How should forecasters be incentivized to acquire the most information when learning takes place over time? We address this question in the context of a novel dynamic mechanism design problem in which a designer incentivizes an expert to learn by conditioning rewards on an event's outcome and the expert's reports. Eliciting summarized advice at a terminal date maximizes information acquisition if an informative signal either fully reveals the outcome or has predictable content. Otherwise, richer reporting capabilities may be required. Our findings shed light on incentive design for consultation and forecasting by illustrating how learning dynamics shape the qualitative properties of effort-maximizing contracts.

翻译：当学习过程随时间推移发生时，应如何激励预测者获取最多信息？我们在一个新颖的动态机制设计问题背景下探讨此问题，其中设计者通过将奖励与事件结果及专家报告挂钩来激励专家学习。若信息性信号要么完全揭示结果，要么具有可预测内容，则在终止日期获取摘要建议能最大化信息获取。否则可能需要更丰富的报告能力。我们的研究通过阐明学习动态如何影响努力最大化契约的定性特征，为咨询和预测领域的激励机制设计提供了启示。