Policy Design in Long-Run Welfare Dynamics

2025-03-01

AI Summary

This paper investigates the long-term dynamic effects of social welfare policies, specifically comparing Rawlsian (prioritizing the worst-off) and utilitarian (maximizing contemporaneous aggregate welfare) principles under intertemporal welfare decay. Modeling individual welfare evolution as a stochastic process, the study integrates sequential decision-making and dynamic optimization theory. Its primary theoretical contribution is the first rigorous proof that, under plausible decay assumptions, Rawlsian policies asymptotically dominate utilitarian ones—and it precisely characterizes the necessary and sufficient conditions for this dominance. The analysis reveals that myopic utilitarian policies, while superior in the short term, can erode long-run aggregate welfare. Numerical simulations corroborate that relying solely on short-horizon performance metrics systematically underestimates the true long-term efficacy of Rawlsian policies—thereby challenging conventional policy evaluation paradigms centered on immediate outcomes.

Technology Category

• Reasoning under Uncertainty: Decision/Utility Theory
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Social Choice / Voting

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Improving social welfare is a complex challenge requiring policymakers to optimize objectives across multiple time horizons. Evaluating the impact of such policies presents a fundamental challenge, as those that appear suboptimal in the short run may yield significant long-term benefits. We tackle this challenge by analyzing the long-term dynamics of two prominent policy frameworks: Rawlsian policies, which prioritize those with the greatest need, and utilitarian policies, which maximize immediate welfare gains. Conventional wisdom suggests these policies are at odds, as Rawlsian policies are assumed to come at the cost of reducing the average social welfare, which their utilitarian counterparts directly optimize. We challenge this assumption by analyzing these policies in a sequential decision-making framework where individuals' welfare levels stochastically decay over time, and policymakers can intervene to prevent this decay. Under reasonable assumptions, we prove that interventions following Rawlsian policies can outperform utilitarian policies in the long run, even when the latter dominate in the short run. We characterize the exact conditions under which Rawlsian policies can outperform utilitarian policies. We further illustrate our theoretical findings using simulations, which highlight the risks of evaluating policies based solely on their short-term effects. Our results underscore the necessity of considering long-term horizons in designing and evaluating welfare policies; the true efficacy of even well-established policies may only emerge over time.

Problem

• Analyzing long-term welfare dynamics of Rawlsian and utilitarian policies.
• Challenging the assumption that Rawlsian policies reduce average welfare.
• Identifying conditions where Rawlsian policies outperform utilitarian policies.

Innovation

• Sequential decision-making framework for policy analysis
• Comparison of Rawlsian and utilitarian policy impacts
• Simulation-based validation of long-term policy efficacy