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Balance of Power and the Relative Military Capacity - Empirical Analysis and Implication to North East Asia - (세력균형(power balance)에서의 군사력 수준과 동북아시아에 주는 함의)

  • Kim, Myung-soo
    • Strategy21
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    • s.38
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    • pp.112-162
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    • 2015
  • This study began to confirm or review the balance of power theory by applying scientific methods through experiential cases. Though there are several kinds of national power, this study supposes military power as a crucial power when it comes to war and peace. This research covered balance and imbalance through comparing relative military power between nations or nations' group. Comparison of relative military power can be achieved by statistically processing the values of which has been converted into the standard variables in same domain, then calculating the values of nation's power which has been synthesized different experiential factors. In addition, the criteria of experiential experiment is highly dedicated to European countries, USA, Japan prior to 1st and 2nd World War, as well as USA, Soviet Union and North East Asia during Cold War era. In addition, the balance of power theory has been redefined to review the action of the state upon the changes of power as mentioned in the theory. To begin with, the redefined theory states that relative level of military power between nations defines the consistency of peace and balance of power. If military power is enough to be on the range of level required to keep the power in equilibrium, peace and balance can be achieved. The opposite would unbalance the military power, causing conflicts. While the relative military level between nations change, nations seek to establish 'nations group' via military cooperation such as alliance, which also shift relative military power between nations group as well. Thus, in order to achieve balance of power, a nation seeks to strengthen its military power(self-help), while pursuing military cooperation(or alliance). This changes relative military power between nations group also. In other words, if there exists balance of power between nations, there is balance of power between nations group as well. In this theory, WWI and II broke out due to the imbalance of military force between nations and nations group, and reviewed that due to the balance of military force during the Cold War, peace was maintained. WWI was resulted from imbalance of military cooperation between two powerful states group and WWII was occurred because of the imbalance among the states. Peace was maintained from cooperation of military power and balance among the states during the Cold War. Imbalance among continental states is more threatening than maritime states and balance of power made by army force and naval force also is feasible. Also the outcomes of two variables are found military power balanced ratio of military power for balance is 67% when variable ratio of balance is 100% and standard value for balance is 0.86. Military power exists in a form of range. The range is what unstabilized the international system causing nations to supplement their military powers. These results made possible the calculation and comparison between state's military power. How balance of power inflicted war and peace has been studied through scientific reviews. Military conflict is highly possible upon already unbalanced military powers of North East Asian countries, if the US draws its power back to America. China and Japan are constantly building up their military force. On the other hand, Korean military force is inferior so in accordance to change of international situation state's survival could be threatened and it is difficult to achieve drastic increase in military force like Germany did. Especially constructing naval force demands lots of time; however but has benefit that naval force can overcome imbalance between continental states and maritime states.

The Prediction of DEA based Efficiency Rating for Venture Business Using Multi-class SVM (다분류 SVM을 이용한 DEA기반 벤처기업 효율성등급 예측모형)

  • Park, Ji-Young;Hong, Tae-Ho
    • Asia pacific journal of information systems
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    • v.19 no.2
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    • pp.139-155
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    • 2009
  • For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

Analyses of the Studies on Cancer-Related Quality of Life Published in Korea (암 환자 삶의 질에 대한 국내 연구논문 분석)

  • Lee Eun-Hyun;Park Hee Boong;Kim Myung Wook;Kang Sunghee;Lee Hye-Jin;Lee Won-Hee;Chun Mison
    • Radiation Oncology Journal
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    • v.20 no.4
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    • pp.359-366
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    • 2002
  • Purpose : The purpose of the present study was to analyze and evaluate prior studies published in Korea on the cancer-related quality of life, in order to make recommendations for further research. Materials and Methods : A total of 31 studies were selected from three different databases. The selected studies were analyzed according to 11 criteria, such as site of cancer, domain, independent variable, research design, self/proxy rating, single/battery instrument, translation/back translation, reliability, validity, scoring, and findings. Results : Of the 31 studies, approximately half of them were conducted using a mixed cancer group of patients. Many of the studies asserted that the concept of quality of life had a multidimensional attribute. Approximately 30% were longitudinal design studies giving information about the changes in quality of life. In all studies, except one, patients directly rated their level of quality of life. With respect to the questionnaires used for measuring the quality of life, most studies did not consider whether or not their reliability and validity had been established. In addition, when using questionnaires developed in other languages, no studies employed a translation/ back-translation technique. All studies used sum or total scoring methods when calculating the level of quality of life. The types of variables tested for their influence on qualify of life were quite limited. Conclusion : It is recommended that longitudinal design studies be peformed, using methods of data collection whose validity and reliability has been confirmed, and that studies be conducted to identify new variables having an influence on the quality of life.

Study on the Effect of Self-Disclosure Factor on Exposure Behavior of Social Network Service (자기노출 요인이 소셜 네트워크 서비스의 노출행동에 미치는 영향에 관한 연구)

  • Do Soon Kwon;Seong Jun Kim;Jung Eun Kim;Hye In Jeong;Ki Seok Lee
    • Information Systems Review
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    • v.18 no.3
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    • pp.209-233
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    • 2016
  • Internet companies that utilize social network have increased in number. The introduction of diverse social media services facilitated innovative changes in e-business. Social network service (SNS), which is a domain of social media, is a web-based service designed to strengthen human relations in the Internet and build new social relations. The remarkable growth of social network services and the profit generation and perception of this service are the new growth engines of this digital age. Given this development, many global IT companies views SNS as the most powerful form of social media. Thus, they invest efforts to develop business models using SNS.2) This study verifies the impact of privacy exposure in SNS as a result of privacy invasion. This study examines the purpose of using the SNS and user's awareness of the significance of personal information, which are key factors that affect self-disclosure of personal information. This study utilizes theory of reasoned action (TRA) to provide a theoretical platform that describes the specific behavior and emotional response of individuals. This study presents a research model that considers negative attitude (negatude). In this model, self-disclosure in SNS is considered a TRA. TRA is a subjective norm, a behavioral intention, and a key variable of exposure behavior. A survey was conducted on college students at Y university in Seoul to empirically verify the research model. The students have experiences in using SNS. A total of 198 samples were collected. Path analysis was applied to analyze the relations of factors. The results of path analysis show the statistically insignificant impact of privacy invasion on negatude, subjective norm, behavioral intention, and exposure behavior. The impact of unrecognized privacy invasion was also considered insignificant. The impacts of intention to use SNS on negatude, subjective norm, behavioral intention, and exposure behavior was significant. A significant impact was also found for the significance of personal information on subjective norm, behavioral intention, and exposure behavior, whereas the impact on negatude was insignificant. The impact of subjective norm on behavioral intention was significant. Lastly, the impact of behavioral intention on exposure behavior was insignificant. These findings are significant because the study examined the process of self-disclosure by integrating psychological and social factors based on theoretical discussion.

Optimization of Multiclass Support Vector Machine using Genetic Algorithm: Application to the Prediction of Corporate Credit Rating (유전자 알고리즘을 이용한 다분류 SVM의 최적화: 기업신용등급 예측에의 응용)

  • Ahn, Hyunchul
    • Information Systems Review
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    • v.16 no.3
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    • pp.161-177
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    • 2014
  • Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.