Published December 28, 2015
| Published
Working Paper
Open
Two Information Aggregation Mechanisms for Predicting the Opening Weekend Box Office Revenues of Films: Boxoffice Prophecy and Guess of Guesses
Chicago
Abstract
Successful field tests were conducted on two new Information Aggregation Mechanisms (IAMs). The mechanisms collected information held as intuitions about opening weekend box office revenues for movies in Australia. Participants were film school students. One mechanism is similar to parimutuel betting that produces a probability distribution over box office amounts. Except for "art house films", the predicted distribution is indistinguishable from the actual revenues. The second mechanism is based on guesses of the guesses of others and applied when incentives for accuracy could not be used. It tested well against data and contains information not encompassed by the first mechanism.
Additional Information
We thank the Gordon and Betty Moore Foundation; the Lee Center; Australian Research Council (Linkage Grant LP110200336); University of Sydney; Australian Film, Television and Radio School (AFTRS); and the Caltech Laboratory for Experimental Economics and Political Science. The computer and software development skills of Hsing Yang Lee and Travis Maron are acknowledged. Their skills and dedication made the research possible. The comments of Matt Shum were very helpful.Attached Files
Published - SSWP1412.pdf
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SSWP1412.pdf
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Additional details
- Eprint ID
- 64644
- Resolver ID
- CaltechAUTHORS:20160222-140722119
- Gordon and Betty Moore Foundation
- Lee Center for Advanced Networking, Caltech
- Australian Research Council Linkage Grant
- LP110200336
- University of Sydney
- Australian Film, Television and Radio School (AFTRS)
- Caltech Laboratory for Experimental Economics and Political Science
- Created
-
2016-03-02Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Caltech groups
- Social Science Working Papers
- Series Name
- Social Science Working Paper
- Series Volume or Issue Number
- 1412