Information Aggregation with AI Agents

• URL: http://arxiv.org/abs/2604.20050v1
• Date: Tue, 21 Apr 2026 23:17:24 GMT
• Title: Information Aggregation with AI Agents
• Abstract summary: We conduct a controlled experiment where AI agents trade in a prediction market after receiving private signals.<n>We find that although the median market is effective at aggregating information in the easy information structures, increasing the complexity has a significant and negative impact.<n>We establish that "smarter" AI agents perform better at aggregation and they are more profitable.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing price movements? We conduct a controlled experiment where AI agents trade in a prediction market after receiving private signals, measuring information aggregation by the log error of the last price. We find that although the median market is effective at aggregating information in the easy information structures, increasing the complexity has a significant and negative impact, suggesting that AI agents may suffer from the same limitations as humans when reasoning about others. Consistent with our theoretical predictions, information aggregation remains unaffected by allowing cheap talk communication, changing the duration of the market or initial price, and strategic prompting-thus demonstrating that prediction markets are robust. We establish that "smarter" AI agents perform better at aggregation and they are more profitable. Surprisingly, giving them feedback about past performance makes them worse at aggregation and reduces their profits.

Related papers

• One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread [39.0786847687173]
  Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation.<n>We introduce a social-learning lens for this setting by extending the classical Bayesian cascade model with the AI indicator as a shared public signal.<n>We show that AI changes what public history means. Through this view, we show that AI changes what public history means.
  arXiv  Detail & Related papers  (2026-08-31T06:27:21Z)
• Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems [103.90831279037094]
  LLM-based multi-agent trading systems are moving rapidly from research prototypes to live deployments that control real assets.<n>A corrupted signal can propagate to the final decision and translate into realized financial loss.<n>We present the first systematic empirical study in the financial domain to characterize how an adversarial signal enters a multi-agent trading system.
  arXiv  Detail & Related papers  (2026-08-25T05:07:37Z)
• When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption [45.10829096284761]
  Complex AI systems can appear highly accurate while making costly errors or embedding hidden defects.<n>This paper provides the first experimental evidence on the role of information asymmetries and disclosure designs in shaping user adoption of AI systems.
  arXiv  Detail & Related papers  (2026-01-29T12:49:28Z)
• How AI Agents Follow the Herd of AI? Network Effects, History, and Machine Optimism [7.1683021355290295]
  This study investigates how AI agents navigate network-effect games, where individual payoffs depend on peer participatio--a context underexplored in multi-agent systems.<n>We introduce a novel workflow design using large language model (LLM)-based agents in repeated decision-making scenarios.
  arXiv  Detail & Related papers  (2025-12-12T12:14:48Z)
• Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents [69.58565132975504]
  Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks.<n>We present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive multi-agent trading.
  arXiv  Detail & Related papers  (2025-02-25T08:41:01Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets [9.053163124987535]
  We examine how AI agents using Q-learning engage in tacit collusion in two-sided markets. Our experiments reveal that AI-driven platforms achieve higher collusion levels compared to Bertrand competition. Increased network externalities significantly enhance collusion, suggesting AI algorithms exploit them to maximize profits.
  arXiv  Detail & Related papers  (2024-07-04T17:57:56Z)
• Language Models Can Reduce Asymmetry in Information Markets [100.38786498942702]
  We introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information.
  arXiv  Detail & Related papers  (2024-03-21T14:48:37Z)
• Bloated Disclosures: Can ChatGPT Help Investors Process Information? [1.3594158136398242]
  Generative AI tools such as ChatGPT can fundamentally change the way investors process information.<n>We probe the economic usefulness of these tools in summarizing complex corporate disclosures using the stock market as a laboratory.<n>We show that bloated disclosure is associated with adverse capital market consequences, such as lower price efficiency and higher information asymmetry.
  arXiv  Detail & Related papers  (2023-06-17T01:22:08Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [48.87381259980254]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training.<n>Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Mechanisms that Incentivize Data Sharing in Federated Learning [90.74337749137432]
  We show how a naive scheme leads to catastrophic levels of free-riding where the benefits of data sharing are completely eroded. We then introduce accuracy shaping based mechanisms to maximize the amount of data generated by each agent.
  arXiv  Detail & Related papers  (2022-07-10T22:36:52Z)
• A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction [100.9772316028191]
  In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation.
  arXiv  Detail & Related papers  (2022-05-01T05:12:22Z)

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.