https://scholars.lib.ntu.edu.tw/handle/123456789/625602
Title: | Sequential hypothesis testing under stochastic deadlines | Authors: | Frazier P.I ANGELA YU-CHEN LIN |
Issue Date: | 2008 | Source: | Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference | Abstract: | Most models of decision-making in neuroscience assume an infinite horizon, which yields an optimal solution that integrates evidence up to a fixed decision threshold; however, under most experimental as well as naturalistic behavioral settings, the decision has to be made before some finite deadline, which is often experienced as a stochastic quantity, either due to variable external constraints or internal timing uncertainty. In this work, we formulate this problem as sequential hypothesis testing under a stochastic horizon. We use dynamic programming tools to show that, for a large class of deadline distributions, the Bayes-optimal solution requires integrating evidence up to a threshold that declines monotonically over time. We use numerical simulations to illustrate the optimal policy in the special cases of a fixed deadline and one that is drawn from a gamma distribution. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858764671&partnerID=40&md5=972ddfc00a51d2285a49b515de564979 https://scholars.lib.ntu.edu.tw/handle/123456789/625602 |
SDG/Keyword: | Decision threshold; External constraints; Gamma distribution; Infinite horizons; Optimal policies; Optimal solutions; Sequential hypothesis testing; Optimal systems; Stochastic systems; Stochastic models |
Appears in Collections: | 環境工程學研究所 |
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