Abstract
Human dynamics and sociophysics suggest statistical models that may explain and provide us with better insight into social phenomena. Here we propose a generative model based on a stochastic differential equation that allows us to analyse the polls leading up to the UK 2016 EU referendum. After a preliminary analysis of the time series of poll results, we provide empirical evidence that the beta distribution, which is a natural choice when modelling proportions, fits the marginal distribution of this time series. We also provide evidence of the predictive power of the proposed model.
Highlights
Recent interest in complex social systems, such as social networks, the world-wide-web, messaging networks and mobile phone networks (Barabási 2016), has led researchers to investigate the processes that could explain the dynamics of human behaviour within these networks
Human dynamics is not limited to the study of behaviour in communication networks, and has a broader remit similar to the aims of sociophysics (Galam 2008, Sen and Chakrabarti 2014), which uses concepts and methods from statistical physics to investigate social phenomena, opinion formation and political behaviour
We make use of stochastic differential equations (SDEs) (Mackevicius 2011, Evans 2013), a model widely used in physics and mathematical finance, which can be viewed as a continuous approximation to a discrete process modelling how the polls vary over time
Summary
Human dynamics and sociophysics suggest statistical models that may explain and provide us with licence. We propose a generative model based on a stochastic. Any further distribution of this work must maintain differential equation that allows us to analyse the polls leading up to the UK 2016 EU referendum. After a preliminary analysis of the time series of poll results, we provide empirical evidence that the author(s) and the title of the work, journal citation beta distribution, which is a natural choice when modelling proportions, fits the marginal distribution and DOI. We provide evidence of the predictive power of the proposed model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
