Investors make predictable mistakes when forecasting earnings and stock returns, but machine learning models avoid them through adaptive learning.

Abstract: We investigate decentralized state estimation for a discrete event system in a setting where the information received at a coordinator may be corrupted or tampered by a malicious attacker.