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Technical note: systematic bias in stochastic learning

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  • Thomas Boucher
  • Yuchen Li

Abstract

The learning curve is a fundamental model used by engineers in cost estimating. In industry, it is typical to use the deterministic model for projecting cost, which is also suggested in standard textbooks. However, the parameters for the model are obtained from actual data, which usually come from a stochastic process. In this technical note, we investigate a particular phenomenon of the stochastic learning model that indicates that a bias may exist in the parameter estimates simply due to random behaviour in learning. The findings suggest that, on average, projections of cost from a model whose parameters are estimated from early data points are, on average, optimistic about the future cost reduction.

Suggested Citation

  • Thomas Boucher & Yuchen Li, 2016. "Technical note: systematic bias in stochastic learning," International Journal of Production Research, Taylor & Francis Journals, vol. 54(11), pages 3452-3463, June.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:11:p:3452-3463
    DOI: 10.1080/00207543.2015.1117674
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    References listed on IDEAS

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    1. Vigil, Dimas P. & Sarper, Huseyin, 1994. "Estimating the effects of parameter variability on learning curve model predictions," International Journal of Production Economics, Elsevier, vol. 34(2), pages 187-200, March.
    2. Neij, Lena, 1997. "Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology," Energy Policy, Elsevier, vol. 25(13), pages 1099-1107, November.
    3. Paul B. Kantor & Willard I. Zangwill, 1991. "Theoretical Foundation for a Learning Rate Budget," Management Science, INFORMS, vol. 37(3), pages 315-330, March.
    4. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
    5. repec:aen:journl:ej35-1-01 is not listed on IDEAS
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    1. Ranasinghe, Thilini & Senanayake, Chanaka D. & Grosse, Eric H., 2024. "Effects of stochastic and heterogeneous worker learning on the performance of a two-workstation production system," International Journal of Production Economics, Elsevier, vol. 267(C).

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