论文标题
全球优化策略能否优于贝叶斯参数估计的近视策略？
Can Global Optimization Strategy Outperform Myopic Strategy for Bayesian Parameter Estimation?
论文作者
论文摘要
贝叶斯自适应推断被广泛用于心理物理学来估计心理测量参数。大多数应用程序都使用近视的一步策略，仅优化了即时实用程序。人们普遍认为，明确优化在某些地平线上的全球优化策略可以在很大程度上改善近视策略的性能。通过比较近视和全球策略的有限研究，期望并没有受到挑战，研究人员仍在大量投资以实现全球优化。真的值得吗？本文基于实验模拟提供了一个令人沮丧的答案，该实验模拟比较了多个模型的参数估计中全球和近视策略之间的性能改善和计算负担。发现全球战略的额外范围对改善最佳全球效用（近视战略）以外的最佳全球效用的贡献可忽略不计。得出数学递归是为了证明，随着该步骤进一步发展到未来，每个添加的视野步骤的效用改进的贡献会迅速减小。
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.
