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    MgO-MgF2-Y2O3-YF3 4์›๊ณ„ ์‹œ์Šคํ…œ์˜ ์ƒํƒœ๋„ ์‹คํ—˜๊ณผ ์—ด์—ญํ•™ ๋ชจ๋ธ๋ง

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2022. 8. ์ •์ธํ˜ธ .The establishment of a reliable thermodynamic database to understand the thermodynamic properties and phase equilibria is crucial in high temperature materials processing including metallurgy, glassmaking, and engineering ceramics fabrications.. As part of a long-term research project to search new ceramic coating materials suitable for plasma environment, the thermodynamic database of MgO-MgF2-Y2O3-YF3 system was developed based on CALculation of PHAse Diagram (CALPHAD) method. Due to lack of phase diagram experiment data in binary Y2O3-YF3, and MgO-MgF2-Y2O3-YF3 reciprocal system, the phase equilibria of the binary and reciprocal systems were investigated using a classical equilibration/quenching experiment and differential thermal analysis (DTA). Equilibrium phases were confirmed by electron probe microanalysis (EPMA) and X-ray diffraction (XRD) phase analysis. For the very first time, the entire range of the phase diagram of yttrium oxy-fluoride system up to 1973 K was experimentally determined. It is found that cubic-Y2O3 phase dissolves more than 5 mol % of YF3 at 1973 K. The melting points of YOF and vernier phases are found to be higher than 1973 K and their steep liquidus in the YF3-rich region are determined. The phase diagram of the MgO-MgF2-Y2O3-YF3 system was investigated from 1273 to 1773 K for the very first time. Eutectic reactions and isothermal liquidus lines were precisely studied and no existence of ternary or quaternary compound was confirmed. Based on new experimental phase diagram data and thermodynamic property data in the literature, the Y2O3-YF3 and MgO-MgF2-Y2O3-YF3 systems were thermodynamically modeled by the CALPHAD method and the accurate thermodynamic database was prepared. As applications of the thermodynamic database, metastable solubilities of YF3 in Y2O3 during plasma etching process were calculated.