Adaptive Bayesian Learning with Action and State-Dependent Signal Variance

• URL: http://arxiv.org/abs/2311.12878v2
• Date: Tue, 28 Nov 2023 18:29:09 GMT
• Title: Adaptive Bayesian Learning with Action and State-Dependent Signal Variance
• Authors: Kaiwen Hou
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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