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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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The Garbage Can Model: A Study in (Non)Reproducible Research.

Stewart A Levin1

  • 1Stanford University, Stanford, CA.

Nonlinear Dynamics, Psychology, and Life Sciences
|September 13, 2021
PubMed
Summary

Reproducing computational research is challenging due to evolving technology. While exact results from the Garbage Can Model were not replicated, modern analysis largely supports its qualitative findings.

Area of Science:

  • Organizational Studies
  • Computational Social Science
  • Decision Making Theory

Background:

  • The seminal 'Garbage Can Model of Organizational Choice' (Cohen, March & Olsen, 1972) pioneered applying decision-making models to non-industrial settings like universities.
  • The original paper was an early instance of reproducible computational research, including a Fortran 66 program for verification.

Purpose of the Study:

  • To assess the reproducibility of the original 'Garbage Can Model' computational research.
  • To investigate the impact of changing computing platforms on replicating historical computational findings.
  • To evaluate the model's conclusions in light of modern computational capabilities.

Main Methods:

  • Attempted exact reproduction of the original Fortran 66 program's results.

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  • Utilized modern computing environments and advanced statistical analysis.
  • Compared reproduced results with original qualitative observations and conclusions.

    • Main Results:

    • Exact numerical reproduction of the original results proved impossible due to hypersensitivity and technological evolution.
    • Modern computational analysis largely corroborated the qualitative insights and conclusions of the 1972 paper.
    • Discrepancies highlight the challenges in long-term computational research reproducibility.

    Conclusions:

    • While exact replication is difficult, the core insights of the Garbage Can Model remain valid.
    • The study underscores the need for careful reevaluation of computational models over time.
    • Future research should re-examine studies that critiqued the model's code based on perceived discrepancies.