# Distributed Estimation in the Presence of Strategic Data Sources

## TL;DR

This paper develops incentive mechanisms for distributed estimation involving strategic human data sources, ensuring data quality while minimizing costs despite agents' self-interest and potential misreporting.

## Contribution

It introduces a novel mechanism design framework that incentivizes strategic agents to provide accurate data for distributed estimation tasks.

## Key findings

- Mechanisms successfully incentivize truthful data reporting.
- Expected total compensation is minimized while maintaining estimation quality.
- The approach addresses strategic behavior in human-in-the-loop data collection.

## Abstract

Distributed estimation that recruits potentially large groups of humans to collect data about a phenomenon of interest has emerged as a paradigm applicable to a broad range of detection and estimation tasks. However, it also presents a number of challenges especially with regard to user participation and data quality, since the data resources may be strategic human agents instead of physical sensors. We consider a static estimation problem in which an estimator collects data from self-interested agents. Since it incurs cost to participate, mechanisms to incentivize the agents to collect and transmit data of desired quality are needed. Agents are strategic in the sense that they can take measurement with different levels of accuracy by expending different levels of effort. They may also misreport their information in order to obtain greater compensation, if possible. With both the measurements from the agents and their accuracy unknown to the estimator, we design incentive mechanisms that encourage desired behavior from strategic agents. Specifically, we solve an optimization problem at the estimator which minimizes the expected total compensation to the agents while guaranteeing a specified quality of the global estimate.