Opinions Before Evidence: Dynamic Information Quality and Source Familiarity

Georgy Lukyanov and Nikita Ogorodnikov
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Georgy Lukyanov: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: New information services begin without a record that teaches customers how to interpret them. We study a two-period monopoly selling standardized forecasts whose committed scoring technology combines noisy evidence with a persistent, initially unknown calibration. More evidence improves a forecast's current decision value but makes the source's calibration harder to learn. The first forecast is released publicly only after its immediate use expires, so it builds a common decoder for the next forecast. For an arbitrary stakes distribution, monopoly pricing uses the same static cutoff in both periods. Provider patience lowers initial evidence intensity; with identical technologies and a binary format menu, transparent conditions generate an endogenous low-evidence first forecast followed by a high-evidence second forecast. A constrained planner switches later because monopoly undercaptures the return to costly evidence. A calibration audit eliminates the accuracy–interpretability trade-off, but the provider may not adopt it even when adoption is socially valuable. The mechanism requires neither distorted reporting nor confirmation preferences: the strategic choice is the auditable evidence input to a mechanically generated score.

Keywords: information provision; forecasting services; source learning; evidence quality; dynamic pricing; calibration audits (search for similar items in EconPapers)
Date: 2026-10-19
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