Social influence with recurrent mobility and multiple options

Abstract

In this paper, we discuss the possible generalizations of the social influence with recurrent mobility (SIRM) model [Phys. Rev. Lett. 112, 158701 (2014)PRLTAO0031-900710.1103/PhysRevLett.112.158701]. Although the SIRM model worked approximately satisfying when U.S. election was modeled, it has its limits: it has been developed only for two-party systems and can lead to unphysical behavior when one of the parties has extreme vote share close to 0 or 1. We propose here generalizations to the SIRM model by its extension for multiparty systems that are mathematically well-posed in case of extreme vote shares, too, by handling the noise term in a different way. In addition, we show that our method opens alternative applications for the study of elections by using an alternative calibration procedure and makes it possible to analyze the influence of the "free will" (creating a new party) and other local effects for different commuting network topologies.

Statistical Physics Of Opinion Formation: Is it a SPOOF?

“…Based on the same model, the outcome of the British referendum on leaving the European Union could have been foreseen [87,88]. Another possibility is to compare the model results with some historical data obtained from various sources, like elections [89][90][91], market shares [65], censuses [92], etc. The problem of this approach is that some models might have been already calibrated to the same data, so such a validation may not be completely reliable.…”

Voter model on networks and the multivariate beta distribution

“…The second change is the normalization of n j by N j and we use v j = n j /N j . The mathematical reason for the modification is to avoid the ill-posedness in the original SIRM model [33]. As we shall show shortly, if we normalize as indicated, the noise term becomes proportional to v i (1 − v i ) as in the Wright-Fisher diffusion equation and it does not break the condition v i ∈ (0, 1) even when v i approaches 0 or 1.…”

Compartmental voter model

“…Some of the recent approaches in opinion dynamics [31][32][33][34][35][36][37][38][39][40] have combined empirical analysis of the detailed electoral data (opinions being observed over multiple electoral units during a single time step) with numerical modeling. Still in [34,38] various groups of researchers have made the same underlying assumption: that the electoral units are mutually independent observations from mostly the same stationary distribution of opinions.…”