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The Unreasonable Effectiveness of Algorithms

Author

Listed:
  • Jens Ludwig
  • Sendhil Mullainathan
  • Ashesh Rambachan

Abstract

We calculate the social return on algorithmic interventions (specifically their Marginal Value of Public Funds) across multiple domains of interest to economists—regulation, criminal justice, medicine, and education. Though these algorithms are different, the results are similar and striking. Each one has an MVPF of infinity: not only does it produce large benefits, it provides a “free lunch.” We do not take these numbers to mean these interventions ought to be necessarily scaled, but rather that much more R&D should be devoted to developing and carefully evaluating algorithmic solutions to policy problems.

Suggested Citation

  • Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024. "The Unreasonable Effectiveness of Algorithms," NBER Working Papers 32125, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:32125
    Note: CH ED EH LE LS PE
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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • H40 - Public Economics - - Publicly Provided Goods - - - General
    • I00 - Health, Education, and Welfare - - General - - - General
    • K00 - Law and Economics - - General - - - General (including Data Sources and Description)

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