Conference item
Limitations on robust ratings and predictions
- Abstract:
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Predictions are a well-studied form of ratings. Their objective nature allows a rigourous analysis. A problem is that there are attacks on prediction systems and rating systems. These attacks decrease the usefulness of the predictions. Attackers may ignore the incentives in the system, so we may not rely on these to protect ourselves. The user must block attackers, ideally before the attackers introduce too much misinformation.We formally axiomatically define robustness as the prop- erty that...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
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(pdf, 298.4KB)
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- Publisher copy:
- 10.1007/978-3-319-41354-9_8
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Authors
Bibliographic Details
- Publisher:
- Springer Publisher's website
- Publication date:
- 2016-07-02
- Acceptance date:
- 2016-04-16
- DOI:
- Pubs id:
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pubs:891820
- URN:
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uri:c9d727c7-d242-4ef5-83a4-0c6c863f2fdf
- UUID:
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uuid:c9d727c7-d242-4ef5-83a4-0c6c863f2fdf
- Local pid:
- pubs:891820
Terms of use
- Copyright holder:
- IFIP International Federation for Information Processing
- Copyright date:
- 2016
- Notes:
- © IFIP International Federation for Information Processing 2016. This is the accepted manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/978-3-319-41354-9_8
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