Archived academic material · Original source · Resource library

This archived description records a research concept or project as presented in the source. Proposed outcomes and implementation status have not been independently verified.

Capability-Aware Imbalance in Composite Decision Systems: An Optimal Transport View

Authors: Hadi Sadoghi Yazdi
Legacy status: The source labels this work “Under review (JMLR 2026)”. Acceptance and current review status have not been independently verified.

Overview

This work reframes structural imbalance in composite decision systems (Mixture-of-Experts, Boosting, hierarchical classifiers, distributed systems) as a natural and often optimal outcome rather than a flaw.

Using the lens of Optimal Transport, we prove that when task difficulty is heterogeneous, the cost-minimizing assignment is necessarily non-uniform — a phenomenon we call Constructive Imbalance.

Key Contributions

Fundamental Theorem of Constructive Imbalance: Optimal routing is non-uniform whenever task difficulty is non-uniform.
Effective Complexity as the primal transport cost.
Complexity attenuation via rare specialization (highly complex experts add almost zero global cost if rarely used).
Unified framework connecting MoE routing, Boosting reweighting, load balancing, and hierarchical decomposition.
Theoretical link between transport-optimal routing and Rademacher generalization bounds.
Empirical validation + corrections to previous claims (harmonic mean bound, marginal cost equalization).

Repository Contents

run_experiments.py — Reproducible main script
results.json — All raw experimental results

Reproducibility

python run_experiments.py