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Subjectivity in Inductive Inference

Author

Listed:
  • Itzhak Gilboa

    (TAU - Tel Aviv University)

  • Larry Samuelson

    (Department of Economics - Yale University [New Haven])

Abstract

This paper examines circumstances under which subjectivity enhances the effectiveness of inductive reasoning. We consider agents facing a data generating process who are characterized by inference rules that may be purely objective (or data-based) or may incorporate subjective considerations. The basic intuition is that agents who invoke no subjective considerations are doomed to "overfit" the data and therefore engage in ineffective learning. The analysis places no computational or memory limitations on the agents|the role for subjectivity emerges in the presence of unlimited reasoning powers.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Itzhak Gilboa & Larry Samuelson, 2012. "Subjectivity in Inductive Inference," Working Papers hal-00756342, HAL.
    • Handle: RePEc:hal:wpaper:hal-00756342
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    Cited by:

    1. Larry Samuelson & Jakub Steiner, 2024. "Robust latent data representations," ECON - Working Papers 460, Department of Economics - University of Zurich, revised Jul 2025.
    2. Gilboa, Itzhak & Samuelson, Larry & Schmeidler, David, 2013. "Dynamics of inductive inference in a unified framework," Journal of Economic Theory, Elsevier, vol. 148(4), pages 1399-1432.
    3. George J. Mailath & Larry Samuelson, 2020. "Learning under Diverse World Views: Model-Based Inference," American Economic Review, American Economic Association, vol. 110(5), pages 1464-1501, May.
    4. Gilboa, Itzhak & Minardi, Stefania & Samuelson, Larry, 2020. "Theories and cases in decisions under uncertainty," Games and Economic Behavior, Elsevier, vol. 123(C), pages 22-40.
    5. Gilboa, Itzhak & Schmeidler, David, 2010. "Simplicity and likelihood: An axiomatic approach," Journal of Economic Theory, Elsevier, vol. 145(5), pages 1757-1775, September.
    6. Gilboa, Itzhak & Samuelson, Larry & Schmeidler, David, 2022. "Learning (to disagree?) in large worlds," Journal of Economic Theory, Elsevier, vol. 199(C).

    More about this item

    Keywords

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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