In this paper, we propose a compositional two-layer stochastic model as a formal basis for simulation-oriented analysis of collective decision-making in networked communities. The model separates the latent opinion dynamics from a non-invasive observation layer. Four primitives are composed into a single population-level Markov kernel on the agent state space: activation, pairwise dialogue, exposure aggregation, and internalization. We then characterize absorbing states coordinate-wise and isolate two structural sufficient conditions, namely inactivity and frozen dialogue. At any latent absorbing state, deterministic voting yields a constant observed outcome, while stochastic voting yields i.i.d. outcomes with an explicit push-forward law. This separation distinguishes the role of the dynamics from that of the decision procedure. As a worked example, an extended pairwise dialogue mechanism with persuasion–resistance attributes is embedded into the framework; its nopersuasiveness regime (with strictly positive resistance) provides a concrete deadlock case.