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Area of Science:

  • Decision Science
  • Computer Science
  • Algorithmic Fairness

Background:

  • Algorithms are increasingly used in high-stakes decisions like employment and lending.
  • Concerns exist about algorithmic monoculture, where multiple agents use the same algorithm.
  • This mirrors agricultural monoculture's vulnerability to shocks.

Purpose of the Study:

  • To investigate the risks of algorithmic monoculture beyond unexpected shocks.
  • To demonstrate how reliance on a single algorithm can degrade collective decision quality.
  • To analyze systems with multiple noisy estimates of alternatives.

Main Methods:

  • Development of a probabilistic framework.
  • Analysis of decision-making systems with multiple agents using a single algorithm.
  • Evaluation of algorithm accuracy in isolation versus collective use.

Main Results:

  • Algorithmic monoculture can reduce overall decision quality, even if the algorithm is individually accurate.
  • The negative impact on accuracy occurs during normal operations, not just due to external shocks.
  • Convergence on a single algorithm by multiple agents can be detrimental.

Conclusions:

  • The risks of algorithmic monoculture are inherent and not solely dependent on external disruptions.
  • Even accurate algorithms can lead to suboptimal collective outcomes when used monolithically.
  • A probabilistic framework is essential for understanding these complex multi-agent algorithmic systems.