Elicitability

2025-10-01

AI Summary

This paper addresses result manipulation in statistical analysis under unsupervised settings, where analysts may strategically misreport findings absent external oversight. The goal is to ensure truthful and credible statistical reporting through incentive design. Method: We introduce the framework of *incentive extractability*, defining a novel experiment comparison order distinct from the Blackwell order—thereby exposing a fundamental misalignment between inferential preferences and incentive design objectives. Integrating statistical decision theory with mechanism design, we formulate a game-theoretic model and construct incentive-compatible payment rules based on independent data sources. Contribution/Results: We prove that “estimation-friendly” data necessarily support incentive extractability, but the converse does not hold—establishing a new dimension for data value assessment. This yields a precise characterization of the class of statistical information that can be reliably elicited via incentives, advancing both theory and practice in verifiable statistical reporting.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

An analyst is tasked with producing a statistical study. The analyst is not monitored and is able to manipulate the study. He can receive payments contingent on his report and trusted data collected from an independent source, modeled as a statistical experiment. We describe the information that can be elicited with appropriately shaped incentives, and apply our framework to a variety of common statistical models. We then compare experiments based on the information they enable us to elicit. This order is connected to, but different from, the Blackwell order. Data preferred for estimation are also preferred for elicitation, but not conversely. Our results shed light on how using data as incentive generator in payment schemes differs from using data for statistical inference.

Problem

• Eliciting reliable information from unmonitored analysts
• Comparing statistical experiments based on elicitable information
• Differentiating data usage for incentives versus statistical inference

Innovation

• Using payment incentives to elicit truthful statistical reports
• Comparing experiments based on elicitable information capabilities
• Differentiating data usage for incentives versus statistical inference