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Human dynamics and sociophysics suggest statistical models that may explainand provide us with better insight into social phenomena. Contextual andselection effects tend to produce extreme values in the tails of rank-ordereddistributions of both census data and district-level election outcomes. Modelsthat account for this nonlinearity generally outperform linear models. Fittingnonlinear functions based on rank-ordering census and election data thereforeimproves the fit of aggregate voting models. This may help improve ecologicalinference, as well as election forecasting in majoritarian systems.  We propose a generative multiplicative decrease model that gives rise to arank-order distribution, and facilitates the analysis of the recent UK EUreferendum results. We supply empirical evidence that the beta-like survivalfunction, which can be generated directly from our model, is a close fit to thereferendum results, and also may have predictive value when covariate data areavailable.

A multiplicative process for generating a beta-like survival function with application to the UK 2016 EU referendum results