Bandit algorithms such as Thompson sampling (TS) have been put forth for decades as useful tools for conducting adaptively-randomised experiments. By skewing the allocation toward superior arms, they can substantially improve particular outcomes of interest for both participants and investigators. For example, they may use participants’ ratings for continuously optimising their experience with a program. However, most of the bandit and TS variants are based on either binary or continuous outcome models, leading to suboptimal performances in rating scale data. Guided by behavioural experiments we conducted online, we address this problem by introducing Multinomial-TS for rating scales. After assessing its improved empirical performance in unique optimal arm scenarios, we explore potential considerations (including prior’s role) for calibrating uncertainty and balancing arm allocation in scenarios with no unique optimal arms.

汤普森采样 (TS) 等强盗算法作为进行自适应随机实验的有用工具已经提出了几十年。通过将分配偏向优势武器，他们可以大大改善参与者和研究人员感兴趣的特定结果。例如，他们可以使用参与者的评级来不断优化他们的计划体验。然而，大多数 bandit 和 TS 变体都基于二元或连续结果模型，导致评级量表数据的表现不佳。在我们在线进行的行为实验的指导下，我们通过引入多项式 TS评分量表来解决这个问题。在评估其在独特的最佳臂场景中改进的经验性能之后，我们探讨了在没有唯一的最佳臂的场景中校准不确定性和平衡臂分配的潜在考虑因素（包括先验的作用）。