MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
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Defines coarse representative addition and coarse cell addition on partitioned scales and demonstrates that a rescaled St. Petersburg sequence becomes inert under a suitably chosen countable partition and representative map.
Analysis of 955 Korean decision conversations reveals people favor satisficing and interactional strategies over optimization, with common heuristics aiding exploration flow while rare rule-based ones drive resolution.
citing papers explorer
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A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
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Absorption and Inertness in Coarse-Grained Arithmetic: A Heuristic Application to the St. Petersburg Paradox
Defines coarse representative addition and coarse cell addition on partitioned scales and demonstrates that a rescaled St. Petersburg sequence becomes inert under a suitably chosen countable partition and representative map.
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Analyzing Human Heuristics and Strategies in Everyday Decision-Making Conversations for Conversational AI Design
Analysis of 955 Korean decision conversations reveals people favor satisficing and interactional strategies over optimization, with common heuristics aiding exploration flow while rare rule-based ones drive resolution.