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This paper generalizes the Kleinberg and Raghavan (2021) model of monoculture in one-sided matching markets with algorithmic advice to quantify the social welfare loss due to decentralized adoption of a common signal. They prove a tight constant bound of 2 on the price of anarchy, demonstrating that the social welfare loss from monoculture is limited. This resolves an open question from the original work.
Monoculture's bite is surprisingly small: even when everyone uses the same algorithmic advice, the resulting social welfare is at worst only half of what's optimally achievable.
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.