Non-spatial Probabilistic Condorcet Election Methodology

TL;DR

This paper introduces a probabilistic Condorcet election methodology that extends traditional models to handle non-spatial and discrete issue sets, enabling more flexible and realistic decision-making analysis.

Contribution

It develops a novel probabilistic framework for Condorcet elections, broadening applicability to complex, non-spatial, and discrete issues in bargaining and conflict models.

Findings

Implemented in prototypes for parliament subset selection.

Addresses strategy optimization in one-dimensional issues.

Extends models beyond single-dimensional, deterministic assumptions.

Abstract

There is a class of models for pol/mil/econ bargaining and conflict that is loosely based on the Median Voter Theorem which has been used with great success for about 30 years. However, there are fundamental mathematical limitations to these models. They apply to issues which can be represented on a single one-dimensional continuum. They represent fundamental group decision process by a deterministic Condorcet Election: deterministic voting by all actors, and deterministic outcomes of each vote. This work provides a methodology for addressing a broader class of problems. The first extension is to continuous issue sets where the consequences of policies are not well-described by a distance measure or utility is not monotonic in distance. The second fundamental extension is to inherently discrete issue sets. Because the options cannot easily be mapped into a multidimensional space so that the utility depends on distance, we refer to it as a non-spatial issue set. The third, but most fundamental, extension is to represent the negotiating process as a probabilistic Condorcet election (PCE). The actors' generalized voting is deterministic, but the outcomes of the votes is probabilistic. The PCE provides the flexibility to make the first two extensions possible; this flexibility comes at the cost of less precise predictions and more complex validation. The methodology has been implemented in two proof-of-concept prototypes which address the subset selection problem of forming a parliament, and strategy optimization for one-dimensional issues.