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Crowdsourced Forecasting with Prediction Markets and Real-time Delphi

Crowdsourced Forecasting with Prediction Markets and Real-time Delphi
Type:Bachelorarbeit, Masterarbeit

Simon Kloker 


According to the market efficiency hypothesis the best forecasts are those that comprise all available information. However, for many questions concerning the society, such as political elections and events, this information is widely spread. Sometimes the high subjectivity also makes it hard to aggregate this information adequately, why statistical models may not be applicable. Scattered, subjective, and plenty of information – a job for the Crowd.


The FAZ.NET-Orakel is a platform for crowdsourced forecasting in cooperating with the Frankfurter Allgemeine Zeitung. In this context several theses in following contexts are available:

  • Attract the crowd: Design, Incentives, and good advertisement for a crowdsourced forecasting
  • Real-time Delphi for Crowds: How many is too much?
  • Cognitive Biases in crowd forecasting
  • Manipulation in Prediction Markets
By arrangement other topics are possible. Feel free to suggest ideas!


Besides of a general interest in the topic, we demand at least basic skills in statistics. Programming skills are not required for all topics, but some topics is also include (up to extensive) programming tasks (mainly web-development and Java-based technologies).
Language: German or English


If you are interested or have any additional questions (or suggestions), feel free to contact. Please add a recent transcript of records to a application.

Simon Kloker (simon.kloker@kit.edu)