10 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…
Coalition Tactics: Bribery and Control in Parliamentary Elections
Hodaya Barr, Eden Hartman, Yonatan Aumann +1
Strategic manipulation of elections is typically studied in the context of promoting individual candidates. In parliamentary elections, however, the focus shifts: voters may care m…
Explaining Decentralized Multi-Agent Reinforcement Learning Policies
Kayla Boggess, Sarit Kraus, Lu Feng
Multi-Agent Reinforcement Learning (MARL) has gained significant interest in recent years, enabling sequential decision-making across multiple agents in various domains. However, m…
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…
Strategic Communication under Threat: Learning Information Trade-offs in Pursuit-Evasion Games
Valerio La Gatta, Dolev Mutzari, Sarit Kraus +1
Adversarial environments require agents to navigate a key strategic trade-off: acquiring information enhances situational awareness, but may simultaneously expose them to threats.…
Bribery for Coalitions in Parliamentary Elections
Hodaya Barr, Yonatan Aumann, Sarit Kraus
We study the computational complexity of bribery in parliamentary voting, in settings where the briber is (also) interested in the success of an entire set of political parties - a…