Misinformation and Market Dynamics: A Cyber-Physical Network Framework for Belief Formation, Consensus, and Welfare Implications

Abstract

This paper presents a cyber-physical systems (CPS) framework to model the interplay between market price dynamics and social belief formation in a decentralized setting. The physical layer captures the evolution of prices through a networked market system governed by linear supply, demand, and crossprice elasticity relationships. The cyber layer represents belief formation via a hypergraph-structured learning model, where agents update expectations through distributed Kalman filters based on noisy price observations and group-level interactions. We analyze how informational frictions—driven by social structure, media influence, or cognitive limitations—induce delays in belief con-vergence to equilibrium prices. These delays, in turn, generate dynamic welfare losses due to suboptimal economic decisions. By linking convergence rates to hypergraph Laplacian spectra, we show how group-level information structures determine the speed and equity of learning processes. Our findings provide a theoretical foundation for studying misinformation and its economic costs in markets shaped by decentralized learning and social influence.