Quantitative Relaxations of Arrow's Axioms

In this paper we develop a novel approach to relaxing Arrow's axioms for voting rules, addressing a long-standing critique in social choice theory. Classical axioms (often styled as fairness axioms or fairness criteria) are assessed in a binary manner, so that a voting rule fails the axiom if it fails in even one corner case. Many authors have proposed a probabilistic framework to soften the axiomatic approach. Instead of immediately passing to random preference profiles, we begin by measuring the degree to which an axiom is upheld or violated on a given profile. We focus on two foundational axioms-Independence of Irrelevant Alternatives (IIA) and Unanimity (U) -and extend them to take values in 0, 1. Our σ₈₈₀ measures the stability of a voting rule when candidates are removed from consideration, while σₔ captures the degree to which the outcome respects majority preferences. Together, these metrics quantify how a voting rule navigates the fundamental trade-off highlighted by Arrow's Theorem. We show that σ₈₈₀ 1 recovers classical IIA, and σₔ>0 recovers classical Unanimity, allowing a quantitative restatement of Arrow's Theorem. In the empirical part of the paper, we test these metrics on two kinds of data: a set of over 1000 ranked choice preference profiles from Scottish local elections, and a batch of synthetic preference profiles generated with a Bradley-Terry-type model. We use those to investigate four positional voting rules-Plurality, 2-Approval, 3-Approval, and the Borda rule-as well as the iterative rule known as Single Transferable Vote (STV). The Borda rule consistently receives the highest σ₈₈₀ and σₔ scores across observed and synthetic elections. This compares interestingly with a recent result of Maskin showing that weakening IIA to include voter preference intensity uniquely selects Borda.