A FUZZY MODEL TO ASSESS E-GOVERNMENT REVENUES

Transcription

1 (REFEREED RESEARCH) A FUZZY MODEL TO ASSESS E-GOVERNMENT REVENUES R. Y. Shikhlinskaya 1,*, N. J. Hajiyev 2, S. M. Salimov 3 1 Baku State University, Institute of Applied Mathematics, Azerbaijan, Baku 2 UNDP CO in Azerbaijan 3 Institute of Economics, Azerbaijan National Academy of Sciences, Azerbaijan, Baku Received: Accepted: Abstract: Several factors concerning the generation of e-government revenues usually remain beyond the sight. Those factors could be described by means of fuzzy parameters. By means of fuzzy theory s notions and methods a fuzzy model is elaborated for assessment of e-government revenues in the present article. Impact of every parameter on final results has been carefully studied. Results generated by this model are compared with real prices. Keywords: e-government, economic-mathematical modeling, fuzzy logic, fuzzy logic inference method 1. INTRODUCTION Information is becoming the most significant strategic resource, and is a characteristic feature of the world s contemporary social and economic development [1, 2]. Increasing role of information and ICTs (information and communication technologies) in information society formation has been resulted in a condition where they became one of the driving forces for improving public administration [3]. In addition, information society requires an electronic government (E-government) system i.e., public administration authorities that officially apply ICT resources [5, 6]. E-government is a concept of new administration method concerning the implementation and development of public administration, its transformation and improvement for the purpose of more close relations with citizens as well as for efficiency increasing of the state bodies activities and responsibilities [12]. In other * Corresponding Author: reyhan_sh@rambler.ru 1

2 words, the core purpose of e-government is to make public administration more accessible for of citizens and organizations in order to work out state policy that will facilitate their interaction with authorities and consider their interests. Building of information society in Azerbaijan is considered as one of the priority issues within the national development, thus electronic development strategy is being successfully implemented across the country. Even in global economic crisis time, Azerbaijan kept on steadily augmenting its economic and technological potential. At the moment, active works are underway for application of e-government in Azerbaijan. Today, one of the most forward-looking directions of researches within analysis, forecasting and modelling of economic events and processes is fuzzy logic. Fuzzy number models, often introduced as software for PCs, allow both managers of different levels and company owners to take economically reasonable decisions [11]. In spite of the fact that a reference to new mathematical modelling method has been appeared about half a century ago, this research sphere still remains insufficiently studied in our country so far. To date, the consumers of research works, based on fuzzy number instrument, are quite a narrow circle of state institutions and a little wider circle of commercial companies. Scholars that create and deliver this product to the market are estimated at one or two dozens of people. Conditionally, the period from origin of this knowledge up to these days could be divided into three phases. first phase (1965 beginning of 70s) formation of primary theoretical postulates; second phase (1973 beginning of 90s) practical designs in different spheres of life based on fuzzy logic; birth of a new scientific direction within the framework of fuzzy logic - `Fuzzy Economics`; third phase (1995 up to date ) product s mass use phase underlying fuzzy logic. Nonetheless, the mentioned division is conditional, since theoretical researches in this field haven t completed yet and are extending the application sphere of this mathematical instrument every year. 2

3 The year of 1965 could be considered as the birth year of this scientific direction, when professor of information sciences of the Californian University in Berkeley Lotfi A. Zadeh, has introduced the idea of fuzzy set into science, giving the name to new theory - fuzzy logic [8], [13]. The scholar has formed a concept expressing fuzzy notions of the attractiveness in a numerical form. As opposed to standard logic, when we were accustomed to two binary condition (1/0, yes/no, truth/lie etc.), the fuzzy logic allows to define interim values between standard assessments. Fuzzy sets (as opposed to standard assessment attractive or unattractive, to the right or to the left, yes or no ) could be applied to the assessments as more attractive, less attractive, yes, rather than not, probably yes, a little to the right, sharply to the left. By means of relevant mathematical instrument it became possible to express the above-listed assessments mathematically and subsequently process them with a PC. Thus, it became possible to maximally approximate the mechanism of computer processing and analyzing of data to human thinking. 2. PROBLEM FORMULATION Purpose of the work: development of fuzzy logic model for evaluation of e- government benefits in the Republic of Azerbaijan as well as for the calculation of profits applying fuzzy logic theory s concepts and methods. Generation of e-government profits is subject of impact of several criteria: volume of investments in ICT and post sector, Internet usage rate for population, capacity of Internet channels, price of Internet usage unit, share of ICT and post sector in GDP, number of fixed-line subscribers, number of mobile communication users etc. We set the factors that impact the e-government profits as non-changing variables and determine their range of values: - X1 volume of investments in ICT and post sector - [0; 600] (mln AZN) - X2 - number of Internet users per 100 persons - [0; 100] (persons) - X3 - price of 1 Mbps internet - [0; 1000] (AZN) - X4 - share of ICT and post sector in GDP - [0; 10] (percent) 3

