5 papers
Pro-AI Bias in Large Language Models
Benaya Trabelsi, Jonathan Shaki, Sarit Kraus
Large language models (LLMs) are increasingly employed for decision-support across multiple domains. We investigate whether these models display a systematic preferential bias in f…
Persuading Stable Matching
Jonathan Shaki, Jiarui Gan, Sarit Kraus
In bipartite matching problems, agents on two sides of a graph want to be paired according to their preferences. The stability of a matching depends on these preferences, which in…
Out-of-Context Reasoning in Large Language Models
Jonathan Shaki, Emanuele La Malfa, Michael Wooldridge +1
We study how large language models (LLMs) reason about memorized knowledge through simple binary relations such as equality (), inequality (), and inclusion (). Unli…
Voter Priming Campaigns: Strategies, Equilibria, and Algorithms
Jonathan Shaki, Yonatan Aumann, Sarit Kraus
Issue salience is a major determinant in voters' decisions. Candidates and political parties campaign to shift salience to their advantage - a process termed priming. We study the…
Bayesian Persuasion with Externalities: Exploiting Agent Types
Jonathan Shaki, Jiarui Gan, Sarit Kraus
We study a Bayesian persuasion problem with externalities. In this model, a principal sends signals to inform multiple agents about the state of the world. Simultaneously, due to t…