Algorithmic Coordination & Fair Allocation

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Abstractions that use algorithms to coordinate distributed processes or allocate resources fairly, covering distributed consensus and leader-election methods (Berkeley and bully algorithms, multi-trials technique), fair-division and preference-aggregation criteria (envy-free division, Pareto efficiency, rank aggregation), and optimization heuristics like stochastic programming.

15 abstractions in this family — domain-specific abstractions that sit near one another in structural-signature space (k-means over structural-signature embeddings). Each is shown with its short description.

• Algorithmic bias — A systematic and repeatable tendency of an algorithmic sociotechnical system to produce unfairly differentiated outcomes across people or categories.
• Berkeley algorithm — A distributed clock-synchronization algorithm in which an elected leader estimates participant clocks, rejects outliers and sends relative adjustments toward their average.
• Bully algorithm — A distributed leader-election algorithm in which the highest-identifier live process eventually becomes coordinator after failures.
• Competitive regret — An online-learning performance measure comparing an algorithm's regret with that of a stronger oracle or benchmark that may possess additional information or computational capability.
• Contingent cooperator — An agent willing to contribute to a collective good when enough others are observed or expected to cooperate, but not unconditionally.
• Efficient envy-free division — A resource allocation that is both Pareto efficient and envy-free, so no feasible change benefits someone without harming another and no agent prefers another’s bundle to their own.
• Fractional Pareto efficiency — A discrete allocation is fractionally Pareto-efficient when no feasible allocation, including fractional ones, can make every agent at least as well off and one strictly better off.
• Imperialist competitive algorithm — A population-based metaheuristic that groups candidate solutions into empires whose competition, assimilation and collapse search an optimization landscape.
• Multi-trials technique — A distributed symmetry-breaking technique in which each node tests an increasing batch of randomized choices per communication round to reduce round complexity.
• Optimistic knowledge gradient — A sequential sampling policy for costly crowdsourced labeling that scores an item by an optimistic estimate of how one more label could improve the final classification decision.
• Rank aggregation — The construction of one consensus ordering from multiple rankings over a common set of alternatives.
• Slope One — A family of item-based collaborative-filtering algorithms that predicts a user's rating from average pairwise rating differences between items and the user's ratings of neighboring items.
• Stochastic programming — An optimization framework in which uncertain parameters are represented by probability distributions or scenarios and decisions are chosen across one or more information stages.
• Tournament sort — A comparison-sorting algorithm that organizes elements in a tournament tree, repeatedly outputs the winner and updates only the path affected by its replacement.
• Wiki survey — An open, adaptive survey method in which participants both evaluate statements and contribute new ones while an aggregation system identifies broadly supported or bridging positions.