4 - X5 number of fixed-line subscribers per 100 persons - X6 number of mobile communication users per 100 persons All of these factors are fixed constants, which may be described verbally. [10] It is clear that X1, X2, X3, X4, X5, X6, affect the output or ICT and postal sector profits. Marking the output as Y and setting its values in the range of: - Y - profits of ICT and post sector - [0; 2160] (mln AZN) The following diagram shows the input and output variables: Figure 1. Calculation of output values if input figures are given (415; 65; 20; 3,5; 18,6; 100) Thus, the structure of model applied for e-government revenues generation can be asserted as follows: 4

5 Figure 2. Structure of model for e-government revenues generation. Term sets for all fuzzy variables are defined as follows: XI: { lowest, low, medium, high, highest }; I=1, 2,,6. These term sets fluctuate in the medium range where relevant interval values are defined. Figure 3. Curves of term sets in X1 input variable. According to expert s knowledge, each rule forming fuzzy database is structured as shown below. Rule: If the amount of ICT investment is low, the number of Internet users for 100 persons is low, the price of 1 mbit/sec of Internet is medium, and finally, ICT and post sector share is low, then ICT sector revenues will be low. 5

6 Using indicators assigned for variables shown above we can easily deduct the following calculations: Rule : If X1= low, and X2= low, and X3=medium, and X4= low, then Y= low. By means of expert evaluation, we can set ten rules for database that form ICT sector revenues: 1. If x1 is the lower and x2 is the lower and x3 is the higher and x4 is lower then y is lower 2. If x1 is the lower and x2 is the lower and x3 is medium and x4 is the lower then y is low 3. If x1 is low and x2 is low and x3 is the higher and x4 is low then y is low 4. If x1 is low and x2 is low and x3 is low and x4 is low then y is medium 5. If x1 is medium and x2 is medium and x3 is medium and x4 is medium then y is medium 6. If x1 is medium and x2 is medium and x3 is the lower and x4 is medium then y is high 7. If x1 is high and x2 is high and x3 is the lower and x4 is lhigh then y is high 8. If x1 is high and x2 is high and x3 is high and x4 is high then y is medium 9. If x1 is lower and x2 is lower and x2 is lower and x2 is lower then y is lower 10. If x1 is the higher and x2 is the higher and x3 is the higher and x4 is the higher then y is the higher 6

7 Figure 4. Chart demonstrating the dependence of Y output variable from X1, X2 input variables. Figure 5. Chart showing dependence of Y output variable from X3, X4 input variables. ICT sector revenues directly demonstrate the efficiency of e-government. This model shows the dependence of this indicator from the following factors: - Amount of ICT investments; - Number of Internet users per 100 persons; - Price of 1 Mbps Internet; - Share of ICT and post services in GDP; 7

8 - number of fixed-line subscribers per 100 persons; - number of mobile communication users per 100 persons. The following chart shows values of X1, X2, X3 and X4 variables stated above as well as ICT sector revenues (Y) for in accordance with statistical data. The last column displays results that we have generated by means of fuzzy model. X1 X2 X3 X4 X5 X6 Y MODEL [0; 600] [0; 100] [0; 1000] [0; 10] [0; 100] [0; 110] [0; 2160] , ,8 14,5 52, , ,6 15, , , ,5 18, Table 1. Prices of variables and model results for As it s seen, results generated by the model for ICT sector revenues are very close to real prices. In some cases they are even coinciding. By changing each input parameters, we can observe the way that they impact on ICT sector revenues. For instance, if the amount of ICT investment in 2007 was 500 million USD, then this sector s revenues could be as high as 916 million USD. 8

9 Figure 6. Calculation of concrete output variable in case of input values are as follows: in CONCLUSION These types of fuzzy models make possible to forecast ICT sector revenues for coming years on the basis of evaluation of different factors. Impact of different indicators on revenues can be studied in this case. If we increase the number of indicators to some extent and make them more accurate, then we will get more exact panorama. These methods can bring good results in evaluating e-government efficiency: Forecast e-government efficiency, Draw index charts for Azerbaijan. ACKNOWLEDGMENT The work is supported by the joint grant of ANAS and SOCAR 26, REFERENCES [1] Гебриаль В.Н. Информационно-коммуникационные технологии в социальном управлении. Автореферат дисс. на соиск.уч.ст.к.соци н., М с. [2] Голобуцкий А.П., Шевчук О.Б. Электронное правительство. - Киев: UMC-Атлант, с. 9

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