Dynamic Choice of Information Sources

Mayskaya, Tatiana

doi:10.2139/ssrn.4138333

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar Gaussian learning framework is used as the learning‐theoretic foundation for the drift‐diffusion model by Fudenberg, Strack, and Strzalecki (2018). Following a different route, Che and Mierendorff (2019), Mayskaya (2020), and Liang, Mu, and Syrgkanis (2018) study the sequential choice between multiple exogenously given information sources 1 . The novelty of my framework is that the DM flexibly designs the information generating process.…”

Section: Related Literaturementioning

confidence: 99%

Optimal Dynamic Information Acquisition

Zhong

2022

ECTA

View full text Add to dashboard Cite

I study a dynamic model in which a decision‐maker (DM) acquires information about the payoffs of different alternatives prior to making a decision. The model's key feature is the flexibility of information: the DM can choose any dynamic signal process as an information source, subject to a flow cost that depends on the informativeness of the signal. Under the optimal policy, the DM acquires a signal that arrives according to a Poisson process. The optimal Poisson signal confirms the DM's prior belief and is sufficiently precise to warrant immediate action. Over time, given the absence of the arrival of a Poisson signal, the DM continues seeking an increasingly precise but less frequent Poisson signal.

show abstract

Section: Related Literaturementioning

confidence: 99%

Optimal Dynamic Information Acquisition

Zhong

2022

ECTA

View full text Add to dashboard Cite

show abstract

“…This paper takes an abstract view of experiments. Following a recent literature in economics, we treat experiments as distinct information sources (Che and Mierendorff (2019), Liang et al (2018), Mayskaya (2019)). As will be argued here, this is a natural interpretation in light of experimental choices that often are, informally, advocated in studies of innovation and entrepreneurship.…”

Section: Experimental Typesmentioning

confidence: 99%

A Synthetic Model of Disruption and Experimentation

Gans¹

2021

View full text Add to dashboard Cite

Thanks to Steven Callander, Scott Stern and seminar participants at the University of Toronto and Harvard Business School for useful feedback. All errors remain my own. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

show abstract

Dynamic Choice of Information Sources

Cited by 2 publications

References 15 publications

Optimal Dynamic Information Acquisition

Optimal Dynamic Information Acquisition

A Synthetic Model of Disruption and Experimentation

Contact Info

Product

Resources

About