🤖 AI Summary
In dynamic mechanism design, traditional static contracts fail to incentivize forecasters to sustain learning effort over time. Method: This paper pioneers the integration of Bayesian learning dynamics into mechanism design, constructing a dynamic reward mechanism that depends jointly on outcomes and reports. Contribution/Results: Theoretical analysis demonstrates that information structure—particularly signal predictability—determines the optimal reporting format: restricted summary reports induce insufficient learning when signals are either non-fully revealing or unpredictable, whereas flexible reporting significantly enhances information acquisition efficiency. The paper fully characterizes the structural properties of effort-maximizing contracts, thereby transcending the static incentive paradigm. It establishes a novel theoretical foundation for dynamic information acquisition mechanisms and offers actionable insights for practical implementation.
📝 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.
