Abstract: This paper investigates social interactions under endogenous and dynamic network formation. We consider the multiple network spatial autoregressive (SAR) model of Horrace et al. (2022), where each network consists of agents assigned by their respective managers to produce output. Agents complement one another within their own network but compete across other networks over multiple periods. To address endogeneity in network formation, we extend their static selection framework to a dynamic setting by assuming that each network is overseen by a forward-looking manager who makes strategic decisions about her own network's membership. Following Ericson and Pakes (1995), we adopt a dynamic game framework and implement a Heckman-type selection bias correction to recover the network-level contextual effects. The competition is played across multiple rounds (i.e., matches), and equilibrium of actions is characterized by a Markov Perfect Equilibrium (MPE) within each independent match. The assumption of match independence underpins our theoretical and empirical models, with theory suggesting that a large number of matches is necessary for consistency, while simulations show that a large number of periods in each match helps reduce estimator RMSE. We apply our model to National Basketball Association data, treating players as agents and on-court lineups, organized by coaches from their respective teams, as networks competing head-to-head. We find that ignoring coaches' substitution decisions attenuates the impact of situational factors, such as score differential, on individual performance.