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Activity-based models in Transportation Science focus on the description of human trips and activities. We address the modeling of the spatial decision for so-called secondary activities: given both home and work locations, where do individuals perform activities such as shopping and leisure? The simulation of these decisions using random utility models requires a full enumeration of the possible outcomes. For large data sets, it becomes computationally unfeasible because of the combinatorial complexity. To overcome this limitation, we propose a model where agents have limited, accurate information about a small subset of the overall spatial environment. Agents are inter-connected by a social network through which they can exchange information. This approach has several advantages compared to the explicit simulation of a standard random utility model: a) it computes plausible choice sets in …