Fuzzy Inference System Predictor of Good Governance against Administrative Corruption

Today, the term ' corruption ' can be found in many news headlines. Increasing of corruption in organizations and other institutions can be recognized only with reference to the press and a variety of mass media. Recently announced the corruption perception index, Iran was among the countries with high corruption. This study follows an extensive review of previous research literature to create a comprehensive model for measuring administrative corruption and the definitions of fuzzy sets of factors affecting administrative corruption has been used here, including predictive variables of good governance in the world bank; the right to comment and accountability; political stability and absence of violence; government effectiveness; regulatory quality and rule of law. In this study, all the different dimensions of good governance against corruption were considered as input of fuzzy inference system and administrative corruption was used as an output. After that, membership functions and fuzzy rules, a fuzzy inference system measuring administrative corruption was designed using good governance indices. At the end, the outgoing model was compared with the Experts' opinion. In this study, the rule writing was created, using the comment of five university professors. The results indicated that the results of the Experts' opinion and those of fuzzy inference sustem were close, which indicates the validity of the system. It can be concluded that one of the main reason for this validation, is the identification of appropriate metrics to predict. In this study these metrics were obtained from the World Bank. The results revealed that the index of the rule of law in governance has a significant influence on the model output which shows the reduction of administrative corruption and by its increasing and decreasing, the level of anticipated corruption in the organization increases and decreases.