When everyone follows the same algorithm’s advice, the collective outcome degrades. This is the algorithmic monoculture problem: a single navigation app routes every driver down the same “fastest” road, creating the congestion it predicted wouldn’t exist. A single recommendation engine points every job applicant to the same “best” openings, flooding those positions while leaving equally good ones undiscovered.

Kleinberg, Sinanaj, and Tardos quantify the damage in matching markets: the price of anarchy — the ratio between the worst equilibrium and the social optimum — is exactly 2. Everyone following identical algorithmic advice produces outcomes at most twice as bad as perfect coordination. The bound is tight: there exist markets where the factor of 2 is achieved, and no market does worse.

This is surprisingly modest. The fear around algorithmic monoculture suggests catastrophic coordination failures — herding, cascading mistakes, systemic fragility. A factor of 2 means the system loses at most half its potential welfare. Bad, but not catastrophic. The monoculture doesn’t destroy the market. It halves its efficiency.

The structural insight: monoculture’s cost comes from correlation, not from the quality of any individual recommendation. The algorithm might give excellent advice to each person in isolation. The problem is that identical excellent advice, followed by everyone simultaneously, creates conflicts that uncorrelated mediocre advice wouldn’t. The pathology is in the uniformity, not the accuracy. Diversity of advisors — even worse advisors — can outperform a single superior one because diversity breaks the correlation that causes congestion.