  As algorithms are increasingly applied to screen applicants for high-stakes
decisions in employment, lending, and other domains, concerns have been raised
about the effects of algorithmic monoculture, in which many decision-makers all
rely on the same algorithm. This concern invokes analogies to agriculture,
where a monocultural system runs the risk of severe harm from unexpected
shocks. Here we show that the dangers of algorithmic monoculture run much
deeper, in that monocultural convergence on a single algorithm by a group of
decision-making agents, even when the algorithm is more accurate for any one
agent in isolation, can reduce the overall quality of the decisions being made
by the full collection of agents. Unexpected shocks are therefore not needed to
expose the risks of monoculture; it can hurt accuracy even under "normal"
operations, and even for algorithms that are more accurate when used by only a
single decision-maker. Our results rely on minimal assumptions, and involve the
development of a probabilistic framework for analyzing systems that use
multiple noisy estimates of a set of alternatives.
