Computable Fairness: Boltzmann-Softmax Control for AI Resource Allocation
In large-scale AI systems, allocating scarce resources such as GPU compute time and bandwidth among multiple agents is a critical challenge. Conventional policies focus on efficiency metrics, potentially leading to dominance concentration that undermines system diversity and stability. We propose Computable Fair Division (CFD), a framework that reinterprets the Boltzmann-Softmax function not as a selection tool but as a probabilistic resource allocation mechanism, redefining the inverse temperature parameter $β$ as a computable control variable governing the efficiency-fairness balance. Static analysis reveals a Pareto frontier with a near-optimal Stability Corridor where total loss remai...
| Authors | Ji-Won Park, Chae Un Kim |
|---|---|
| Primary category | physics.app-ph |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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