinton will win 347 electoral votes in NovemberUnited States Politics and Policy Blog, London School of Economics, August 22, 2016.
  5. Graefe, Andreas & Mario Haim (2016). Člověk nebo algoritmy? Čí články čtenáři upřednostňují? European Journalism Observatory, May 25, 2016.
  6. Graefe, Andreas & Mario Haim (2016). Human Or Computer? Whose Stories Do Readers Prefer? European Journalism Observatory, May 17, 2016.
  7. Graefe, Andreas & Mario Haim (2016). Wenn Algorithmen Journalismus machen. European Journalism Observatory, May 17, 2016.
  8. Graefe, Andreas & Mario Haim (2016). Quando a scrivere è un algoritmo. European Journalism Observatory, May 11, 2016.
  9. Graefe, Andreas & J. Scott Armstrong (2016). Our biographical model predicts Clinton would defeat Trump by a landslide, but would be tied with Cruz. United States Politics and Policy Blog, London School of Economics, April 15, 2016.
  10. Graefe, Andreas (2016). A better way to predict who will win in November. Newsweek, March 20, 2016.
  11. Graefe, Andreas (2016). Combining forecasts predicts a Democratic win in this year’s election. United States Politics and Policy Blog, London School of Economics, March 15, 2016.
  12. Graefe, Andreas (2014). Asking voters who they think will win is one of the most accurate methods for forecasting elections available. United States Politics and Policy Blog, London School of Economics, October 28, 2014.
  13. Graefe, Andreas (2014). Looking at how candidates handle issues and their leadership capability can be just as effective at predicting presidential races as the strength of the economy. United States Politics and Policy Blog, London School of Economics, January 16, 2014.

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