# Modelling election dynamics and the impact of disinformation

## TL;DR

This paper models election dynamics by treating information flow as the input, allowing analysis of election prediction, disinformation impact, and information management strategies through a novel information-driven dynamical systems approach.

## Contribution

It introduces an information-based modeling framework for electoral dynamics, contrasting with traditional ad hoc models, to analyze election outcomes and disinformation effects.

## Key findings

- Model predicts election poll fluctuations based on information flow.
- Disinformation impact on election outcomes can be quantitatively assessed.
- Optimal information management strategies can be derived from the model.

## Abstract

Complex dynamical systems driven by the unravelling of information can be modelled effectively by treating the underlying flow of information as the model input. Complicated dynamical behaviour of the system is then derived as an output. Such an information-based approach is in sharp contrast to the conventional mathematical modelling of information-driven systems whereby one attempts to come up with essentially {\it ad hoc} models for the outputs. Here, dynamics of electoral competition is modelled by the specification of the flow of information relevant to election. The seemingly random evolution of the election poll statistics are then derived as model outputs, which in turn are used to study election prediction, impact of disinformation, and the optimal strategy for information management in an election campaign.