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MIT professor Gabriele Farina is advancing AI decision-making by combining game theory with machine learning, building on his earlier work with the diplomatic AI Cicero.
Mediator.ai is a tool that applies Nash bargaining game theory and LLMs to facilitate fair cooperative negotiation, generating and scoring candidate agreements against both parties' stated needs until an optimal solution is found.
KWBench introduces a benchmark of 223 professional tasks to evaluate whether LLMs can recognize the underlying game-theoretic structure of a situation without prompting, finding that even the best model succeeds on only 27.9% of tasks. The benchmark targets unprompted problem recognition—a step prior to task execution—across domains like acquisitions, clinical pharmacy, and fraud analysis.
OpenAI and University of Oxford researchers present LOLA (Learning with Opponent-Learning Awareness), a reinforcement learning method that enables agents to model and account for the learning of other agents, discovering cooperative strategies in multi-agent games like the iterated prisoner's dilemma and coin game.
OpenAI presents LOLA (Learning with Opponent-Learning Awareness), a multi-agent reinforcement learning method where agents shape the anticipated learning of other agents. The approach demonstrates emergence of cooperation in iterated prisoner's dilemma and convergence to Nash equilibrium in game-theoretic settings.