Quantifying the Restrictiveness of Theories

Fudenberg, Drew; Gao, Wayne Yuan; Liang, Annie

doi:10.2139/ssrn.3580408

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Cited by 2 publications

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“…Appendix Notes: The table evaluates the restrictiveness (Fudenberg, Gao, and Liang 2023) and completeness (Fudenberg et al 2022) of the quantitative model of log(price per viewer) described in Section 5. Column (1) uses log(price per viewer) predicted from the baseline model in which advertisers' value of a first impression is homogeneous across viewers.…”

Section: Quantificationmentioning

confidence: 99%

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Pricing Power in Advertising Markets: Theory and Evidence

Gentzkow¹,

Shapiro²,

Yang³

et al. 2022

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show abstract

Section: Quantificationmentioning

confidence: 99%

“…Appendix Notes (continued): The table evaluates the restrictiveness (Fudenberg, Gao, and Liang 2023) and completeness (Fudenberg et al 2022) of the quantitative model of log(price per viewer) described in Section 5.…”

Section: Quantificationmentioning

confidence: 99%

Pricing Power in Advertising Markets: Theory and Evidence

Gentzkow¹,

Shapiro²,

Yang³

et al. 2022

View full text Add to dashboard Cite

show abstract

Machine learning for evaluating and improving theories

Fudenberg

Liang

2020

SIGecom Exch.

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We summarize our recent work that uses machine learning techniques as a complement to theoretical modeling, rather than a substitute for it. The key concepts are those of the completeness and restrictiveness of a model. A theory's completeness is how much it improves predictions over a naive baseline, relative to how much improvement is possible. When a theory is relatively incomplete, machine learning algorithms can help reveal regularities that the theory doesn't capture, and thus lead to the construction of theories that make more accurate predictions. Restrictiveness measures a theory's ability to match arbitrary hypothetical data: A very unrestrictive theory will be complete on almost any data, so the fact that it is complete on the actual data is not very instructive. We algorithmically quantify restrictiveness by measuring how well the theory approximates randomly generated behaviors. Finally, we propose "algorithmic experimental design" as a method to help select which experiments to run.

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Quantifying the Restrictiveness of Theories

Cited by 2 publications

References 66 publications

Pricing Power in Advertising Markets: Theory and Evidence

Pricing Power in Advertising Markets: Theory and Evidence

Machine learning for evaluating and improving theories

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