Incentivizing Forecasters to Learn: Summarized vs. Unrestricted Advice

TL;DR

This paper explores how to design incentives for forecasters to learn effectively over time, showing that summarized advice maximizes information gain under certain conditions, with implications for forecasting and consultation.

Contribution

It introduces a dynamic mechanism design framework for incentivizing expert learning, highlighting when summarized advice suffices and when richer reports are necessary.

Findings

Summarized advice maximizes information if signals fully reveal outcomes.

Richer reporting is needed when signals are less informative.

Learning dynamics influence optimal incentive structures.

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.