In this paper we propose an interactive decision-making framework for wireless networks where the outcome is influenced not only by the decisions of the users but also by a random variable. We examine specially the finite players case which we call random matrix games (RMGs). We present different approaches and solutions concepts in such games as well as distributed strategic learning in which each player adjusts her strategy in response to the recent information and signals. The applicability of the proposed framework is illustrated in user-centric network selection under measurement noise.