# Adaptive Bayesian Learning with Action and State-Dependent Signal Variance

arXiv: https://arxiv.org/abs/2311.12878 · HTML: https://mlsanity.com/paper/2311.12878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 25 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Abstract (arXiv)

This manuscript presents an advanced framework for Bayesian learning by incorporating action and state-dependent signal variances into decision-making models. This framework is pivotal in understanding complex data-feedback loops and decision-making processes in various economic systems. Through a series of examples, we demonstrate the versatility of this approach in different contexts, ranging from simple Bayesian updating in stable environments to complex models involving social learning and state-dependent uncertainties. The paper uniquely contributes to the understanding of the nuanced interplay between data, actions, outcomes, and the inherent uncertainty in economic models.