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Results for "Game Theory"

KAUST Ph.D. students win best paper awards at mean-field games conferences

KAUST ·

KAUST Ph.D. students David Evangelista and Xianjin Yang won best paper awards at international conferences this summer for their work in mean-field game theory. Evangelista's paper focused on solutions for stationary mean-field games with congestion, while Yang's paper developed numerical methods for homogenization problems. The awards were presented at the 18th International Symposium on Dynamic Games and Applications in France and the 12th American Institute of Mathematical Sciences (AIMS) Conference in Taiwan. Why it matters: The recognition highlights KAUST's strength in applied mathematics and computational science, specifically in the emerging field of mean-field games with applications across various domains.

Rational Counterfactuals

arXiv ·

This paper introduces rational counterfactuals, a method for identifying counterfactuals that maximize the attainment of a desired consequent. The approach aims to identify the antecedent that leads to a specific outcome for rational decision-making. The theory is applied to identify variable values that contribute to peace, such as Allies, Contingency, Distance, Major Power, Capability, Democracy, and Economic Interdependency. Why it matters: The research provides a framework for analyzing and promoting conditions conducive to peace using counterfactual reasoning.

DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information

arXiv ·

This paper introduces DaringFed, a novel dynamic Bayesian persuasion pricing mechanism for online federated learning (OFL) that addresses the challenge of two-sided incomplete information (TII) regarding resources. It formulates the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, demonstrating the existence of a unique Bayesian persuasion Nash equilibrium. Evaluations on real and synthetic datasets demonstrate that DaringFed optimizes accuracy and convergence speed and improves the server's utility.

Information Design under Uncertainty

MBZUAI ·

Munther Dahleh from MIT gave a talk on information design under uncertainty, focusing on the challenges of creating an information marketplace. The talk addressed the externality faced by firms when information is allocated to competitors, and considered two models for this externality. The presentation included mechanisms for both models and highlighted the impact of competition on the revenue collected by the seller. Why it matters: The research advances understanding of information markets and mechanism design, relevant to the growing data economy in the GCC region.

Fast Rates for Maximum Entropy Exploration

MBZUAI ·

This paper addresses exploration in reinforcement learning (RL) in unknown environments with sparse rewards, focusing on maximum entropy exploration. It introduces a game-theoretic algorithm for visitation entropy maximization with improved sample complexity of O(H^3S^2A/ε^2). For trajectory entropy, the paper presents an algorithm with O(poly(S, A, H)/ε) complexity, showing the statistical advantage of regularized MDPs for exploration. Why it matters: The research offers new techniques to reduce the sample complexity of RL, potentially enhancing the efficiency of AI agents in complex environments.

Energy Pricing in P2P Energy Systems Using Reinforcement Learning

arXiv ·

This paper presents a reinforcement learning framework for optimizing energy pricing in peer-to-peer (P2P) energy systems. The framework aims to maximize the profit of all components in a microgrid, including consumers, prosumers, the service provider, and a community battery. Experimental results on the Pymgrid dataset demonstrate the approach's effectiveness in price optimization, considering the interests of different components and the impact of community battery capacity.

KAUST hosts International Graduate School on Control event

KAUST ·

KAUST recently hosted the European Embedded Control Institute's International Graduate School on Control (IGSC). As part of the event, KAUST Professor Jeff Shamma gave a one-week course on "Game Theory and Distributed Control". The course had over 30 registered attendees, including participants from KAUST, KACST, King Saud University, and nine European universities. Why it matters: Hosting international events like IGSC enhances KAUST's global reputation, fosters collaboration opportunities, and exposes visiting researchers to KAUST's research environment.