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Optimizing the Envy Cycle Elimination Algorithm

NeurIPS 2026 · Learning theory and algorithms › Submodular and game algorithms · approach: Fair division of indivisible goods · 0 citations (OpenAlex)

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Studies heuristics for choosing the good and unenvied agent in envy cycle elimination (ECE) for EF1 allocations, proving strong utilitarian and egalitarian price bounds for each heuristic and running average-case experiments.

Problem

Vanilla envy cycle elimination yields EF1 allocations, but the choice of good and receiving agent in each round can lead to very different welfare; the worst-case welfare loss of arbitrary ECE allocations (strong price) is large.

Approach

Define greedy ECE heuristics that choose the agent, the good, or both (maximizing utility) and a max-min matching heuristic for the first n rounds, then analyze their strong utilitarian and egalitarian prices against the vanilla ECE baseline.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: Spliddit instances

Benchmarks: Spliddit instances

Metrics: strong utilitarian price strong egalitarian price average utilitarian price average egalitarian price

Baselines: vanilla ECE algorithm round-robin algorithm

Builds on: envy cycle elimination round-robin algorithm price of fairness

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