The Overcompensation Effect of Information Asymmetry: Why Hidden Types Can Increase Optimal Update Frequency

Xing et al. (2024) deliver a striking result in data trading: with asymmetric information, suppliers optimally choose more frequent updates to sustain utility when quantity falls. This deviates from the standard intuition that asymmetry simply degrades outcomes. We propose a general theory of “overcompensation” in dynamic contracts for perishable information goods (data freshness, model staleness, semantics in AIGC). The model predicts regimes where the principal optimally induces higher refresh rates as a signal and as a utility-preserving substitute for unobservable quality/quantity. We extend to FL and teleoperation AIGC services (Zhan et al., 2025), and AoI-driven MCS (Zhou et al., 2025), providing conditions under which overcompensation lowers information rents and improves welfare despite increased operational cost. Methodologically, we bring tools from algorithmic contract theory (Dütting et al., 2024) to characterize simple near-optimal dynamic menus and test via simulation and field data. The novelty is a unifying explanation and cross-domain validation of a counterintuitive comparative statics result observed in one domain (Xing et al., 2024). If borne out, this yields crisp design guidance: when you cannot screen types on quantity/quality, incentivize freshness more aggressively—up to an analytically characterizable threshold.

References:

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