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Prior-free Blackwell

Maxwell Rosenthal

Economic Theory Bulletin2026https://doi.org/10.1007/s40505-026-00307-6article
ABDC B
Weight
0.50

Abstract

This paper develops a prior-free model of data-driven decision making in which the decision maker observes the entire distribution of signals generated by a known experiment under an unknown distribution of the state variable and evaluates actions according to their worst-case payoff over the set of state distributions consistent with that observation. We propose a ranking of experiments in which E is robustly more informative than $$E'$$ E ′ if the value of the decision maker’s problem after observing E is always at least as high as the value of the decision maker’s problem after observing $$E'.$$ E ′ . This comparison, which is strictly weaker than Blackwell’s classical order, holds if and only if the null space of E is contained in the null space of $$E'.$$ E ′ .

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https://doi.org/https://doi.org/10.1007/s40505-026-00307-6

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@article{maxwell2026,
  title        = {{Prior-free Blackwell}},
  author       = {Maxwell Rosenthal},
  journal      = {Economic Theory Bulletin},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s40505-026-00307-6},
}

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Prior-free Blackwell

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.