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

## TL;DR

This paper introduces a multiplicative model that generates a beta-like survival function, effectively fitting and potentially predicting UK EU referendum results by capturing nonlinear effects in social data distributions.

## Contribution

The paper presents a novel multiplicative process that produces a beta-like survival function, improving modeling of social phenomena like election outcomes.

## Key findings

- Beta-like survival function closely fits referendum data
- Model captures nonlinear tail effects in social distributions
- Potential for improved election forecasting with covariates

## Abstract

Human dynamics and sociophysics suggest statistical models that may explain and provide us with better insight into social phenomena. Contextual and selection effects tend to produce extreme values in the tails of rank-ordered distributions of both census data and district-level election outcomes. Models that account for this nonlinearity generally outperform linear models. Fitting nonlinear functions based on rank-ordering census and election data therefore improves the fit of aggregate voting models. This may help improve ecological inference, as well as election forecasting in majoritarian systems. We propose a generative multiplicative decrease model that gives rise to a rank-order distribution, and facilitates the analysis of the recent UK EU referendum results. We supply empirical evidence that the beta-like survival function, which can be generated directly from our model, is a close fit to the referendum results, and also may have predictive value when covariate data are available.