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Learning about a changing state

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  • Benjamin Davies

Abstract

A long-lived Bayesian agent observes costly signals of a time-varying state. He chooses the signals' precisions sequentially, balancing their costs and marginal informativeness. I compare the optimal myopic and forward-looking precisions when the state follows a Brownian motion. I also compare the myopic precisions induced by other Gaussian processes.

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  • Benjamin Davies, 2024. "Learning about a changing state," Papers 2401.03607, arXiv.org.
    • Handle: RePEc:arx:papers:2401.03607
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    References listed on IDEAS

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    Cited by:

    1. Benjamin Davies, 2024. "Estimating sample paths of Gauss-Markov processes from noisy data," Papers 2404.00784, arXiv.org.

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