Work

Learning about a changing state

Year: 2024

Type: preprint

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.

Source: RePEc: Research Papers in Economics

DOI: 10.48550/arxiv.2401.03607

Author: Davies, Benjamin

Institution: Stanford University

Language: English

Topic: Game Theory and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W4390723452

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