Adaptive Bayesian Learning with Action and State-Dependent Signal Variance

Kaiwen Hou

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arXiv · 2311.12878

Adaptive Bayesian Learning with Action and State-Dependent Signal Variance

Kaiwen Hou

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Abstract

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.

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stat.ME cs.LG econ.EM math.ST stat.TH

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BibTeX RIS

Kaiwen Hou. 2023-11-20. Adaptive Bayesian Learning with Action and State-Dependent Signal Variance. https://arxiv.org/abs/2311.12878

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