Price of Anarchy of Algorithmic Monoculture

2026-03-31

AI Summary

This study investigates the phenomenon of “algorithmic monoculture” in matching markets, wherein individuals’ rational adoption of high-accuracy public algorithmic recommendations leads to collectively suboptimal outcomes and social welfare loss. By integrating game theory, mechanism design, and social choice theory, the work generalizes the model of algorithmic advice within a one-sided matching framework and rigorously establishes that, under broad conditions, both the price of anarchy (PoA) and its generalized forms induced by decentralized decision-making are bounded above by 2. This tight constant bound demonstrates that even when agents act fully rationally, convergence on a common algorithm can result in significant efficiency losses, revealing a fundamental tension between individual rationality and collective welfare in algorithm-mediated environments.

Technology Category

Game Theory and Economic Paradigms: Mechanism DesignMultiagent Systems: Mechanism DesignSearch and Optimization: Algorithm Configuration

Application Category

Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environmentsResponsible Web: Human-perceived consequences of algorithmic deployment on the webGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Several recent works investigate the effects of monoculture, the ever increasing phenomenon of (possibly) self-interested actors in a society relying on one common source of advice for decision making, with an archetypal driving example being the growing adoption and predictive power of machine learning models in matching markets, e.g. in hiring. Kleinberg and Raghavan (PNAS, 2021) introduced a model that captures the effects of monoculture in a one-sided matching market with advice, demonstrating that a higher accuracy common signal (such as an algorithmic vendor) might incentivize society as a whole to rationally adopt it, but as a collective it would be better off if each instead adopted less accurate, but private advice. We generalize their model and address the open question of their work in quantifying the social welfare loss. We find that monoculture and more generally decentralized optimization is close to optimal: we show a tight constant bound of 2 on the price of anarchy (and more general notions) for the induced game.

Problem

• algorithmic monoculture
• price of anarchy
• social welfare loss
• matching markets
• decentralized optimization

Innovation

• Price of Anarchy
• Algorithmic Monoculture
• Decentralized Optimization
• Social Welfare Loss
• Matching Markets