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Procedures with Incomplete Information

In: Multilevel Strategic Interaction Game Models for Complex Networks

Author

Listed:
  • Duccio Piovani

    (nam.R, Head of Data Science)

  • Jelena Grujić

    (Vrije Universiteit Brussel, Department of Computer Science, Sciences and Bioengineering Sciences Artificial Intelligence Laboratory
    Machine Learning Group, Université Libre de Bruxelles, Computer Science Department, Faculty of Science)

  • Henrik J. Jensen

    (Imperial College London, Department of Mathematics and Centre for Complexity Science)

Abstract

As mentioned at the beginning of the chapter an obvious short coming concerning application to real situations of the forecasting procedure as described so far is that we make use of complete knowledge of the entire space of agents and their interactions. To test the strength of the results against incomplete information a first attempt has been introducing an error in the interaction matrix used for the mean field treatment. This represents the situation in which an observer would have to measure the interactions between agents and does so with an error. This is possibly the biggest problem one would have to overcome when trying to describe real systems. As we will see the forecasting method has proven itself to be quite robust, yielding similar results in both models even in the presence of non negligible errors.

Suggested Citation

  • Duccio Piovani & Jelena Grujić & Henrik J. Jensen, 2019. "Procedures with Incomplete Information," Springer Books, in: Eitan Altman & Konstantin Avrachenkov & Francesco De Pellegrini & Rachid El-Azouzi & Huijuan Wang (ed.), Multilevel Strategic Interaction Game Models for Complex Networks, chapter 0, pages 299-309, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-24455-2_16
    DOI: 10.1007/978-3-030-24455-2_16
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