Platforms are able to gather large quantities of data, which can result in high precision predictions regarding consumers purchasing patterns. A fundamental question is whether a platform has the incentives and ability to share such non-verifiable information with its sellers. We demonstrate that cheap-talk information can be exchanged when the platform shares with its sellers region-forecast. In this equilibrium, the platform shares only a region that contains its private information.

Firms rely on expert teams for decision making but face privacy concerns. We propose a mechanism to protect experts' privacy and analyze incentives. The firm garbles experts' reports to address privacy concerns, encouraging truthful reporting. Larger teams may perform worse, and more capable experts may be detrimental to team performance under privacy-conscious conditions.

Tele-driving lets drivers operate vehicles remotely, offering a viable alternative to fully automated ones. It reduces spatial mismatch in ride-hailing, enabling any driver for any customer. We quantify gains by comparing traditional systems to tele-driving. Findings: 1) For impatient customers, optimized driver capacity enhances service or reduces drivers; 2) For patient customers, tele-driving stabilizes systems or cuts drivers with similar service quality.