teleo-codex/domains/internet-finance/aimd-converges-to-fair-resource-allocation-without-global-coordination-through-local-congestion-signals.md

type	domain	description	confidence	source	created	secondary_domains	supports	reweave_edges
claim	internet-finance	AIMD algorithm achieves provably fair and stable distributed resource allocation using only local congestion feedback	proven	Corless, King, Shorten, Wirth (SIAM 2016) - AIMD Dynamics and Distributed Resource Allocation	2026-03-11	mechanisms collective-intelligence	aimd congestion control generalizes to distributed resource allocation because queue dynamics are structurally identical across networks and compute pipelines aimd scaling solves variable load expensive compute coordination without prediction	aimd congestion control generalizes to distributed resource allocation because queue dynamics are structurally identical across networks and compute pipelines|supports|2026-04-04 aimd scaling solves variable load expensive compute coordination without prediction|supports|2026-04-04

AIMD converges to fair resource allocation without global coordination through local congestion signals

Additive Increase Multiplicative Decrease (AIMD) is a distributed resource allocation algorithm that provably converges to fair and stable resource sharing among competing agents without requiring centralized control or global information. The algorithm operates through two simple rules: when no congestion is detected, increase resource usage additively (rate += α); when congestion is detected, decrease resource usage multiplicatively (rate *= β, where 0 < β < 1).

The SIAM monograph by Corless et al. demonstrates that AIMD is mathematically guaranteed to converge to equal sharing of available capacity regardless of the number of agents or parameter values. Each agent only needs to observe local congestion signals—no knowledge of other agents, total capacity, or system-wide state is required. This makes AIMD the most widely deployed distributed resource allocation mechanism, originally developed for TCP congestion control and now applicable to smart grid energy allocation, distributed computing, and other domains where multiple agents compete for shared resources.

The key insight is that AIMD doesn't require predicting load, modeling arrivals, or solving optimization problems. It reacts to observed system state through simple local rules and is guaranteed to find the fair allocation through the dynamics of the algorithm itself. The multiplicative decrease creates faster convergence than purely additive approaches, while the additive increase ensures fairness rather than proportional allocation.

Evidence

• Corless, King, Shorten, Wirth (2016) provide mathematical proofs of convergence and fairness properties
• AIMD is the foundation of TCP congestion control, the most widely deployed distributed algorithm in existence
• The algorithm works across heterogeneous domains: internet bandwidth, energy grids, computing resources
• Convergence is guaranteed regardless of number of competing agents or their parameter choices

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Relevant Notes:

• coordination mechanisms
• optimal governance requires mixing mechanisms because different decisions have different manipulation risk profiles
• collective intelligence requires diversity as a structural precondition not a moral preference
• designing coordination rules is categorically different from designing coordination outcomes as nine intellectual traditions independently confirm

Topics:

• domains/internet-finance/_map
• core/mechanisms/_map
• foundations/collective-intelligence/_map