Contribution to the Measurement of Organizational Performance based on a Multi-Agent Approach

Authors

  • Ansar Daghouri Laboratory : Signals, Distributed Systems and Artificial Intelligence (SSDIA) ENSET of Mohammedia, University Hassan II of Casablanca
  • Khalifa Mansouri Laboratory : Signals, Distributed Systems and Artificial Intelligence (SSDIA) ENSET of Mohammedia, Université Hassan II of Casablanca
  • Mohammed Qbadou Laboratory : Signals, Distributed Systems and Artificial Intelligence (SSDIA) ENSET of Mohammedia, Université Hassan II of Casablanca

DOI:

https://doi.org/10.14738/tmlai.54.2980

Keywords:

Systemic Thinking, Decision Support Systems, Organizational Performance, Artificial Intelligence.

Abstract

This research focuses on evaluating and analyzing the organizational performance of a risk management unit within banks. The main proposal is to analyze and simulate the process of risk management based on decision support system and artificial intelligence. This is why this paper uses the systemic thinking and simulation tool. We finally propose a multi-agent model showing nine autonomous agents communicating with each other to simulate a risk. This model provides both a tool to simulate the risk and a way to modify the organizational structure of the risk management unit to improve the performance of bank.

References

(1) P. A. Chiappori et M. O. Yanelle, «Le risque bancaire : un aperçu théorique,» Revue d'économie financière , pp. 97-111, 1996.

(2) O. De Bandt, P. Hartmann et J. L. Peydró, Systemic Risk in Banking, The Oxford Handbook of Banking, 2012.

(3) A. Mouhtadi, «Risque de liquidité : cas du Maroc,» International Journal of Innovation and Applied Studies, vol. 10 No, pp. 325-335, 2015.

(4) M. A. Malina et F. H. Selto, «Choice and change of measures in performance measurement models,» Management Accounting Research, vol. 15, pp. 441-469, 2004.

(5) O. Villarmois, «Le concept de performance et sa mesure : un état de l’art, Les Cahiers de la Recherche, Centre Lillois d’Analyse et de Recherche sur l’Evolution des,» 2001.

(6) M. Karsky, La dynamique des systèmes complexes ou la systémique de l'ingénieur, 2004.

(7) S. Sheard, «Complex Systems Science and its Effects on Systems Engineering,» chez European Systems Engineering Conference, 2006.

(8) F. Le Gallou, «Systémique: Théorie et applications,» chez Editions Tec Et Doc, 1993.

(9) G. Donnadieu and M. Karsky, La systémique, penser et agir dans la complexité, Paris: Editions de liaisons, 2002.

(10) J. M. Flaus, E. Berthelier et F. Giannoccaro, «Modélisation de systemes organisationnels pour l’analyse des défaillances : application au plan de sauvegarde communal,» chez Conférence Internationale de Modélisation et Simulation, Tunisie, 2010.

(11) G.-M. Karagiannis, thèse: Méthodologie pour l’analyse de robustesse des plans de secours industriels, 2010.

(12) J. Flaus , E. Berthelier et F. Giannoccaro, «Modélisation de systemes organisationnels pour l’analyse des défaillances : application au plan de

sauvegarde communal,» 2010.

(13) J. Lin, L. Chaoyu Lin et S. Huang, «Improving System Performance by Extending Inheritance Analysis and Design,» 2016.

(14) M. Bienvenu, «Instructor for online course ''Introduction to Artificial Intelligence'' Institution: Université de Provence, Marseille, France,» 2006-2008.

(15) F. Bellifemine, A. Poggi and G. Rimassa, «JADE – A FIPAcompliant agent framework,» 1999.

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Published

2017-09-01

How to Cite

Daghouri, A., Mansouri, K., & Qbadou, M. (2017). Contribution to the Measurement of Organizational Performance based on a Multi-Agent Approach. Transactions on Engineering and Computing Sciences, 5(4). https://doi.org/10.14738/tmlai.54.2980

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems