Language Models Can Reduce Asymmetry in Information Markets

• URL: http://arxiv.org/abs/2403.14443v1
• Date: Thu, 21 Mar 2024 14:48:37 GMT
• Title: Language Models Can Reduce Asymmetry in Information Markets
• Authors: Nasim Rahaman, Martin Weiss, Manuel Wüthrich, Yoshua Bengio, Li Erran Li, Chris Pal, Bernhard Schölkopf,
• Abstract summary: 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.
• Score: 100.38786498942702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limit access to prevent theft. To study this, 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 not only have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. This ability to induce amnesia allows vendors to grant temporary access to proprietary information, significantly reducing the risk of unauthorized retention while enabling agents to accurately gauge the information's relevance to specific queries or tasks. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information. Concretely, our experiments (a) uncover biases in language models leading to irrational behavior and evaluate techniques to mitigate these biases, (b) investigate how price affects demand in the context of informational goods, and (c) show that inspection and higher budgets both lead to higher quality outcomes.

Related papers

• 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)
• Sell Data to AI Algorithms Without Revealing It: Secure Data Valuation and Sharing via Homomorphic Encryption [10.12846924939717]
  We introduce the Trustworthy Influence Protocol (TIP), a privacy-preserving framework that enables buyers to quantify the utility of external data without decrypting the raw assets.<n>By integrating Homomorphic Encryption with gradient-based influence functions, our approach allows for the precise, blinded scoring of data points against a buyer's specific AI model.<n> Empirical simulations in healthcare and generative AI domains validate the framework's economic potential.
  arXiv  Detail & Related papers  (2025-12-04T16:35:09Z)
• Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets [74.91125572848439]
  We study two-sided agentic marketplaces where Assistant agents represent consumers and Service agents represent competing businesses.<n>This environment enables us to study key market dynamics: the utility agents achieve, behavioral biases, vulnerability to manipulation, and how search mechanisms shape market outcomes.<n>Our experiments show that frontier models can approach optimal welfare-- but only under ideal search conditions. Performance degrades sharply with scale, and all models exhibit severe first-proposal bias, creating 10-30x advantages for response speed over quality.
  arXiv  Detail & Related papers  (2025-10-27T18:35:59Z)
• Information Retrieval Induced Safety Degradation in AI Agents [52.15553901577888]
  This study investigates how expanding retrieval access affects model reliability, bias propagation, and harmful content generation.<n>Retrieval-enabled agents built on aligned LLMs often behave more unsafely than uncensored models without retrieval.<n>These findings underscore the need for robust mitigation strategies to ensure fairness and reliability in retrieval-enabled and increasingly autonomous AI systems.
  arXiv  Detail & Related papers  (2025-05-20T11:21:40Z)
• MisinfoEval: Generative AI in the Era of "Alternative Facts" [50.069577397751175]
  We introduce a framework for generating and evaluating large language model (LLM) based misinformation interventions. We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users. Our findings confirm that LLM-based interventions are highly effective at correcting user behavior.
  arXiv  Detail & Related papers  (2024-10-13T18:16:50Z)
• Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives [0.3069335774032178]
  We propose a transaction framework based on the federated learning architecture, and design a seller selection algorithm and incentive compensation mechanism. Specifically, we use gradient similarity and Shapley algorithm to fairly and accurately evaluate the contribution of sellers. After the training, fair compensation is made according to the seller's participation in the training.
  arXiv  Detail & Related papers  (2024-10-10T03:50:20Z)
• Verification of Machine Unlearning is Fragile [48.71651033308842]
  We introduce two novel adversarial unlearning processes capable of circumventing both types of verification strategies. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
  arXiv  Detail & Related papers  (2024-08-01T21:37:10Z)
• 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)
• ChatShop: Interactive Information Seeking with Language Agents [16.879814917881895]
  desire and ability to seek new information strategically are fundamental to human learning. We analyze a popular web shopping task designed to test language agents' ability to perform strategic exploration. We show that the proposed task can effectively evaluate the agent's ability to explore and gradually accumulate information.
  arXiv  Detail & Related papers  (2024-04-15T16:35:41Z)
• Federated Learning Incentive Mechanism under Buyers' Auction Market [2.316580879469592]
  Auction-based Federated Learning (AFL) enables open collaboration among self-interested data consumers and data owners. We adapt the procurement auction framework, aiming to explain the pricing behavior under buyers' market. In order to select clients with high reliability and data quality, and to prevent from external attacks, we utilize a blockchain-based reputation mechanism.
  arXiv  Detail & Related papers  (2023-09-10T16:09:02Z)
• Equilibrium of Data Markets with Externality [5.383900608313559]
  We model real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers. A key component here is the negative externality buyers induce on one another due to data purchases. We prove that platforms intervening through a transaction cost can lead to a pure equilibrium with strong welfare guarantees.
  arXiv  Detail & Related papers  (2023-02-16T00:57:49Z)
• Augmented cross-selling through explainable AI -- a case from energy retailing [0.0]
  We analyze data on 220,185 customers of an energy retailer, predict cross-purchases with up to 86% correctness (AUC), and show that the XAI method SHAP provides explanations that hold for actual buyers. We further outline implications for research in information systems, XAI, and relationship marketing.
  arXiv  Detail & Related papers  (2022-08-24T09:51:52Z)
• OSOUM Framework for Trading Data Research [79.0383470835073]
  We supply, to the best of our knowledge, the first open source simulation platform, Open SOUrce Market Simulator (OSOUM) to analyze trading markets and specifically data markets. We describe and implement a specific data market model, consisting of two types of agents: sellers who own various datasets available for acquisition, and buyers searching for relevant and beneficial datasets for purchase. Although commercial frameworks, intended for handling data markets, already exist, we provide a free and extensive end-to-end research tool for simulating possible behavior for both buyers and sellers participating in (data) markets.
  arXiv  Detail & Related papers  (2021-02-18T09:20:26Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)

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.