2 citations · 4 across the 12 of their papers we have counts for
13 papers
Alignment Makes Language Models Normative, Not Descriptive
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
Post-training alignment optimizes language models to match human preference signals, but this objective is not equivalent to modeling observed human behavior. We compare 120 base-a…
Sequential LLM Release Facilitates Manipulation in Regulated Markets
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
AI agents increasingly mediate bargaining, negotiation and persuasion for people and firms. Such markets extend software-mediated commerce, but add a governance problem: independen…
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…
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.…
Fairness under Competition
Ronen Gradwohl, Eilam Shapira, Moshe Tennenholtz
Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal…
White Hat Search Engine Optimization using Large Language Models
Niv Bardas, Tommy Mordo, Oren Kurland +2
We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
Towards Hybrid Traffic Laws for Mixed Flow of Human-Driven Vehicles and Connected Autonomous Vehicles
Tal Kraicer, Jack Haddad, Erez Karaps +1
Hybrid traffic laws represent an innovative approach to managing mixed environments of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs) by introducing separate…
Search results diversification in competitive search
Tommy Mordo, Itamar Reinman, Moshe Tennenholtz +1
In Web retrieval, there are many cases of competition between authors of Web documents: their incentive is to have their documents highly ranked for queries of interest. As such, t…
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…
STEER: Assessing the Economic Rationality of Large Language Models
Narun Raman, Taylor Lundy, Samuel Amouyal +3
There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it…
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…
Optimal Information Design in Sender-Receiver Cheap Talk Interactions
Itai Arieli, Ivan Geffner, Moshe Tennenholtz
This paper considers the dynamics of cheap talk interactions between an oblivious receiver and a sender with different amounts of information. Even though it may seem that having a…