This paper presents a framework for how to incorporate prior sources of information into the design of a sequential experiment. These sources can include previous experiments, expert opinions, or the experimenter's own introspection. We formalize this problem using a multi-prior Bayesian approach that maps each source to a Bayesian model. These models are aggregated according to their associated posterior probabilities. We evaluate a broad of policy rules according to three criteria: whether the experimenter learns the parameters of the payoff distributions, the probability that the experimenter chooses the wrong treatment when deciding to stop the experiment, and the average rewards. We show that our framework exhibits several nice finite sample properties, including robustness to any source that is not externally valid.

翻译：本文为如何将先前的信息来源纳入相继实验的设计提供了一个框架。 这些来源可以包括以前的实验、专家意见或实验者自己的反省。 我们使用多种主要巴伊西亚方法将这一问题正式化,该方法将每种来源都映射到巴伊西亚模型中。 这些模型根据其相关的后继概率进行汇总。 我们根据三个标准评估了广泛的政策规则:实验者是否了解了付款分布的参数,实验者在决定停止实验时选择错误治疗的可能性,以及平均回报。 我们展示了我们的框架有几种精细的样本属性,包括对外部无效的任何来源的坚固性。