Abstract

Bayesian optimal experimental design (BOED) is a methodology to identify experiments that are expected to yield informative data. Recent work in cognitive science considered BOED for computational models of human behavior with tractable and known likelihood functions. However, tractability often comes at the cost of realism; simulator models that can capture the richness of human behavior are often intractable. In this work, we combine recent advances in BOED and approximate inference for intractable models, using machine-learning methods to find optimal experimental designs, approximate sufficient summary statistics and amortized posterior distributions. Our simulation experiments on multi-armed bandit tasks show that our method results in improved model discrimination and parameter estimation, as compared to experimental designs commonly used in the literature.
Original languageEnglish
Title of host publicationNeurIPS 2021 Workshop "AI for Science"
Number of pages10
Publication statusAccepted/In press - 22 Oct 2021
EventNeurIPS 2021 Workshop "AI for Science: Mind the Gaps" - Online
Duration: 13 Dec 202113 Dec 2021
https://ai4sciencecommunity.github.io/index.html

Workshop

WorkshopNeurIPS 2021 Workshop "AI for Science: Mind the Gaps"
Period13/12/2113/12/21
Internet address

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