Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition

• URL: http://arxiv.org/abs/2604.05523v1
• Date: Tue, 07 Apr 2026 07:23:51 GMT
• Title: Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition
• Abstract summary: This paper introduces textbfMarket-Bench, a benchmark that evaluates the capabilities of large language models (LLMs) in economically-relevant tasks.<n>We construct a multi-agent supply chain economic model where LLMs act as retailer agents responsible for procuring and retailing merchandise.<n>Market-Bench logs complete trajectories of bids, prices, slogans, sales, and balance-sheet states, enabling automatic evaluation with economic, operational, and semantic metrics.
• Score: 102.73682784993169
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability of large language models (LLMs) to manage and acquire economic resources remains unclear. In this paper, we introduce \textbf{Market-Bench}, a comprehensive benchmark that evaluates the capabilities of LLMs in economically-relevant tasks through economic and trade competition. Specifically, we construct a configurable multi-agent supply chain economic model where LLMs act as retailer agents responsible for procuring and retailing merchandise. In the \textbf{procurement} stage, LLMs bid for limited inventory in budget-constrained auctions. In the \textbf{retail} stage, LLMs set retail prices, generate marketing slogans, and provide them to buyers through a role-based attention mechanism for purchase. Market-Bench logs complete trajectories of bids, prices, slogans, sales, and balance-sheet states, enabling automatic evaluation with economic, operational, and semantic metrics. Benchmarking on 20 open- and closed-source LLM agents reveals significant performance disparities and winner-take-most phenomenon, \textit{i.e.}, only a small subset of LLM retailers can consistently achieve capital appreciation, while many hover around the break-even point despite similar semantic matching scores. Market-Bench provides a reproducible testbed for studying how LLMs interact in competitive markets.

Related papers

• Competitive Market Behavior of LLMs [10.688937502565592]
  We replace human subjects with large language models (LLMs) to test market mechanisms designed for humans.<n>We find that markets populated by LLM agents exhibit slower or no convergence towards market equilibrium.<n>We also run a lexical analysis of Chain-of-Thought traces generated by the agents.
  arXiv  Detail & Related papers  (2026-09-02T13:24:42Z)
• StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets? [44.10622904101254]
  Large language models (LLMs) have recently demonstrated strong capabilities as autonomous agents.<n>We introduce StockBench, a benchmark designed to evaluate LLM agents in realistic, multi-month stock trading environments.<n>Our evaluation shows that while most LLM agents struggle to outperform the simple buy-and-hold baseline, several models demonstrate the potential to deliver higher returns and manage risk more effectively.
  arXiv  Detail & Related papers  (2025-10-02T16:54:57Z)
• AI Playing Business Games: Benchmarking Large Language Models on Managerial Decision-Making in Dynamic Simulations [0.0]
  This research analyses a novel benchmark using a business game for the decision making in business.<n>The research contributes to the recent literature on AI by proposing a reproducible, open-access management simulator.
  arXiv  Detail & Related papers  (2025-09-30T14:43:05Z)
• On the Performance of LLMs for Real Estate Appraisal [5.812129569528997]
  This study examines how Large Language Models (LLMs) can democratize access to real estate insights by generating competitive and interpretable house price estimates.<n>We evaluate leading LLMs on diverse international housing datasets, comparing zero-shot, few-shot, market report-enhanced, and hybrid prompting techniques.<n>Our results show that LLMs effectively leverage hedonic variables, such as property size and amenities, to produce meaningful estimates.
  arXiv  Detail & Related papers  (2025-06-13T14:14:40Z)
• A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs [74.35290684163718]
  A primary challenge in large language model (LLM) development is their onerous pre-training cost. This paper explores a promising paradigm to improve LLM pre-training efficiency and quality by leveraging a small language model (SLM)
  arXiv  Detail & Related papers  (2024-10-24T14:31:52Z)
• Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions [0.0]
  Machine-learning technologies are seeing increased deployment in real-world market scenarios.<n>We explore the strategic behaviors of large language models (LLMs) when deployed as autonomous agents in multi-commodity markets.
  arXiv  Detail & Related papers  (2024-09-19T20:10:40Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models.<n>It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop.<n>Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• Knowledge Fusion of Large Language Models [73.28202188100646]
  This paper introduces the notion of knowledge fusion for large language models (LLMs) We externalize their collective knowledge and unique strengths, thereby elevating the capabilities of the target model beyond those of any individual source LLM. Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation.
  arXiv  Detail & Related papers  (2024-01-19T05:02:46Z)
• Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models [53.620827459684094]
  Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks. We propose the first open-source comprehensive framework for exploring LLMs for credit scoring. We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
  arXiv  Detail & Related papers  (2023-10-01T03:50:34Z)
• AgentBench: Evaluating LLMs as Agents [99.12825098528212]
  Large Language Model (LLM) as agents has been widely acknowledged recently.<n>We present AgentBench, a benchmark that consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
  arXiv  Detail & Related papers  (2023-08-07T16:08:11Z)

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.