์—ด์—ญํ•™ ํŠน์„ฑ๊ณผ ์ƒํ‰ํ˜•์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ์—ด์—ญํ•™ ๋ฐ์ดํ„ฐ ๋ฒ ์ด์Šค์˜ ๊ตฌ์ถ•์€ ์•ผ๊ธˆ, ์œ ๋ฆฌ ์ œ์กฐ ๋ฐ ์—”์ง€๋‹ˆ์–ด๋ง, ์„ธ๋ผ๋ฏน ์ œ์กฐ๋ฅผ ํฌํ•จํ•œ ๊ณ ์˜จ ์žฌ๋ฃŒ ๊ฐ€๊ณต์—์„œ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ๋ฐ˜๋„์ฒด ๊ณต์ •์—์„œ ์‚ฌ์šฉ๋˜๋Š” ์„ธ๋ผ๋ฏน ํžˆํ„ฐ๊ฐ€ ํ”Œ๋ผ์ฆˆ๋งˆ ๊ฐ€์Šค์— ๋…ธ์ถœ ๋จ์— ๋”ฐ๋ผ ๋ถ€์‹ ๋ฐ ์ˆ˜์œจ ์ €ํ•˜ ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•˜์˜€๊ณ  ๊ทธ์— ๋”ฐ๋ผ ์ƒˆ๋กœ์šด ์ฝ”ํŒ… ๋ฌผ์งˆ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์˜ ํ•„์š”๊ฐ€ ๋Œ€๋‘๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” CALculation of PHAse Diagram (CALPHAD) ๋ฐฉ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ MgO-MgF2-Y2O3-YF3 ์‹œ์Šคํ…œ์˜ ์—ด์—ญํ•™ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. Y2O3-YF3 ์ด์›๊ณ„ ์‹œ์Šคํ…œ ๋ฐ MgO-MgF2-Y2O3-YF3 reciprocal ์‹œ์Šคํ…œ์˜ ์ƒํƒœ๋„ ๋ฐ ์—ด์—ญํ•™ ์‹คํ—˜ ๋ฐ์ดํ„ฐ๊ฐ€ ๋งค์šฐ ๋ถ€์กฑํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ƒํ‰ํ˜• ์‹คํ—˜ ๋ฐ ์‹œ์ฐจ ์—ด๋ถ„์„ (DTA) ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ํ‰ํ˜•์ƒ์€ electron probe microanalysis (EPMA)์™€ X-ray diffraction (XRD)์„ ํ†ตํ•ด ๋ถ„์„ํ•˜์—ฌ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ฒ˜์Œ์œผ๋กœ ์˜ฅ์‹œ ๋ถˆํ™” ์ดํŠธ๋ฅจ๊ณ„ ์‹œ์Šคํ…œ์˜ ์ „์ฒด ์ƒํƒœ๋„๊ฐ€ ์—ฐ๊ตฌ๋˜์—ˆ๊ณ  ๊ณ ์šฉ์ฒด์ธ Y2O3 ์ƒ์€ 1973 K์—์„œ 5 mol % ์ด์ƒ์˜ YF3 ์ƒ์„ ์šฉํ•ดํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ฐํ˜€์กŒ๋‹ค. YOF ๋ฐ vernier ์ƒ์˜ ์œต์ ์€ 1973 K ์ด์ƒ์œผ๋กœ ๋ฐํ˜€์กŒ๊ณ , ์‹คํ—˜์„ ํ†ตํ•ด YF3 ์ƒ ๋ถ€๊ทผ์˜ ์•ก์ƒ์„ ์˜ ์กฐ์„ฑ์ด ๊ฒฐ์ •๋˜์—ˆ๋‹ค. MgO-MgF2-Y2O3-YF3 ์‹œ์Šคํ…œ์˜ ์ƒํƒœ๋„๋Š” ์ฒ˜์Œ์œผ๋กœ 1273 K์—์„œ 1773 K๊นŒ์ง€ ์—ฐ๊ตฌ๋˜์—ˆ๊ณ  ๊ณต์œต์ ๊ณผ ๋“ฑ์˜จ ์•ก์ƒ์„ ์„ ์ •๋ฐ€ํ•˜๊ฒŒ ๋ถ„์„ํ•˜์˜€๋‹ค. 3์› ํ˜น์€ 4์› ํ™”ํ•ฉ๋ฌผ์€ ํ™•์ธ๋˜์ง€ ์•Š์•˜๋‹ค. ์‹คํ—˜์„ ํ†ตํ•ด ์–ป์€ ์ƒํƒœ๋„ ๋ฐ์ดํ„ฐ์™€ ๋ฌธํ—Œ์˜ ์—ด์—ญํ•™ ์„ฑ์งˆ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ Y2O3-YF3 ๋ฐ MgO-MgF2-Y2O3-YF3 ์‹œ์Šคํ…œ์„ CALPHAD ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์—ด์—ญํ•™ ๋ชจ๋ธ๋งํ•˜์˜€๊ณ  ์ •๋ฐ€ํ•œ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ๊ตฌ์ถ•ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ์‘์šฉ์œผ๋กœ ํ”Œ๋ผ์ฆˆ๋งˆ ์—์นญ ๊ณต์ • ๋™์•ˆ ์˜ฅ์‹œ ๋ถˆํ™” ์ดํŠธ๋ฅจ๊ณ„ ์‹œ์Šคํ…œ์˜ ์ฆ๊ธฐ์••์„ ๊ณ„์‚ฐํ•˜์˜€๊ณ  ์ด๋ฅผ ๋ถˆํ™” ์•Œ๋ฃจ๋ฏธ๋Š„์˜ ์ฆ๊ธฐ์••๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ค€์•ˆ์ • ์ƒํƒœ์—์„œ Y2O3 ๊ณ ์šฉ์ฒด์— YF3๊ฐ€ ์šฉํ•ด๋˜๋Š” ์ •๋„๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๊ณ  ์ด๋ฅผ Y2O3์˜ ๊ฒฐ์ • ๊ตฌ์กฐ๋ฅผ ํ†ตํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค.Abstract i Table of Contents ii List of Figures vi List of Tables viii Chapter 1. Introduction 1 1.1 Research Objective 1 1.2 Organization 1 Chapter 2. Thermodynamic Optimization and the 3 CALculation of PHAse Diagrams (CALPHAD) Methodology 3 2.1 Thermodynamic Optimization 3 2.2 Thermodynamic Models 5 2.2.1 Stoichiometric Compounds 5 2.2.2 Liquid Solution 6 2.2.3 Solid Solution 12 2.2.4 Metallic and Gas Phases 14 Chapter 3. Key Phase Diagram Experiments and the Thermodynamic Optimizations of the Y2O3-YF3 Binary Systems 16 3.1 Introduction 17 3.2 Literature review of the Y2O3-YF3 system 18 3.3 Phase diagram experiments 20 3.4 Thermodynamic models 22 3.4.1 Stoichiometric compounds 22 3.4.2 Liquid solution 23 3.4.3 Solid solutions 24 3.5 Experimental results and thermodynamic optimization 27 3.5.1 Yttria phases (c-Y2O3, and h-Y2O3) and Yttrium oxyfluoride (YOF) 27 3.5.2 Vernier phases and Yttrium oxyfluoride (YF3) 29 3.5.3 Thermodynamic optimization of the Y2O3-YF3 system 32 3.6 Chemical reaction of Y2O3 in plasma etching and cleaning process 39 Chapter. 4 A Coupled Phase Diagram Experiment and Thermodynamic Optimization of the MgO-MgF2-Y2O3-YF3 system 74 4.1 Introduction 75 4.2 Literature Review 76 4.2.1 MgO-MgF2 system 76 4.2.2 MgO-Y2O3 system 77 4.2.3 Y2O3-YF3 system 78 4.2.4 MgF2-YF3 system 78 4.2.5 MgO-MgF2-Y2O3-YF3 system 79 4.3 Phase diagram experiments 79 4.3.1 Starting materials 79 4.3.2 