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.
A UAE-based grocery platform is preparing to launch an AI-powered chatbot designed to help residents find the cheapest deals and prices for groceries. This initiative aims to enhance the shopping experience by leveraging artificial intelligence for price comparison. The platform expects the chatbot to streamline the process of finding value for money on everyday essentials. Why it matters: This development signifies the growing adoption of AI in consumer services and e-commerce within the UAE, potentially impacting household budgeting and competitive pricing strategies.
24 startups from the seventh TAQADAM cohort at KAUST are preparing to pitch their business ideas to investors on March 3, 2024, after surviving the accelerator program. The startups received mentorship in product design, pricing, and legal matters. Example startups include Cognna (cybersecurity), BuildBench (construction platform), Talents Arena (AI recruitment), and Trh Capital (Shariah-compliant finance). Why it matters: This KAUST program highlights growing entrepreneurial activity and venture capital interest in Saudi startups, especially in areas like cybersecurity and AI.
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 Ph.D. student Chiheb Ben Hammouda won the best poster award at the Society for Industrial and Applied Mathematics Conference on Financial Mathematics & Engineering (FM19) for his work on option pricing under the rough Bergomi model. The winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," details research carried out under the supervision of KAUST Professor Raul Tempone. The research group designed new efficient numerical methods for pricing derivatives under the rough Bergomi model by combining smoothing techniques. Why it matters: This award highlights KAUST's growing expertise in financial mathematics and its contribution to solving complex problems in the field using advanced numerical methods.
KAUST's Stochastic Numerics Research Group is developing methods for pricing European options. Their approach, detailed in an upcoming Journal of Computational Finance article, focuses on systematically tuning parameters to achieve accuracy while minimizing computational effort. The goal is to enable automated computation of fair prices for options contracts, similar to how insurance companies determine premiums. Why it matters: This research advances computational finance in the region, potentially improving risk management and investment strategies.
MBZUAI Professor Fakhri Karray and colleagues from the University of Waterloo are using AI to forecast crop yields, focusing on the impact of extreme temperatures on California strawberry yields. The research uses historical climate and agricultural data to predict yields, addressing issues from 2023 when unusual weather caused a $100 million loss to the strawberry industry. Better predictions could benefit consumers, farmers, and the agricultural industry by improving pricing and supply chain management. Why it matters: This research can improve understanding of agricultural system vulnerabilities amid climate change and extreme weather.
Xi Chen from NYU Stern gave a talk at MBZUAI on digital privacy in personalized pricing using differential privacy. The talk also covered research in Web3 and decentralized finance, including delta hedging liquidity positions on Uniswap V3. Chen highlighted open problems in decentralized finance during the presentation. Why it matters: The talk suggests MBZUAI's interest in exploring the intersection of AI, privacy, and blockchain technologies, reflecting growing trends in data protection and decentralized systems.