Statistical Mechanism Design: Robust Pricing, Estimation, and Inference

Fiche du document

Date

27 mai 2024

Type de document
Périmètre
Identifiant
  • 2405.17178
Collection

arXiv

Organisation

Cornell University




Citer ce document

Duarte Gonçalves et al., « Statistical Mechanism Design: Robust Pricing, Estimation, and Inference », arXiv - économie


Partage / Export

Résumé 0

This paper tackles challenges in pricing and revenue projections due to consumer uncertainty. We propose a novel data-based approach for firms facing unknown consumer type distributions. Unlike existing methods, we assume firms only observe a finite sample of consumers' types. We introduce \emph{empirically optimal mechanisms}, a simple and intuitive class of sample-based mechanisms with strong finite-sample revenue guarantees. Furthermore, we leverage our results to develop a toolkit for statistical inference on profits. Our approach allows to reliably estimate the profits associated for any particular mechanism, to construct confidence intervals, and to, more generally, conduct valid hypothesis testing.

document thumbnail

Par les mêmes auteurs

Sur les mêmes sujets

Sur les mêmes disciplines

Exporter en