Assessing Organizational Efficiency Under Macroeconomic Uncertainty in Decision Support Systems
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www.mruni.eu Sergei Kornilov DOCTORAL DISSERTATION ASSESSING ORGANIZATIONAL EFFICIENCY UNDER MACROECONOMIC UNCERTAINTY IN DECISION SUPPORT SYSTEMS: ENSEMBLE METHODS IN MACHINE LEARNING WITH TWO-STAGE NONPARAMETRIC EFFICIENCY MODELS SOCIAL SCIENCES, ECONOMICS (S 004) VILNIUS, 2020 MYKOLAS ROMERIS UNIVERSITY Sergei Kornilov ASSESSING ORGANIZATIONAL EFFICIENCY UNDER MACROECONOMIC UNCERTAINTY IN DECISION SUPPORT SYSTEMS: ENSEMBLE METHODS IN MACHINE LEARNING WITH TWO-STAGE NONPARAMETRIC EFFICIENCY MODELS Doctoral Dissertation Social Sciences, Economics (S 004) Vilnius, 2020 This dissertation was prepared during the period 2011-2017 at Tallinn University of Technology (Estonia) and during the period 2019-2020 at Mykolas Romeris University under the doctoral pro- gram right conferred to Vytautas Magnus University, ISM University of Management and Economics, Mykolas Romeris University and Šiauliai University on 22 February 2019 by the Order No. V-160 of the Minister of Education, Science and Sport of the Republic of Lithuania. Dissertation is defended on a non-resident basis. Scientific consultant: Prof. Dr. Asta Vasiliauskaitė (Mykolas Romeris University, Social Sciences, Economics S 004). Scientific supervisor at Tallinn University of Technology in 2011-2017: Prof. Dr. Tatjana Põlajeva (Tallinn University of Technology, Estonia, Social Sciences, Economics S 004). The doctoral dissertation is defended at the Council of Economics of Vytautas Magnus University, ISM University of Management and Economics, Mykolas Romeris University and Šiauliai University: Chairperson: Prof. Dr. Violeta Pukelienė (Vytautas Magnus University, Social Sciences, Economics S 004) Members: Prof. Dr. Natalja Lace (Riga Technical University, Latvia, Social Sciences, Economics S 004); Prof. Habil. Dr. Žaneta Simanavičienė (Mykolas Romeris University, Social Sciences, Economics S 004); Assoc. Prof. Dr. Sigita Urbonienė (Vytautas Magnus University, Natural Sciences, Mathematics N 001); Assoc. Prof. Dr. Inga Žilinskienė (Mykolas Romeris University, Technological Sciences, Informat- ics Engineering T 007). The doctoral dissertation will be defended at a public meeting of the Council of Economics on 30 June 2020 at 12:00 at Mykolas Romeris University, auditorium I-414. Address: Ateities str. 20, 08303 Vilnius, Lithuania. © Mykolas Romeris University, 2020 MYKOLO ROMERIO UNIVERSITETAS Sergei Kornilov SISTEMŲ, PADEDANČIŲ PRIIMTI SPRENDIMUS, ORGANIZACINIO EFEKTYVUMO VERTINIMAS ESANT MAKROEKONOMINIAM NEAPIBRĖŽTUMUI: APIBENDRINTI MOKYMOSI METODŲ ALGORITMAI TAIKANT DVIEJŲ PAKOPŲ NEPARAMETRINIUS EFEKTYVUMO MODELIUS Mokslo daktaro disertacija Socialiniai mokslai, ekonomika (S 004) Vilnius, 2020 Mokslo daktaro disertacija rengta 2011-2017 metais Talino technologijos universitete (Estijos Re- spublika) ir 2019-2020 metais Mykolo Romerio universitete pagal Vytauto Didžiojo universitetui su ISM Vadybos ir ekonomikos universitetu, Mykolo Romerio universitetu, Šiaulių universitetu Lietuvos Respublikos švietimo, mokslo ir sporto ministro 2019 m. vasario 22 d. įsakymu Nr. V-160 suteiktą doktorantūros teisę. Disertacija ginama eksternu. Mokslinė konsultantė: Prof. dr. Asta Vasiliauskaitė (Mykolo Romerio universitetas, socialiniai mokslai, ekonomika S 004). Mokslinė vadovė Talino technologijos universitete 2011-2017 metais: Prof. dr. Tatjana Põlajeva (Talino technologijos universitetas, Estijos Respublika, socialiniai mok- slai, ekonomika S 004). Mokslo daktaro disertacija ginama Vytauto Didžiojo universiteto, ISM Vadybos ir ekonomikos universiteto, Mykolo Romerio universiteto, Šiaulių universiteto Ekonomikos mokslo krypties tary- boje: Pirmininkė: prof. dr. Violeta Pukelienė (Vytauto Didžiojo universitetas, socialiniai mokslai, ekonomika S 004). Nariai: prof. dr. Natalja Lace (Rygos technikos universitetas, Latvijos Respublika, socialiniai mokslai, eko- nomika S 004); prof. habil. dr. Žaneta Simanavičienė (Mykolo Romerio universitetas, socialiniai mokslai, eko- nomika S 004); doc. dr. Sigita Urbonienė (Vytauto Didžiojo universitetas, gamtos mokslai, matematika N 001); doc. dr. Inga Žilinskienė (Mykolo Romerio universitetas, technologijos mokslai, informatikos inžinerija T 007). Mokslo daktaro disertacija bus ginama viešame Ekonomikos mokslo krypties tarybos posėdyje 2020 m. birželio 30 d. 12 val. Mykolo Romerio universiteto I-414 aud. Adresas: Ateities g. 20, 08303 Vilnius. © Mykolo Romerio universitetas, 2020 To my son Artemi. TABLE OF CONTENTS LIST OF TABLES 9 LIST OF FIGURES 10 KEY TERMS 11 ABBREVIATIONS 13 INTRODUCTION 15 I. MAIN CHARACTERISTICS OF UNCERTAINTY, EFFICIENCY ASSESSMENT IN DECISION SUPPORT SYSTEMS AND METHODOLOGICAL APPROACHES 27 1.1. The decision-making context in heterogeneous