aggregate other-regarding preferences

This paper addresses three enduring problems in welfare economics: the ambiguous conceptualization of “general welfare” in constitutional frameworks; the oversimplified modeling of individual preferences as homogeneous, self-interested, and consequentialist; and the lack of a rigorous, operational definition of “fairness.” To resolve these, we propose a novel social welfare function framework that—uniquely—systematically integrates heterogeneous social preferences, deontological motivations (e.g., duty and justice concerns), and meta-preferences (i.e., preferences over aggregation rules themselves). Employing normative welfare analysis, social choice theory, and axiomatic modeling, our approach transcends conventional simplifying assumptions. It enables welfare evaluation to authentically reflect pluralistic societal values—including demographic flourishing, distributive justice, and procedural legitimacy—thereby furnishing a theoretically grounded basis for inclusive, democracy-compatible public policy design.

This study addresses the aggregation of individual preferences that incorporate altruistic or other-regarding concerns while respecting individual sovereignty. By introducing an individual sovereignty axiom and imposing two key requirements—sequential aggregation consistency and consensus stability—the authors demonstrate that social utility must be a linear combination of individuals’ self-interested utilities, with other-regarding concerns influencing only the weighting scheme. Moreover, the framework uniquely identifies the geometric mean as the rule for inter-individual weight allocation. Employing an axiomatic approach grounded in social choice theory, the analysis extends to settings with feasibility constraints and subjective uncertainty, thereby establishing both the structural role of other-regarding preferences and the uniqueness of the admissible weighting mechanism.

In multi-agent systems, inappropriate selection of the social cost function (SCF) often leads to resource allocation imbalance. This paper addresses the heterogeneous individual utility aggregation problem by— for the first time—coupling the utility comparability hierarchy (from ordinal to cardinal) from social choice theory with a formal fairness axiom system, thereby establishing a rigorous criterion framework for SCF selection. Methodologically, it integrates social choice theory, axiomatic welfare economics, and distributed control modeling to achieve provably optimal allocation of water and transportation resources. Theoretically, it precisely characterizes the necessary conditions under which classical SCFs—utilitarian, Nash, and maximin—are applicable and normatively justified. Empirical validation demonstrates that the framework ensures principled, simultaneous optimization of fairness and efficiency, providing both theoretical guarantees and actionable design principles for equitable resource distribution in heterogeneous agent systems.

This paper addresses the challenge of simultaneously aggregating individual preferences and private information in public project decision-making to maximize social welfare while resisting strategic manipulation. We propose a two-stage mechanism: Stage I aggregates Bayesian signals via prediction markets or betting mechanisms; Stage II selects projects using quadratic transfers for preference aggregation. Our work is the first to jointly model both preference and information motives, yielding a fully strategy-proof, incentive-compatible mechanism robust against all forms of manipulation. We derive the first non-asymptotic price-of-anarchy bound for quadratic transfers and prove that the price-of-anarchy converges to 1 in large populations. Under mild assumptions, the mechanism guarantees budget balance and robust price-of-anarchy—i.e., welfare remains close to optimal even under adversarial deviations.

This work addresses a critical limitation in current reinforcement learning from human feedback (RLHF) methods, which implicitly aggregate heterogeneous preferences without explicit control over social choice axioms, leading to opaque normative assumptions in the learned reward functions. To remedy this, the authors propose the Differential Voting framework, which for the first time reformulates classical voting rules—such as Copeland and Kemeny—as instance-level differentiable loss functions, ensuring that the optimization objective precisely aligns with a specified voting mechanism at the population level. Through consistency analysis, gradient field modeling, and asymptotic studies of smoothing parameters, the paper systematically uncovers the geometric structure of these losses and their correspondence to foundational social choice axioms, enabling principled axiom-based trade-offs in RLHF. Experiments confirm the alignment between the proposed method and its target voting rules, and the implementation is publicly released.

This study addresses the challenge of explaining and predicting aggregate consumption behavior and achieving fair resource allocation without access to individual utility information. The authors propose a Constructive Rationalization Method (CRM) that constructs an auxiliary market populated by artificial consumers endowed with easily computable demand functions. By dynamically adjusting the number of artificial consumers and their wealth allocations based on observed aggregate demand, CRM approximates real-market behavior. This approach provides the first generalization risk guarantee for learning aggregate demand functions and achieves an approximation of proportional fairness—all while preserving individual privacy and eschewing any reliance on individual-level utility data. Empirical results demonstrate that CRM effectively predicts group-level consumption patterns and yields resource allocations closely aligned with proportional fairness.

This study addresses the fundamental challenge in budget aggregation of simultaneously achieving proportional fairness, maximizing social welfare, and ensuring strategyproofness. Focusing on the ℓ₁ utility model, the work proposes UtilProp—the first truthful proportional mechanism that attains the optimal worst-case social welfare ratio under single-peaked preferences—and introduces the notion of “decomposability” to design GreedyDecomp, an approximately optimal mechanism. Theoretical analysis shows that UtilProp dominates all known single-peaked, proportional, and truthful mechanisms in terms of social welfare, while GreedyDecomp achieves a 2-approximation of UtilDecomp’s welfare and preserves the optimal worst-case welfare ratio, all while maintaining decomposability. By integrating mechanism design, worst-case analysis, and approximation algorithms, this research systematically characterizes the trade-offs among these three desiderata.

This study addresses how to aggregate individual preferences—each focused on distinct quantiles of outcome distributions, such as downside risk, typical performance, or upside potential—into collectively Pareto-efficient decisions. The authors develop a quantile preference model and employ an axiomatic approach combined with spectral weighting analysis to characterize social aggregation rules over general and elliptically contoured distribution domains. Their main contributions include a spectral support theorem showing that Pareto-consistent aggregation can only assign positive weight to quantiles already represented in society; an equivalence between representative-quantile aggregation and dictatorial mechanisms; and a full characterization of necessary and sufficient conditions for finite or threshold-based Pareto efficiency, along with the associated reducible structures.

This study addresses income inequality from the perspective of equality of opportunity, distinguishing between inequality arising from circumstances beyond individual control and disparities stemming from voluntary risk-taking. To this end, it develops an axiomatic framework that first computes expected utilities within groups sharing identical environmental characteristics and then aggregates group welfare using a one-parameter opportunity-sensitive social welfare function. Integrating ambiguity decision theory, expected utility analysis, and mean-divergence decomposition, the framework introduces an opportunity stochastic dominance criterion and yields a decomposable measure of overall inequality into between-group opportunity inequality and within-group risk-related dispersion. The analysis derives multiple equivalent representations of the social welfare function, offering a practical toolkit for normative welfare evaluation and inequality decomposition.

This study investigates whether dictatorship remains unavoidable in collective multi-class classification when surjectivity—requiring every class to be nonempty—is satisfied only with high probability and the aggregation function is far from constant. By extending impossibility theorems from social choice theory to a general setting featuring probabilistic surjectivity and non-degenerate aggregation functions, and leveraging the combinatorial framework of Alekseev and Filmus together with probabilistic methods under symmetric i.i.d. assumptions, the authors demonstrate that even if surjectivity holds merely with probability $1-\varepsilon$, dictatorship is still inevitable provided the aggregation function is not approximately constant. This result is further generalized to the setting of equivalence relation aggregation.