We present a model that describes how past experience and available information may influence the behavior of bounded rational agents over a fixed network. This work can be framed in the literature on network performance and, in particular, on how such a performance could be defined in terms of effectiveness in coordinating flow dynamics on a fixed network structure as well as how individual agents’ preferences and behaviors may lead to different network performance. Specifically, our model describes the agents’ flow using a mean-field approach that considers path preference dynamics. Such a dynamic highlights the fact that the agents choose their path based on both the network congestion state and the observation of the decisions of the agents that have preceded them. We introduce the reader to a set of assumptions and an approach that can be used to prove the existence of a mean-field equilibrium over a suitable set of time-varying mass distributions, defined edge by edge in the network. Finally, we discuss the limitations and possible future developments of the model and its applications to organizational networks.
