Will Trump win again in the 2020 election? An answer from a sociophysics model
arXiv:2010.10602 · doi:10.1016/j.physa.2021.125835
Abstract
This paper predicting Trump victory has been submitted before the election and revised after, allowing to add a Foreword and Note Added in Revision to discuss in details the causes of the failure of the prediction. In 2016, Trump was unanimously seen as the loser in the November 8 election. In contrast, using a model of opinion dynamics I have been developing for a few decades within the framework of sociophysics, I predicted his victory against all odds. According to the model, the winning paradoxical martingale of 2016, has been Trump capability to activate frozen prejudices in many voters by provoking their real indignation. However, four year later, Trump shocking outings do not shock anymore, they became devitalized, losing their ability to generate major emotional reactions. Does this mean that this time around he will lose the 2020 election against Biden, as nearly all analysts, pundits and commentators still predict? No, because with frozen prejudices remaining frozen, the spontaneous prejudices will be activated but this time they will benefit to both Biden and Trump. The main ones are the fear of the other candidate policy and the personal stand facing a danger. In addition, Trump presidency having polarized a large part of American voters into narrow-minded anti-Trump and narrow-minded pro-Trump, those I denote in my model as inflexibles, will be driving the dynamics of choices. Both effects, prejudices and inflexibles can either compete or cooperate making their local combination within each state, decisive to determine the faith of the state election. As a result, tiny differences can make the outcome. Based on my rough estimates of associated proportions of inflexibles and prejudices, the model predicts Trump victory in the 2020 November election.
arXiv comment22 pages, 7 figures. The manuscript was predicting a 2020 Trump victory. The update version is enriched with a Foreword and a Note Added in Revision discussing in details both the causes of the failure of the prediction and what has been robust in its making
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