Social Mechanism Design: A Low-Level Introduction

How do we deal with the fact that agents have preferences over both decision outcomes and the rules or procedures used to make decisions? If we create rules for aggregating preferences over rules, it would appear that we run into infinite regress with preferences and rules at successively higher "levels." The starting point of our analysis is the claim that infinite regress should not be a problem in practice, as any such preferences will necessarily be bounded in complexity and structured coherently in accordance with some (possibly latent) normative principles. Our core contributions are (1) the identification of simple, intuitive preference structures at low levels that can be generalized to form the building blocks of preferences at higher levels, and (2) the development of algorithms for maximizing the number of agents with such low-level preferences who will "accept" a decision. We analyze algorithms for acceptance maximization in two different domains: asymmetric dichotomous choice and constitutional amendment. In both settings we study the worst-case performance of the appropriate algorithms, and reveal circumstances under which universal acceptance is possible. In particular, we show that constitutional amendment procedures proposed recently by Abramowitz, Shapiro, and Talmon (2021) can achieve universal acceptance.