An algorithm for a fairer and better voting system

TL;DR

This paper introduces a novel ranked voting algorithm designed to improve fairness and accuracy in selecting the best candidate, demonstrating superior performance over traditional voting methods through AI-based simulations.

Contribution

The paper presents a new voting algorithm that outperforms existing methods like Instant-Runoff and FPTP, supported by AI simulations and comparative analysis.

Findings

The new algorithm outperforms traditional voting systems under certain conditions.

Simulations show improved fairness and representation.

The approach supports the wisdom of crowds in decision-making.

Abstract

The major finding, of this article, is an ensemble method, but more exactly, a novel, better ranked voting system (and other variations of it), that aims to solve the problem of finding the best candidate to represent the voters. We have the source code on GitHub, for making realistic simulations of elections, based on artificial intelligence for comparing different variations of the algorithm, and other already known algorithms. We have convincing evidence that our algorithm is better than Instant-Runoff Voting, Preferential Block Voting, Single Transferable Vote, and First Past The Post (if certain, natural conditions are met, to support the wisdom of the crowds). By also comparing with the best voter, we demonstrated the wisdom of the crowds, suggesting that democracy (distributed system) is a better option than dictatorship (centralized system), if those certain, natural conditions are met. Voting systems are not restricted to politics, they are ensemble methods for artificial intelligence, but the context of this article is natural intelligence. It is important to find a system that is fair (e.g. freedom of expression on the ballot exists), especially when the outcome of the voting system has social impact: some voting systems have the unfair inevitability to trend (over time) towards the same two major candidates (Duverger's law).