The Census and the Second Law: An Entropic Approach to Optimal Apportionment for the U.S. House of Representatives
A.E. Charman

TL;DR
This paper introduces an entropic method for apportioning U.S. Congressional seats that aims to maximize fairness and proportionality by minimizing relative entropy, with broad applications in political and resource allocation systems.
Contribution
It applies principles of information theory and entropy to develop a novel, mathematically grounded apportionment method that improves fairness in legislative seat distribution.
Findings
The entropic apportionment method aligns with maximizing representational equity.
It can be used to compare different apportionment schemes and district configurations.
The approach is applicable to various multi-constituency resource allocation problems.
Abstract
The Constitutionally mandated task of assigning Congressional seats to the various U.S. States proportional to their represented populations ("according to their numbers") has engendered much contention, but rather less consensus. Using the same principles of entropic inference that underlie the foundations of information theory and statistical thermodynamics, and also enjoy fruitful application in image processing, spectral analysis, machine learning, econometrics, bioinformatics, and a growing number of other fields, we motivate and explore a method for Congressional apportionment based on minimizing relative entropy (also known as Kullback-Leibler divergence), or, equivalently, maximizing Shannon entropy. In terms of communication theory, we might say that the entropic apportionment gives each constituent as equal a voice as possible. If we view representational weight as a finite…
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Taxonomy
TopicsStatistical Mechanics and Entropy
