AIAug 7, 2014

Generalized Qualitative Probability: Savage Revisited

arXiv:1408.1481v177 citations
Originality Synthesis-oriented
AI Analysis

This work addresses foundational issues in decision theory and logic for researchers in those fields, but it appears incremental as it builds on Savage's framework with weaker assumptions.

The paper tackles the problem of analyzing preferences among acts with weaker rationality postulates than Savage's, deriving the Sure Thing Principle and characterizing a generalized qualitative probability that blends traditional qualitative probability and ranked structures from logic.

Preferences among acts are analyzed in the style of L. Savage, but as partially ordered. The rationality postulates considered are weaker than Savage's on three counts. The Sure Thing Principle is derived in this setting. The postulates are shown to lead to a characterization of generalized qualitative probability that includes and blends both traditional qualitative probability and the ranked structures used in logical approaches.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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