Regret Bounds for Competitive Resource Allocation with Endogenous Costs

We study online resource allocation among N interacting modules over T rounds. Unlike standard online optimization, costs are endogenous: they depend on the full allocation vector through an interaction matrix W encoding pairwise cooperation and competition. We analyze three paradigms: (I) uniform allocation (cost-ignorant), (II) gated allocation (cost-estimating), and (III) competitive allocation via multiplicative weights update with interaction feedback (cost-revealing). Our main results esta

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ethics.ai (19 March 2026), “Regret Bounds for Competitive Resource Allocation with Endogenous Costs,” evidence record 7000, https://ethics.ai/record/7000 (originally published by arXiv).

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