Related papers: Agentic Markets: Equilibrium Effects of Improving Consumer Search

Agentic Markets: Equilibrium Effects of Improving Consumer Search

URL: http://arxiv.org/abs/2603.25893v1
Date: Thu, 26 Mar 2026 20:30:45 GMT
Title: Agentic Markets: Equilibrium Effects of Improving Consumer Search
Abstract summary: We study the impact of improved search technology on learning and welfare in markets.<n>We find cheaper search improves learning and consumer surplus, whereas more informative search can degrade both.
Score: 49.77824898242877
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Motivated by agentic markets -- two-sided markets in which consumers and businesses are assisted by AI tools that facilitate consumers' search -- we study the impact of improved search technology on learning and welfare in markets. We put forth a model where consumers engage in costly search to acquire signals of product fit prior to purchase. The market tracks indications of fit for searched products and indications of quality for chosen products, thereby guiding searches. We characterize the long-run steady-state of the resulting dynamics as well as the impact of improving search technology. We find cheaper search improves learning and consumer surplus, whereas more informative search can degrade both unless the market learns as much as consumers about the products by, for example, ``reading the transcripts'' of agentic conversations. Finally, we consider the impact of search improvements on how businesses set prices. At equilibrium prices in symmetric markets, consumer surplus is improved by cheaper search but may be decreased by more informative search, due to weakened inter-business competition.

Related papers

From Product Search to Preference Articulation: The Economics of Agentic Commerce [0.6226609932118123]
Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents.<n> Preference complexity is the number of satisfaction-relevant dimensions that are difficult to articulate before search but readily evaluated upon inspection.<n>Consumers have finite attention and choose search intensity: products inspected manually or preference-refinement depth with an agent.
arXiv Detail & Related papers (2026-08-09T01:09:24Z)
From Query to Conscience: The Importance of Information Retrieval in Empowering Socially Responsible Consumerism [6.287601579114018]
We argue that the field of Information Retrieval has a critical role to play by empowering consumers to make more informed and more responsible choices.<n>We advocate for the development of new and novel IR systems and interfaces that address the intricacies of socially responsible consumerism.
arXiv Detail & Related papers (2026-04-12T17:50:01Z)
SmartSearch: Process Reward-Guided Query Refinement for Search Agents [63.46067892354375]
Large language model (LLM)-based search agents have proven promising for addressing knowledge-intensive problems.<n>Existing works largely focus on optimizing the reasoning paradigms of search agents, yet the quality of intermediate search queries during reasoning remains overlooked.<n>We introduce SmartSearch, a framework built upon two key mechanisms to mitigate this issue.
arXiv Detail & Related papers (2026-01-08T12:39:05Z)
AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning [61.974530499621274]
Overreliance on search introduces unnecessary cost and risks exposure to noisy or malicious content.<n>We propose a two-stage, outcome-driven RL framework that disentangles problem solving from the decision of whether to invoke search.<n>AdaSearch substantially improves knowledge-boundary awareness, reduces unnecessary search calls, preserves strong task performance, and offers more transparent, interpretable decision behaviors.
arXiv Detail & Related papers (2025-12-18T18:50:01Z)
Progressive Refinement of E-commerce Search Ranking Based on Short-Term Activities of the Buyer [1.8986796884429726]
This study presents a systematic approach to adapting e-commerce search results based on the current context.<n>We start with basic methods and incrementally incorporate more contextual information and state-of-the-art techniques to improve the search outcomes.<n>Our findings demonstrate that this incremental enhancement, from simple autoregressive features to advanced sequence models, significantly improves ranker performance.
arXiv Detail & Related papers (2025-12-15T07:07:32Z)
Agentic AI Framework for Smart Inventory Replenishment [0.6670498055582529]
In contemporary retail, the variety of products makes it difficult to predict the demand, prevent stockouts, and find high-potential products.<n>We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or high-margin products to incorporate.
arXiv Detail & Related papers (2025-11-28T17:14:13Z)
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)
Algorithmic Collusion of Pricing and Advertising on E-commerce Platforms [0.0]
We investigate whether sellers make pricing and advertising decisions together, i.e., two-dimensional decisions.<n>We show that when consumers have high search costs, learning algorithms can coordinate on prices lower than competitive prices.<n>We analyze the platform's strategic response and find that reserve price adjustments will not increase platform profits.
arXiv Detail & Related papers (2025-08-09T18:44:17Z)
A Market for Accuracy: Classification under Competition [13.175123810033124]
We study learning in a market setting, as it affects providers, consumers, and the market itself.<n>We propose a method for classification under competition, so that a learner can maximize market share in the presence of competitors.<n>We show that our approach benefits the providers as well as the consumers, and find that the timing of market entry and model updates can be crucial.
arXiv Detail & Related papers (2025-02-25T10:18:43Z)
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)
Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding [53.02059906193556]
We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
arXiv Detail & Related papers (2020-07-14T06:06:41Z)

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