A catalytic neuro-fuzzy approach to model agent-based simulation for ombudsman (Lokpal). (1st January 2014)

• Record Type: Journal Article
• Authors: Srivastava, Praveen Ranjan; Verma, Saurabh; Upadhyay, Shivam
• Is Part Of: International journal of bio-inspired computation. Volume 6:Number 3(2014)
• Page Start: 192
• Page End: 204
• Publication Date: 2014-01-01
• Subjects: metrics -- scenario attributes -- weights -- working conditions -- severity -- centroid formula -- decision -- opinion metric -- attribute metric -- working conditions metric -- fuzzy -- neural network -- ombudsman -- agent-based simulation -- bio-inspired computation; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

To live a better life, every citizen requires a clean, honest, dutiful society wherein people do value the integrity of one another and perform their work judiciously but the prevailing corruption and lack of transparency in governance, private or public organisations, educational institutions and related offices which are setup to help common people to avail their rightful services has aroused dissatisfaction among the citizens of the country. People are facing problems when they approach corrupted offices and are forced to bribe the officials to get their work done. To pacify this situation which is eating up the nation slowly and steadily people are willing to set up a Lokpal, an autonomous agency that will not only heed to the appeal by the citizens when they face or observe corruption in local/regional/national government/public bodies but also check for it and take necessary actions. In context with this necessity, this paper proposes an agent-based simulation tool using neural network and fuzzy to understand under what conditions a simple model of the Lokpal agency will be effective. Certain metrics, various scenarios, working conditions and different environment under the organisation is working has been simulated to evaluate their respective effectiveness.