economic processes 27 1.1.1. Bounded rationality under macroeconomic complexity 27 1.1.2. Recursive decision-making model 30 1.1.3. Information accessibility phenomena for decision support systems 32 1.1.4. The problematic of ergodicity and stability of stochastic processes 35 1.2. Methodological context of assessment organizational efficiency 38 1.2.1. Factors of economic complexity 38 1.2.2. Context of heterogeneous economic agents 40 1.3. Foundations of uncertainty in economics theories 42 1.3.1. Uncertainty phenomena in macroeconomic studies 42 1.3.2. Uncertainty in error minimization models 47 1.3.3. Structural risk minimization 51 1.3.4. Uncertainty in expert meta-analysis 53 1.4. Classification of performance, effectiveness and efficiency 56 1.5. Ensemble machine learning approach in decision-making process 67 1.6. Integration of methods into decision support systems 72 1.7. Results and generalization of the literature analysis 76 II. PROPOSED MODEL FOR THE ASSESSING EFFICIENCY IN DECISION SUPPORT SYSTEMS UNDER UNCERTAINTY 81 2.1. Model definition for the assessing efficiency under uncertainty factors 81 2.2. Taxonomy of datasets selection for decision support system 84 2.3. Data mining techniques 89 2.4. Two-stage DEA nonparametric efficiency analysis 92 2.5. Ensemble methods in machine learning 101 2.5.1. Support vector machines 101 2.5.2. Artificial neural networks 105 2.5.3. Random forest 106 2.6. Research limitations 108 III. TESTING THE PROPOSED MODEL FOR THE ASSESSEMENT EFFICIENCY IN DECISION SUPPORT SYSTEMS UNDER UNCERTAINTY 111 3.1. Practical implementation steps of decision support systems 111 3.2. Datasets acquisition and analysis for decision support systems 114 3.2.1. Data model for decision support systems 114 3.2.2. Datasets structure analysis 120 3.2.3. Preliminary results of data model analysis 122 3.3. Efficiency assessment by nonparametric model 125 3.3.1. Nonparametric model selection 125 3.3.2. Decomposition of factors influencing efficiency 131 3.4. Feature set selection and efficiency influencing factors 135 3.4.1. Sensitivity analysis of the factors influencing efficiency 135 3.4.2. Results of analysis of features selection 137 3.5. Nonparametric efficiency models with ensemble machine learning 141 3.5.1. Results of ensemble machine learning techniques 141 3.5.2. Discussion of models for the decision support systems 146 3.6. Empirical Research Results and Discussion 149 CONCLUSIONS AND RECOMMENDATIONS 151 REFERENCES 155 APPENDICES 177 SANTRAUKA 219 MOKSLINIŲ PUBLIKACIJŲ SĄRAŠAS 242 SUMMARY 245 LIST OF TABLES Table 1. Various measurement approaches of efficiency, performance and effectiveness by source of information and area of implication 57 Table 2. Significance analysis of DEA researchers according to their g-index, h-index 60 Table 3. Scientific articles using DEA method published in the Baltic Sea Region 61 Table 4. Methodological review of machine learning techniques employed for efficiency assessment in economic studies 63 Table 5. Components overview of machine learning methods and techniques for elaboration decision support systems model 70 Table 6. Organization’s resources orientated efficiency parameters 94 Table 7. Market performance orientated efficiency parameters 95 Table 8. SVM Kernel functions 104 Table 9. Sequences of practical assessment using proposed methodological approaches 112 Table 10. Data model’s logical principle structured in data tables 116 Table 11. Country specific variables composition with data sources and abbreviation 118 Table 12. Country specific data sources 119 Table 13. Datasets for World Uncertainty Index and Economic Policy Uncertainty 120 Table 14. Results of estimating the datasets on random effects, cross-sectional dependences, serial correlation, stationary and heteroscedasticity 121 Table 15. Baltic stock market uncertainty and volatility (log) 124 Table 16. Selection and validation of nonparametric models (A1, A2, A3, A4, A5, A6) 128 Table 17. Validation of efficiency models (A1, A2, A3, A4, A5, A6) 129 Table 18. Mean and Standard deviation in inputs 130 Table 19. Feature selection of efficiency and uncertainty variables using ordinary least squared, fixed effects, random effects and pooling models 136 Table 20. Feature set selection by factors for decision making process 137 Table 21. Influential power for externalities selection in ensemble machine learning 138 Table 22. Firm-level feature set definition for ensemble machine learning 139 Table 23. Factors of market performance output orientated decision making process 140 Table 24. Categorization of performance and efficiency 141 Table 25. Fitting machine learning algorithms 144 Table 26. Analysis of the confusion matrix 146 Table 27. Results of SVM kernels 147 9 LIST OF FIGURES Figure 1. Logical Structure of the research 22 Figure 2. Multi-disciplinary