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Limitations on robust ratings and predictions

Abstract:

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

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Publisher copy:
10.1007/978-3-319-41354-9_8

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
Publisher:
Springer Publisher's website
Journal:
10th IFIP WG 11.11 International Conference on Trust Management (IFIP TM 2016) Journal website
Host title:
10th IFIP WG 11.11 International Conference on Trust Management (IFIP TM 2016)
Publication date:
2016-07-02
Acceptance date:
2016-04-16
DOI:
Source identifiers:
891820
Pubs id:
pubs:891820
UUID:
uuid:c9d727c7-d242-4ef5-83a4-0c6c863f2fdf
Local pid:
pubs:891820
Deposit date:
2018-07-31

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