Bayesian adaptive inference is widely used in psychophysics to estimate psychometric parameters. Most applications used myopic one-step ahead strategy which only optimizes the immediate utility. The widely held expectation is that global optimization strategies that explicitly optimize over some horizon can largely improve the performance of the myopic strategy. With limited studies that compared myopic and global strategies, the expectation was not challenged and researchers are still investing heavily to achieve global optimization. Is that really worthwhile? This paper provides a discouraging answer based on experimental simulations comparing the performance improvement and computation burden between global and myopic strategies in parameter estimation of multiple models. The finding is that the added horizon in global strategies has negligible contributions to the improvement of optimal global utility other than the most immediate next steps (of myopic strategy). Mathematical recursion is derived to prove that the contribution of utility improvement of each added horizon step diminishes fast as that step moves further into the future.

中文翻译：

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全局优化策略能否胜过贝叶斯参数估计的近视策略？

贝叶斯自适应推理广泛用于心理物理学中以估计心理测量参数。大多数应用程序使用近视一步一步策略，该策略仅优化即时效用。人们普遍认为，在某个范围内明确优化的全局优化策略可以在很大程度上提高近视策略的性能。由于比较短视和全局策略的研究有限，预期没有受到挑战，研究人员仍在大力投资以实现全局优化。真的值得吗？本文提供了基于实验模拟的令人沮丧的答案，比较了多个模型参数估计中全局和近视策略之间的性能改进和计算负担。发现是，除了最直接的下一步（近视战略）之外，全球战略中增加的视野对改善最佳全球效用的贡献可以忽略不计。推导出数学递归以证明每个增加的视野步骤的效用改善的贡献随着该步骤进一步向未来移动而迅速减少。