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Optimal Dynamic Resource Allocation to Prevent Defaults

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  • Urtzi Ayesta

    (LAAS-SARA - Équipe Services et Architectures pour Réseaux Avancés - LAAS - Laboratoire d'analyse et d'architecture des systèmes - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INSA Toulouse - Institut National des Sciences Appliquées - Toulouse - INSA - Institut National des Sciences Appliquées - UT - Université de Toulouse - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse)

  • M Erausquin
  • E Ferreira
  • P Jacko

    (Lancaster University)

Abstract

We consider a resource allocation problem, where a rational agent has to decide how to share a limited amount of resources among different companies that might be facing financial difficulties. The objective is to minimize the total long term cost incurred by the economy due to default events. Using the framework of multi-armed restless bandits and, assuming a two-state evolution of the default risk, the optimal dynamic resource sharing policy is determined. This policy assigns an index value to each company, which orders its priority to be funded. We obtain an analytical expression for this index, which generalizes the return-on-investment (ROI) index under the static setting, and we analyse the influence of the future events on the optimal dynamic policy. A discussion about the structure of the optimal dynamic policy is provided, as well as some extensions of the model.

Suggested Citation

  • Urtzi Ayesta & M Erausquin & E Ferreira & P Jacko, 2016. "Optimal Dynamic Resource Allocation to Prevent Defaults," Post-Print hal-01300681, HAL.
    • Handle: RePEc:hal:journl:hal-01300681
    Note: View the original document on HAL open archive server: https://hal.science/hal-01300681v1
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    References listed on IDEAS

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    Cited by:

    1. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.

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    More about this item

    Keywords

    Multi-Armed Bandit Problem; Default Risk Management; Dynamic Resource Allocation Policies; Markov Decision Processes;
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