3 citations · 6 across the 12 of their papers we have counts for
12 papers
Learning Dynamics of Strategic Publishers in Generative AI Ecosystems
Sagie Dekel, Omer Madmon, Moshe Tennenholtz +1
Generative AI (GenAI) search systems are transforming how users access information. Unlike ranking-based search systems, where users observe a ranked list of documents, GenAI searc…
Stability in Competitive Search with Results Diversification
Itamar Reinman, Omer Madmon, Moshe Tennenholtz +1
In a competitive search setting, publishers strategically modify their documents in response to induced rankings so as to improve their future ranking. We present a novel game-theo…
Cooperation Under Network-Constrained Communication
Tommy Mordo, Omer Madmon, Moshe Tennenholtz
In this paper, we study cooperation in distributed games under network-constrained communication. Building on the framework of Monderer and Tennenholtz (1999), we derive a sufficie…
RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback
Tommy Mordo, Sagie Dekel, Omer Madmon +2
Competitive search is a setting where document publishers modify them to improve their ranking in response to a query. Recently, publishers have increasingly leveraged LLMs to gene…
Data Sharing with a Generative AI Competitor
Boaz Taitler, Omer Madmon, Moshe Tennenholtz +1
As GenAI platforms grow, their dependence on content from competing providers, combined with access to alternative data sources, creates new challenges for data-sharing decisions.…
GLEE: A Unified Framework and Benchmark for Language-based Economic Environments
Eilam Shapira, Omer Madmon, Itamar Reinman +3
Large Language Models (LLMs) show significant potential in economic and strategic interactions, where communication via natural language is often prevalent. This raises key questio…
On the Convergence of No-Regret Dynamics in Information Retrieval Games with Proportional Ranking Functions
Omer Madmon, Idan Pipano, Itamar Reinman +1
Publishers who publish their content on the web act strategically, in a behavior that can be modeled within the online learning framework. Regret, a central concept in machine lear…
Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games
Eilam Shapira, Omer Madmon, Roi Reichart +1
Human choice prediction in economic contexts is crucial for applications in marketing, finance, public policy, and more. This task, however, is often constrained by the difficultie…
Robust Price Discrimination
Itai Arieli, Yakov Babichenko, Omer Madmon +1
We consider a model of third-degree price discrimination where the seller's product valuation is unknown to the market designer, who aims to maximize buyer surplus by revealing buy…
The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents
Omer Madmon, Idan Pipano, Itamar Reinman +1
We study a game-theoretic information retrieval model in which strategic publishers aim to maximize their chances of being ranked first by the search engine while maintaining the i…
Reputation-based Persuasion Platforms
Itai Arieli, Omer Madmon, Moshe Tennenholtz
In this paper, we introduce a two-stage Bayesian persuasion model in which a third-party platform controls the information available to the sender about users' preferences. We aim…
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation
Eilam Shapira, Omer Madmon, Reut Apel +2
Recent advances in Large Language Models (LLMs) have spurred interest in designing LLM-based agents for tasks that involve interaction with human and artificial agents. This paper…