Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare

• URL: http://arxiv.org/abs/2510.01164v1
• Date: Wed, 01 Oct 2025 17:52:31 GMT
• Title: Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare
• Authors: Zhengliang Shi, Ruotian Ma, Jen-tse Huang, Xinbei Ma, Xingyu Chen, Mengru Wang, Qu Yang, Yue Wang, Fanghua Ye, Ziyang Chen, Shanyi Wang, Cixing Li, Wenxuan Wang, Zhaopeng Tu, Xiaolong Li, Zhaochun Ren, Linus,
• Abstract summary: Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare.<n>We introduce the Social Welfare Function Benchmark, a dynamic simulation environment where an LLM acts as a sovereign allocator.<n>We evaluate 20 state-of-the-art LLMs and present the first leaderboard for social welfare allocation.
• Score: 87.06241096619112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare. However, the principles and values that guide these models when distributing scarce societal resources remain largely unexamined. To address this, we introduce the Social Welfare Function (SWF) Benchmark, a dynamic simulation environment where an LLM acts as a sovereign allocator, distributing tasks to a heterogeneous community of recipients. The benchmark is designed to create a persistent trade-off between maximizing collective efficiency (measured by Return on Investment) and ensuring distributive fairness (measured by the Gini coefficient). We evaluate 20 state-of-the-art LLMs and present the first leaderboard for social welfare allocation. Our findings reveal three key insights: (i) A model's general conversational ability, as measured by popular leaderboards, is a poor predictor of its allocation skill. (ii) Most LLMs exhibit a strong default utilitarian orientation, prioritizing group productivity at the expense of severe inequality. (iii) Allocation strategies are highly vulnerable, easily perturbed by output-length constraints and social-influence framing. These results highlight the risks of deploying current LLMs as societal decision-makers and underscore the need for specialized benchmarks and targeted alignment for AI governance.

Related papers

• The Role of Social Learning and Collective Norm Formation in Fostering Cooperation in LLM Multi-Agent Systems [13.628908663240564]
  We introduce a CPR simulation framework that removes explicit reward signals and embeds cultural-evolutionary mechanisms.<n>We examine norm evolution across a $2times2$ grid of environmental and social initialisations.<n>Our results reveal systematic model differences in sustaining cooperation and norm formation.
  arXiv  Detail & Related papers  (2025-10-16T07:59:31Z)
• Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games [87.5673042805229]
  How large language models balance self-interest and collective well-being is a critical challenge for ensuring alignment, robustness, and safe deployment.<n>We adapt a public goods game with institutional choice from behavioral economics, allowing us to observe how different LLMs navigate social dilemmas.<n>Surprisingly, we find that reasoning LLMs, such as the o1 series, struggle significantly with cooperation.
  arXiv  Detail & Related papers  (2025-06-29T15:02:47Z)
• Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values [13.798198972161657]
  A number of societal problems involve the distribution of resources, where fairness, along with economic efficiency, play a critical role in the desirability of outcomes.<n>This paper examines whether large language models (LLMs) adhere to fundamental fairness concepts and investigate their alignment with human preferences.
  arXiv  Detail & Related papers  (2025-02-01T04:24:47Z)
• Value Compass Benchmarks: A Platform for Fundamental and Validated Evaluation of LLMs Values [76.70893269183684]
  Large Language Models (LLMs) achieve remarkable breakthroughs.<n> aligning their values with humans has become imperative for their responsible development.<n>There still lack evaluations of LLMs values that fulfill three desirable goals.
  arXiv  Detail & Related papers  (2025-01-13T05:53:56Z)
• Social Debiasing for Fair Multi-modal LLMs [59.61512883471714]
  Multi-modal Large Language Models (MLLMs) have dramatically advanced the research field and delivered powerful vision-language understanding capabilities.<n>These models often inherit deep-rooted social biases from their training data, leading to uncomfortable responses with respect to attributes such as race and gender.<n>This paper addresses the issue of social biases in MLLMs by introducing a comprehensive counterfactual dataset with multiple social concepts.
  arXiv  Detail & Related papers  (2024-08-13T02:08:32Z)
• Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approaches [69.73783026870998]
  This work proposes a novel framework, ValueLex, to reconstruct Large Language Models' unique value system from scratch. Based on Lexical Hypothesis, ValueLex introduces a generative approach to elicit diverse values from 30+ LLMs. We identify three core value dimensions, Competence, Character, and Integrity, each with specific subdimensions, revealing that LLMs possess a structured, albeit non-human, value system.
  arXiv  Detail & Related papers  (2024-04-19T09:44:51Z)
• Prompting Fairness: Integrating Causality to Debias Large Language Models [19.76215433424235]
  Large language models (LLMs) are susceptible to generating biased and discriminatory responses.<n>We propose a causality-guided debiasing framework to tackle social biases.
  arXiv  Detail & Related papers  (2024-03-13T17:46:28Z)
• Do LLM Agents Exhibit Social Behavior? [5.094340963261968]
  State-Understanding-Value-Action (SUVA) is a framework to systematically analyze responses in social contexts. It assesses social behavior through both their final decisions and the response generation processes leading to those decisions. We demonstrate that utterance-based reasoning reliably predicts LLMs' final actions.
  arXiv  Detail & Related papers  (2023-12-23T08:46:53Z)
• Heterogeneous Value Alignment Evaluation for Large Language Models [91.96728871418]
  Large Language Models (LLMs) have made it crucial to align their values with those of humans. We propose a Heterogeneous Value Alignment Evaluation (HVAE) system to assess the success of aligning LLMs with heterogeneous values.
  arXiv  Detail & Related papers  (2023-05-26T02:34:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.