Games of Incomplete Information Played By Statisticians

Annie Liang

Papers from arXiv.org

Abstract: Players are statistical learners who learn about payoffs from data. They may interpret the same data differently, but have common knowledge of a class of learning procedures. I propose a metric for the analyst's "confidence" in a strategic prediction, based on the probability that the prediction is consistent with the realized data. The main results characterize the analyst's confidence in a given prediction as the quantity of data grows large, and provide bounds for small datasets. The approach generates new predictions, e.g. that speculative trade is more likely given high-dimensional data, and that coordination is less likely given noisy data.

Date: 2019-10, Revised 2020-07
New Economics Papers: this item is included in nep-gth and nep-mic
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Citations: View citations in EconPapers (6)

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