Intuitions of Compromise: Utilitarianism vs. Contractualism

TL;DR

This study empirically compares utilitarian and contractualist decision-making approaches, revealing humans' preference for contractualist methods and highlighting discrepancies between human and AI judgments in social choice contexts.

Contribution

It provides the first empirical comparison of utilitarian and contractualist algorithms in preference aggregation, showing human favor towards contractualist methods.

Findings

Humans prefer contractualist aggregation over utilitarian sum.

Large language models significantly differ from human preferences.

Contractualist approach aligns more with intuitive social decision-making.

Abstract

What is the best compromise in a situation where different people value different things? The most commonly accepted method for answering this question -- in fields across the behavioral and social sciences, decision theory, philosophy, and artificial intelligence development -- is simply to add up utilities associated with the different options and pick the solution with the largest sum. This ``utilitarian'' approach seems like the obvious, theory-neutral way of approaching the problem. But there is an important, though often-ignored, alternative: a ``contractualist'' approach, which advocates for an agreement-driven method of deciding. Remarkably, no research has presented empirical evidence directly comparing the intuitive plausibility of these two approaches. In this paper, we systematically explore the proposals suggested by each algorithm (the ``Utilitarian Sum'' and the contractualist ''Nash Product''), using a paradigm that applies those algorithms to aggregating preferences across groups in a social decision-making context. While the dominant approach to value aggregation up to now has been utilitarian, we find that people strongly prefer the aggregations recommended by the contractualist algorithm. Finally, we compare the judgments of large language models (LLMs) to that of our (human) participants, finding important misalignment between model and human preferences.