Incentivizing Forecasters to Learn: Summarized vs. Unrestricted Advice

Yingkai Li and Jonathan Libgober

Papers from arXiv.org

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 where a designer can incentivize learning by conditioning a reward on an event's outcome and expert reports. Eliciting summarized advice at a terminal date maximizes information acquisition if an informative signal 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 qualitative properties of effort-maximizing contracts.

Date: 2023-10, Revised 2025-04
New Economics Papers: this item is included in nep-gth and nep-mic
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