This paper develops a distributed algorithm for decision/awareness propagation in mobile-agent networks. A time-dependent proximity network topology is adopted to represent a mobile-agent scenario. The agent-interaction policy formulated here is inspired from the recently developed language-measure theory. Analytical results related to convergence of statistical moments of agent states are derived and then validated by numerical simulation. The results show that a single (user-defined) parameter in the agent interaction policy can be identified to control the trade-off between Propagation Radius (i.e. how far a decision spreads from its source) and Localisation Gradient (i.e. the extent to which the spatial variations may affect localisation of the source) as well as the temporal convergence properties.