Information Acquisition with $\alpha$-Divergence Costs

Takashi Ui

Papers from arXiv.org

Abstract: 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 probabilities belong to the $q$-exponential family, which arises in nonextensive statistical mechanics (Tsallis, 1988) and in the $q$-logit model of traffic route choice (Nakayama, 2013). In the KL-divergence special case, this family reduces to the modified logit of Mat\v{e}jka and McKay (2015).

Date: 2026-05
References: Add references at CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2605.28026 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2605.28026

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().