Defines resilience evaluation D^ρ π as the L1-limit of scaled dynamic risk measure applied to process increments, and derives its dual representation as worst-case conditional expectation of an effective drift when ρ arises from BSDEs with Lipschitz or quadratic drivers.
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CEO-Bench evaluates AI agents on managing a startup over 500 days, showing that even top models like Claude Opus 4.8 and GPT-5.5 barely maintain starting capital and fail to turn consistent profits.
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.
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.
Preisach hysteresis framing of latent worker utilities, estimated by margin-trained dual NN and XGBoost on price encodings, yields 0.799 AUC and supports simultaneous 21.3% wage-bill reduction plus 9.7 pp fill-rate gain on 36k gig transactions.
A hybrid DRL system for multi-pair crypto trading with deterministic risk shielding outperforms a heuristic baseline at 10% significance on Binance futures data.
Goal pursuit theory is presented as an illustrative behavioral framework that models multiple goals in travel decisions across activity scheduling, vehicle ownership, and location choice applications.
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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.