Computable Fairness: Boltzmann-Softmax Control for AI Resource Allocation
The paper presents a new framework called Computable Fair Division (CFD) for resource allocation in large-scale AI systems. It reinterprets the Boltzmann-Softmax function as a probabilistic resource allocation mechanism, focusing on balancing efficiency and fairness. The proposed method, AHC++, adapts in real-time to maintain fairness targets while minimizing throughput degradation.
- ▪Allocating resources like GPU compute time among multiple agents is a significant challenge in AI systems.
- ▪The proposed AHC++ method updates a control variable in real-time to suppress dominance concentration.
- ▪Simulations indicate that the method can maintain fairness without substantial loss in throughput.
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Physics > Applied Physics arXiv:2605.22827 (physics) [Submitted on 12 Apr 2026] Title:Computable Fairness: Boltzmann-Softmax Control for AI Resource Allocation Authors:Ji-Won Park, Chae Un Kim View a PDF of the paper titled Computable Fairness: Boltzmann-Softmax Control for AI Resource Allocation, by Ji-Won Park and Chae Un Kim View PDF Abstract: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.
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