Sociomeeting: Estimating transmission noise on networks from stationary local order
In this talk we discuss networks of nodes characterised by binary traits that change both endogenously and through nearest-neighbour interaction. We utilise techniques from statistical physics such as the stochastic pair approximation, as well a further exact technique which we developed, to show that the geospatial distribution of a trait carries information about the noisiness of its transmission. It is possible to rank traits based on this transmission noise. Crucially, this ranking is independent of the network topology and similar to the ranking based on purely spatial data. As an example, we show how this can be applied in the field of linguistic typology where we confirm a long-standing hypothesis. We conjecture that similar inferences may be possible in a more general class of Markovian systems. Consequently, in many empirical domains where longitudinal information is not easily available the propensities of traits to change could be estimated from spatial data alone.
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