Contents

Description of CHPV and GV

Introduction
Analogy
Weightings
Voting
Counting
Outcomes
Party-List
Summary

Evaluations of CHPV and GV

Ranked Ballot

Introduction (RB)
General Criteria
Majority Criteria
Clones & Teaming
Teaming Thresholds
Summary (RB)

Party-List

Introduction (PL)
Diagrams & Maps
CHPV Maps
Optimality
Party Cloning
Proportionality
Summary (PL)

Comparisons of CHPV with other voting systems

Single-Winner

Introduction (SW)
Plurality (FPTP)
Borda Count
Geometric Voting
Positional Voting
Condorcet Methods
AV (IRV)
Plur. Rule Methods
Summary (SW)

Multiple-Winner

Introduction (MW)
STV
Party-List
PL ~ Hare
PL ~ Droop
~ Maps Opt PC Pro
PL ~ D'Hondt
~ Maps Opt PC Pro
PL ~ Sainte-Laguë
~ Maps Opt PC Pro
Mixed Member Sys
Summary (MW)

Conclusions

Ranked Ballot CHPV
Party-List CHPV

General

Table of Contents

Map Construction

Table of Contents

Mathematical Proofs

Table of Contents
Notation & Formats

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Home > Proofs > Positional Voting > CV3
Last Revision: 25 May 2020

Mathematical Proofs: Positional Voting

Proof CV3: Consensus and Polarization Indices for Geometric Voting

The standard positional voting vector for geometric voting with a common ratio of r is given below.

Proof CV3a

Both bias indices are determined using normalized vectors only. The normalized vector for geometric voting is achieved by subtracting rN-1 from each weighting and then dividing each one by 1-rN-1. The resultant normalized vector is shown below.

Proof CV3b

The formula for calculating each bias index is given in the Consensus and Polarization Indices for Positional Voting Vectors proof. It is however useful to determine the sum of all the preference weightings first. Geometric voting derives its name from the fact that its preference weightings form a geometric progression. As such, the standard mathematical formula for determining the sum of the first N terms in a geometric progression is used here.

Proof CV3c

Notice that the sum Σ is a function of both the common ratio r and the number of candidates N. Hence, the two bias indices are also functions of these variables. The first table below shows values of Σ for selected values of r with up to ten candidates. The second table gives the corresponding Consensus Index (CIV) value for the N-candidate GV(r) vector.

Sigma Table for GV Vectors Consensus Index Table for GV Vectors

As the number of candidates tends to infinity (N → ∞), the sum Σ simply reduces to 1/(1-r); see above. Therefore, the two bias indices can be greatly simplified here as shown below.

Proof CV3d

As the number of candidates N increases, the Consensus Index (CIV) for a GV vector converges towards its asymptote; namely its common ratio value of r.


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