UFXP and OUFXP estimators decouple utility parameters from the Bellman fixed point in infinite-horizon DDC models, enabling consistent neural-network estimation without embedded linear systems.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Develops self-consistency monitoring for preference annotators and derives sample-complexity bounds showing linear contracts achieve near-ideal performance faster than binary ones under continuous actions.
A data-augmentation framework for conjoint analysis integrates LLM-generated data with human responses to yield consistent, asymptotically normal estimators and reported cost savings of 24.9-79.8% in two empirical studies.
Derives closed-form optimal order quantity for newsvendor under mean-variance ambiguity and optimal-transport misspecification, generalizing Scarf model with finite-sample performance guarantees separating estimation error and distribution shift.
citing papers explorer
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Training Neural Networks Embedded in Dynamic Discrete Choice Models
UFXP and OUFXP estimators decouple utility parameters from the Bellman fixed point in infinite-horizon DDC models, enabling consistent neural-network estimation without embedded linear systems.
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How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators
Develops self-consistency monitoring for preference annotators and derives sample-complexity bounds showing linear contracts achieve near-ideal performance faster than binary ones under continuous actions.
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Large Language Models for Market Research: A Data-augmentation Approach
A data-augmentation framework for conjoint analysis integrates LLM-generated data with human responses to yield consistent, asymptotically normal estimators and reported cost savings of 24.9-79.8% in two empirical studies.
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Newsvendor under Ambiguity and Misspecification
Derives closed-form optimal order quantity for newsvendor under mean-variance ambiguity and optimal-transport misspecification, generalizing Scarf model with finite-sample performance guarantees separating estimation error and distribution shift.