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.

翻译：近期多项研究探讨了单一文化（即社会中可能存在自利行为的主体日益依赖单一共同建议来源进行决策的现象）的影响，其典型示例是机器学习模型在匹配市场（如招聘）中日益普及且预测能力提升。Kleinberg与Raghavan（PNAS, 2021）提出了一个模型，用于刻画带有建议的单边匹配市场中单一文化的影响，表明高精度的共同信号（如算法供应商）可能激励整个社会理性地采用它，但集体而言，若各方各自采用精度较低但私有的建议，反而会更优。我们推广了他们的模型，并解决了其工作中关于量化社会福利损失这一开放性问题。我们发现单一文化及更广义的分散优化接近最优：对于由此引发的博弈，我们给出了无政府代价（及更广义概念）的紧常数界2。