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Title: Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
Researchers have introduced a new algorithm to estimate structural models of dynamic decisions by human agents, addressing the challenge of high computational complexity. Traditionally, this task involves a nested structure: an inner problem identifying an optimal policy and an outer problem maximizing a measure of fit. Previous methods have struggled with large discrete state spaces or high-dimensional continuous state spaces, often sacrificing reward estimation accuracy. The new approach combines policy improvement with a stochastic gradient step for likelihood maximization, ensuring accurate reward estimation without compromising computational efficiency. This single-loop algorithm, designed to handle high-dimensional state spaces, converges to a stationary solution with finite-time guarantees. When the reward is linearly parameterized, it approximates the maximum likelihood estimator sublinearly, offering a robust solution for complex decision modeling tasks.  more » « less
Award ID(s):
1910385 2414372 2426064
PAR ID:
10546631
Author(s) / Creator(s):
; ;
Publisher / Repository:
INFORMS
Date Published:
Journal Name:
Operations Research
ISSN:
0030-364X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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