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arXiv · 2310.19147

Incentivizing Forecasters to Learn: Summarized vs. Unrestricted Advice

Yingkai Li·Jonathan Libgober
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Abstract

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.

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Yingkai Li, Jonathan Libgober. 2023-10-29. Incentivizing Forecasters to Learn: Summarized vs. Unrestricted Advice. https://arxiv.org/abs/2310.19147

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