The Measurement of Ranks and the Laws of Iterated Contraction

Artificial Intelligence 172:1195-1218 (2008)
Abstract
Ranking theory delivers an account of iterated contraction; each ranking function induces a specific iterated contraction behavior. The paper shows how to reconstruct a ranking function from its iterated contraction behavior uniquely up to multiplicative constant and thus how to measure ranks on a ratio scale. Thereby, it also shows how to completely axiomatize that behavior. The complete set of laws of iterated contraction it specifies amend the laws hitherto discussed in the literature.
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Emil Weydert (2012). Conditional Ranking Revision. Journal of Philosophical Logic 41 (1):237-271.

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