Differential thermal analysis (DTA) 80 4.3.3 Quenching experiments 80 4.3.4 Phase characterization 81 4.4 Thermodynamic models 81 4.4.1 Stoichiometric compounds 81 4.4.2 Liquid solution 82 4.4.3 Solid solutions 85 4.5 Experimental results and thermodynamic optimization 87 4.5.1 Binary systems 87 4.5.2 Mg, Y // O, F reciprocal system 90 Chapter 5. Conclusion 125 5.1 Summary 125 5.2 Original contribution to knowledge 126 5.3 Future suggestions 127 Appendix. Review of 128 rare earth oxyfluoride systems 128 A1. Evaluation of the Gibbs energy of REOF 128 A1.1 NdOF 128 A1.2. LaOF 129 A1.3 CeOF 130 A1.4 Summary 131 A2. Simple estimation of phase diagram of RE2O3-REF3 system 132 References 144 Abstract 147์„

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    ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๊ณต๊ณต์กฐ์ง ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ์ „๋žต์  ํ–‰์œ„๋กœ์„œ ๋ชฉํ‘œ๋‹ฌ์„ฑ๊ณผ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„๋ฅผ ๋ฐํžˆ๊ณ , ์ด์— ๋Œ€ํ•œ ์‹ค์ฆ์ ์ธ ๋ถ„์„์„ ์‹ค์‹œํ•˜๋Š” ๋ฐ ์žˆ๋‹ค. ์‚ฌ์‹ค์ƒ ๊ณต๊ณต์กฐ์ง์˜ ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„๋Š” ๋ณด๋‹ค ๋‚˜์€ ์„œ๋น„์Šค์™€ ์งˆ ์ข‹์€ ํ–‰์ •์„ ์œ„ํ•œ ์‹ ๊ณต๊ณต๊ด€๋ฆฌ์ (New Public Management) ๊ฐœํ˜๋…ผ์˜์™€ ํ•จ๊ป˜ ๋‹ค์–‘ํ•œ ๋ฐฉ์‹์œผ๋กœ ๊ณต๊ณต๋ถ€๋ฌธ์˜ ์„ฑ๊ณผ๊ด€๋ฆฌ์ฒด๊ณ„์— ๋„์ž…๋˜์–ด ์™”๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ ์—ญ์‹œ 2001๋…„๊ณผ 2006๋…„ ใ€Œ์ •๋ถ€์—…๋ฌด๋“ฑ์˜ํ‰๊ฐ€์—๊ด€ํ•œ๊ธฐ๋ณธ๋ฒ•ใ€๊ณผ ใ€Œ์ •๋ถ€์—…๋ฌดํ‰๊ฐ€๊ธฐ๋ณธ๋ฒ•ใ€๋“ฑ์ด ์ œ์ •๋˜๋ฉด์„œ ์ •๋ถ€๋ถ€์ฒ˜์— ๋Œ€ํ•œ ํ†ตํ•ฉ์ ์ธ ์„ฑ๊ณผ๊ด€๋ฆฌ๊ฐ€ ์‹œ๋„๋˜๊ณ  ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, 1983๋…„ ใ€Œ์ •๋ถ€ํˆฌ์ž๊ธฐ๊ด€๊ด€๋ฆฌ๊ธฐ๋ณธ๋ฒ•ใ€์— ์˜ํ•œ ๊ธฐ๊ด€ํ‰๊ฐ€๋ฅผ ์‹œ์ž‘์œผ๋กœ 2007๋…„ ใ€Œ๊ณต๊ณต๊ธฐ๊ด€์˜์šด์˜์—๊ด€ํ•œ๋ฒ•๋ฅ ใ€์— ์˜ํ•œ ๊ณต๊ธฐ์—…๊ณผ ์ค€์ •๋ถ€๊ธฐ๊ด€์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ธ ์„ฑ๊ณผ๊ด€๋ฆฌ์ฒด๊ณ„๊ฐ€ ๋„์ž…๋˜์–ด ์šด์˜๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ณต๊ณต์กฐ์ง์—์„œ์˜ ์„ฑ๊ณผํ‰๊ฐ€๊ฐ€ ์กฐ์ง์˜ ํ˜์‹ ๊ณผ ๊ด€๋ จ๋œ ๋‹ค์–‘ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๊ณ , ์กฐ์ง์šด์˜์„ ์„ฑ๊ณผ์ค‘์‹ฌ์˜ ์‚ฌํ›„์  ํ†ต์ œ๋กœ ๋ณ€ํ™”์‹œํ‚ด์œผ๋กœ์„œ ์ž์œจ์ ์ธ ์„ฑ๊ณผํ–ฅ์ƒ์˜ ๊ธฐํšŒ๋ฅผ ๋ถ€์—ฌํ•  ์ˆ˜ ์žˆ๋Š” ์ œ๋„๋กœ ์ž๋ฆฌ ์žก์„ ๊ฒƒ์ด๋ผ๋Š” ๋‚™๊ด€์ ์ธ ๊ธฐ๋Œ€์™€ ๋‹ฌ๋ฆฌ, ์‹ค์ œ ๊ณต๊ณต๋ถ€๋ฌธ์— ๋„์ž…๋œ ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„๋Š” ๊ณต๊ณต๋ถ€๋ฌธ์ด ๊ฐ–๊ณ  ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์ œ์•ฝ์š”๊ฑด๋“ค๊ณผ ํ•œ๊ณ„๋กœ ์ธํ•ด ์‹คํšจ์„ฑ์ด ๋–จ์–ด์ง„๋‹ค๋Š” ๋น„ํŒ๋„ ๋งŒ๋งŒ์น˜ ์•Š๋‹ค.(Smith, 1995Kim, P. S. & K. P. Hong, 2013) ํŠนํžˆ ๊ณต๊ณต์กฐ์ง์— ๋Œ€ํ•œ ์ง€๋‚œ ์ˆ˜๋…„๊ฐ„์˜ ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„์˜ ํ™œ์šฉ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ทธ ์‹คํšจ์„ฑ์— ๋Œ€ํ•œ ๋ฌธ์ œ์ ์ด ์ง€์†์ ์œผ๋กœ ์ œ๊ธฐ๋˜๊ณ  ์žˆ๋‹ค๋Š” ์ ์€ ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„์— ๋Œ€ํ•œ ๋‘ ๊ฐ€์ง€ ์ธก๋ฉด์—์„œ์˜ ๋น„ํŒ์  ๊ฒ€ํ† ๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ํ•˜๋‚˜๋Š” ํ˜„์žฌ ์šด์˜๋˜๊ณ  ์žˆ๋Š” ๊ณต๊ณต๋ถ€๋ฌธ์˜ ์กฐ์ง๋“ค์— ๋Œ€ํ•œ ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„๊ฐ€ ์ž˜๋ชป ์„ค๊ณ„๋˜์–ด, ํ‰๊ฐ€๋„๊ตฌ๋กœ์„œ์˜ ์‹คํšจ์„ฑ์„ ์žƒ๊ณ  ์žˆ๋Š” ๊ฒƒ์ด ์•„๋‹Œ๊ฐ€ ํ•˜๋Š” ๋ถ€๋ถ„์ด๋‹ค. ํ•˜์ง€๋งŒ ๊ณต๊ณต๊ธฐ๊ด€์— ์ ์šฉ๋œ ์„ฑ๊ณผํ‰๊ฐ€์ฒด๊ณ„์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ํ‰๊ฐ€ยท๊ด€๋ฆฌ์ฒด๊ณ„์˜ ์กฐ์ •์ด ๊ณ„์†์ ์œผ๋กœ ์žˆ์–ด์™”๋‹ค๋Š” ์ ์—์„œ ํ‰๊ฐ€์ œ๋„์˜ ๋ชจ์ˆœ๊ณผ ์˜ค๋ฅ˜๋Š” ๊ณ„์†์ ์œผ๋กœ ์ˆ˜์ •๋˜์–ด์™”๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์ธก๋ฉด์—์„œ๋Š” ์„ฑ๊ณผํ‰๊ฐ€๊ฐ€ ์šด์˜๋˜๋Š” ๊ณผ์ •์—์„œ ํ‰๊ฐ€๋Œ€์ƒ(๊ธฐ๊ด€)๋“ค์ด ํ‰๊ฐ€์ œ๋„์— ์ „๋žต์ ์œผ๋กœ ๋Œ€์‘(strategic response)ํ•จ์œผ๋กœ์„œ ์‹ค์ œ ์„ฑ๊ณผํ‰๊ฐ€๊ณผ์ •์— ๋‚ด์žฌ๋œ ์™œ๊ณก๊ณผ ์˜ค๋ฅ˜๋“ค์ด ๋“ค์–ด๋‚˜์ง€ ์•Š๋Š” ์ฒด ์™ธ์ ์ธ ์ œ๋„ ์ˆ˜์ •์„ ๋ฐ˜๋ณตํ•˜๊ณ  ์žˆ์„ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์ด๋‹ค. ๋”ฐ๋ผ์„œ ํ˜„์žฌ์˜ ์„ฑ๊ณผ๊ด€๋ฆฌ ๋ฐ ์„ฑ๊ณผํ‰๊ฐ€ ์ฒด๊ณ„์˜ ์ œ๋„์  ์ธก๋ฉด๊ณผ ๊ตฌ๋ถ„๋˜๋Š” ํ”ผํ‰๊ฐ€์ž์˜ ํ–‰ํƒœ์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ํ•„์š”ํ•˜๋ฉฐ, ์ด๋ฅผ ์ œ๋„์  ์„ค๊ณ„์— ๋ฐ˜์˜ํ•ด์•ผ ํ•  ํ•„์š”์„ฑ์„ ์ œ๊ธฐ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ดˆ์  ์—ญ์‹œ ํ›„์ž์— ์žˆ๋‹ค. ์‚ฌ์‹ค์ƒ ๊ทธ๋™์•ˆ ๋‹ค์–‘ํ•œ ๋ฌธํ—Œ๊ณผ ์‚ฌ๋ก€๋“ค์„ ํ†ตํ•ด ์„ฑ๊ณผํ‰๊ฐ€๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํ”ผํ‰๊ฐ€์ž๋“ค์˜ ์ „๋žต์  ํ–‰์œ„๋“ค์ด ์ œ์‹œ๋œ๋ฐ” ์žˆ์œผ๋ฉฐ, ์ข๊ฒŒ๋Š” ์„ฑ๊ณผํ‰๊ฐ€ ์ดํ›„ ํ‰๊ฐ€์ง€ํ‘œ์—๋งŒ ๋ชฐ๋‘ํ•˜๋Š” ๊ฒฝํ–ฅ๋ถ€ํ„ฐ ๋„“๊ฒŒ๋Š” ์ฐจ๋…„๋„ ๋ณด์ƒ์„ ์œ„ํ•ด ์˜ฌํ•ด์˜ ์„ฑ๊ณผ๋ฅผ ์ง€์—ฐํ•˜์—ฌ ๋ณด๊ณ ํ•˜๋Š” ํ˜„์ƒ๊นŒ์ง€ ์„ฑ๊ณผํ‰๊ฐ€๊ณผ์ •์—์„œ ๋‹ค์–‘ํ•œ ์™œ๊ณก๊ณผ ์ „๋žต์  ํ–‰์œ„๋“ค์ด ๋ณด๊ณ ๋˜๊ณ  ์žˆ๋‹ค.(Wilson, 2004) ๋˜ํ•œ ๊ตญ๋‚ด์˜ ๋ฌธํ—Œ๋“ค์—์„œ๋„ ์—ญ์‹œ ๊ณต๊ณต๋ถ€๋ฌธ ์ข…์‚ฌ์ž๋“ค์ด ์ธ์ง€ํ•˜๊ณ  ์žˆ๋Š” ์„ฑ๊ณผํ‰๊ฐ€์˜ ์™œ๊ณก๊ณผ ์ „๋žต์  ํ–‰์œ„๊ฐ€ ๋†’์€ ์ˆ˜์ค€์ด๋ผ๋Š” ์‚ฌ์‹ค๋“ค์ด ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค.(๊ณต๋™์„ฑ ์™ธ, 2009๊ธˆ์žฌ๋• ์™ธ, 2009์‹ ๋ฏผ์ฒ , 2010๋‚จ์Šนํ•˜, 2012์œ ์Šนํ˜„, 2013) ๊ทธ๋Ÿฌ๋‚˜ ์ง€๊ธˆ๊นŒ์ง€์˜ ๊ตญ๋‚ด์˜ ์—ฐ๊ตฌ๋“ค์ด ๊ณต๊ณต์กฐ์ง์˜ ์„ฑ๊ณผํ‰๊ฐ€๊ณผ์ •์—์„œ์˜ ์™œ๊ณก๊ณผ ์ „๋žต์  ํ–‰์œ„๊ฐ€ ์กด์žฌํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๋ฌธ์ œ๋ฅผ ์ œ๊ธฐํ•˜๊ณ  ์žˆ๋‹ค๋Š” ์ ์—์„œ๋Š” ๊ธ์ •์ ์ด๊ธฐ๋Š” ํ•˜์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๋“ค์ด ์‚ฌ๋ก€๋ถ„์„์ด๋‚˜ ์„ค๋ฌธ์กฐ์‚ฌ์— ์˜์กดํ•˜๊ณ  ์žˆ์–ด ์ด์— ๋Œ€ํ•œ ์‹ค์ฆ์ ์ด๊ณ  ์ฒด๊ณ„์ ์ธ ์—ฐ๊ตฌ๋ฅผ ์‹œ๋„ํ•œ ๊ฒฝ์šฐ๋Š” ์ฐพ์•„๋ณด๊ธฐ ํž˜๋“ค๋‹ค. ๋ฌผ๋ก  ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋Š” ๊ณต๊ณต์กฐ์ง์˜ ์„ฑ๊ณผํ‰๊ฐ€์˜ ๊ฒฝ์šฐ ์„ฑ๊ณผ์™€ ํ‰๊ฐ€์ •๋ณด์— ๋Œ€ํ•œ ์ž๋ฃŒ๊ฐ€ ํ•œ์ •๋˜์–ด ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์„ฑ๊ณผ์™œ๊ณก๊ณผ ์ „๋žต์  ํ–‰์œ„ ์ž์ฒด๊ฐ€ ๋•Œ๋กœ๋Š” ์กฐ์ง์˜ ๋น„ํšจ์œจ์„ฑ๊ณผ ์„ฑ๊ณผ์ €ํ•˜์˜ ์›์ธ์œผ๋กœ, ๋˜ ๋•Œ๋กœ๋Š” ๊ณต๊ณต๋ถ€๋ฌธ์˜ ์ข…์‚ฌ์ž์˜ ๋น„์œค๋ฆฌ์ ยท๋น„๋„์ ์  ํ–‰์œ„์˜ ํ•˜๋‚˜๋กœ ๊ฐ„์ฃผ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์ด๋Ÿฌํ•œ ํ–‰์œ„๋“ค์„ ์ฐพ์•„๋‚ด๋Š” ๊ฒƒ์ด ์šฉ์ดํ•˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ทธ๋™์•ˆ ๊ณต๊ณต์กฐ์ง์˜ ์„ฑ๊ณผํ‰๊ฐ€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋“ค์ด ๊ทœ๋ฒ”์ ์ธ ์ˆ˜์ค€์—์„œ ๊ฐ•์กฐํ•ด์˜จ ์„ ์ˆœํ™˜์ ์ธ ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„๊ฐ€ ์ •์ฐฉ๋˜๊ธฐ ์œ„ํ•ด์„ , ