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A Graph-based Similarity Function for CBDT: Acquiring and Using New Information

Author

Listed:
  • Federico Contiggiani

    (Universidad Nacional de Río Negro)

  • Fernando Delbianco

    (Universidad Nacional del Sur/CONICET)

  • Fernando Tohmé

    (Universidad Nacional del Sur/CONICET)

Abstract

One of the consequences of persistent technological change is that it force individuals to make decisions under extreme uncertainty. This means that traditional decision-making frameworks cannot be applied. To address this issue we introduce a variant of Case-Based Decision Theory, in which the solution to a problem obtains in terms of the distance to previous problems. We formalize this by defining a space based on an orthogonal basis of features of problems. We show how this framework evolves upon the acquisition of new information, namely features or values of them arising in new problems. We discuss how this can be useful to evaluate decisions based on not yet existing data.

Suggested Citation

  • Federico Contiggiani & Fernando Delbianco & Fernando Tohmé, 2022. "A Graph-based Similarity Function for CBDT: Acquiring and Using New Information," Working Papers 146, Red Nacional de Investigadores en Economía (RedNIE).
    • Handle: RePEc:aoz:wpaper:146
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    File URL: https://rednie.eco.unc.edu.ar/files/DT/146.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    : Microeconomic Behavior; Decision-Making under Risk and Uncertainty; Case Based Decision Theory;
    All these keywords.

    JEL classification:

    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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