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Multitasking, Multiarmed Bandits, and the Italian Judiciary

Author

Listed:
  • Robert L. Bray

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

  • Decio Coviello

    (HEC Montréal, Montréal, Québec H3T 2A7, Canada)

  • Andrea Ichino

    (European University Institute, 50014 San Domenico di Fiesole FI, Italy; and University of Bologna, 40126 Bologna, Italy)

  • Nicola Persico

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

Abstract

We model how a judge schedules cases as a multiarmed bandit problem. The model indicates that a first-in-first-out (FIFO) scheduling policy is optimal when the case completion hazard rate function is monotonic. But there are two ways to implement FIFO in this context: at the hearing level or at the case level. Our model indicates that the former policy, prioritizing the oldest hearing, is optimal when the case completion hazard rate function decreases, and the latter policy, prioritizing the oldest case, is optimal when the case completion hazard rate function increases. This result convinced six judges of the Roman Labor Court of Appeals—a court that exhibits increasing hazard rates—to switch from hearing-level FIFO to case-level FIFO. Tracking these judges for eight years, we estimate that our intervention decreased the average case duration by 12% and the probability of a decision being appealed to the Italian supreme court by 3.8%, relative to a 44-judge control sample.

Suggested Citation

  • Robert L. Bray & Decio Coviello & Andrea Ichino & Nicola Persico, 2016. "Multitasking, Multiarmed Bandits, and the Italian Judiciary," Manufacturing & Service Operations Management, INFORMS, vol. 18(4), pages 545-558, October.
  • Handle: RePEc:inm:ormsom:v:18:y:2016:i:4:p:545-558
    DOI: 10.1287/msom.2016.0586
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    References listed on IDEAS

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    3. Tarantino, Emanuele & Simcoe, Timothy S. & Ganglmair, Bernhard, 2018. "Learning When to Quit: An Empirical Model of Experimentation," CEPR Discussion Papers 12733, C.E.P.R. Discussion Papers.
    4. Auerbach, Jan U. & Fonseca, Miguel A., 2020. "Preordered service in contract enforcement," Games and Economic Behavior, Elsevier, vol. 122(C), pages 130-149.
    5. Gneezy, Uri & Nelidov, Vadim & Offerman, Theo & van de Ven, Jeroen, 2023. "When opportunities backfire: Alternatives reduce perseverance and success in task completion," Journal of Economic Behavior & Organization, Elsevier, vol. 208(C), pages 304-324.
    6. Kaufmann, Marc, 2022. "Projection bias in effort choices," Games and Economic Behavior, Elsevier, vol. 135(C), pages 368-393.
    7. Antonio Peyrache & Angelo Zago, 2024. "The inefficiency of courts of justice: industry structure, capacity and misallocation," Journal of Productivity Analysis, Springer, vol. 62(2), pages 217-238, October.
    8. Ruomeng Cui & Hao Ding & Feng Zhu, 2020. "Gender Inequality in Research Productivity During the COVID-19 Pandemic," Papers 2006.10194, arXiv.org, revised Feb 2021.
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    10. Antonio Peyrache & Angelo Zago, 2020. "The (in)efficiency of Justice. An equilibrium analysis of supply policies," CEPA Working Papers Series WP042020, School of Economics, University of Queensland, Australia.
    11. Chemin, Matthieu & Kimalu, Paul & Newman-Bachand, Simon, 2024. "Courts, Crime and Economic Performance: Evidence from a Judicial Reform in Kenya," Journal of Public Economics, Elsevier, vol. 231(C).

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