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Adaptive Bayesian Learning with Action and State-Dependent Signal Variance

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  • Kaiwen Hou
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.

Suggested Citation

  • Kaiwen Hou, 2023. "Adaptive Bayesian Learning with Action and State-Dependent Signal Variance," Papers 2311.12878, arXiv.org, revised Nov 2023.
    • Handle: RePEc:arx:papers:2311.12878
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    References listed on IDEAS

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