Learn2Match is a POMG-based MARL benchmark for two-sided matching with temporally extended feedback; independent PPO yields higher social welfare and lower regret than CA-ETC but higher information-friction loss.
U., Ghim, C
3 Pith papers cite this work. Polarity classification is still indexing.
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Computable Fair Division uses Boltzmann-Softmax for probabilistic AI resource allocation with beta as control variable and AHC++ for dynamic dominance tracking, showing stability in simulations.
Solipsistic superintelligence developed via unilateral optimization is unlikely to cooperate due to endogenous non-stationarity creating an unclosable train-test-deploy gap.
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Computable Fairness: Boltzmann-Softmax Control for AI Resource Allocation
Computable Fair Division uses Boltzmann-Softmax for probabilistic AI resource allocation with beta as control variable and AHC++ for dynamic dominance tracking, showing stability in simulations.