# Games of Incomplete Information Played By Statisticians

arXiv: https://arxiv.org/abs/1910.07018 · HTML: https://mlsanity.com/paper/1910.07018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Pricing AI Model Accuracy](https://mlsanity.com/paper/2504.13375/) (2025)

## Abstract (arXiv)

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.