{"paper":{"title":"Information Acquisition with $\\alpha$-Divergence Costs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"econ.TH","authors_text":"Takashi Ui","submitted_at":"2026-05-27T06:31:13Z","abstract_excerpt":"Building on the $f$-information model of Bloedel et al. (2025), this paper introduces a one-parameter family of information acquisition models that extends the mutual information model (Mat\\v{e}jka and McKay, 2015) while preserving its analytical tractability, and characterizes optimal information acquisition. The information cost is derived from the $\\alpha$-divergence and represented in closed form via the $\\alpha$-integration of Amari (2007), nesting the KL-divergence ($\\alpha=-1$), the reverse KL-divergence ($\\alpha=1$), and the squared Hellinger distance ($\\alpha=0$). The optimal choice p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28026","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.28026/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}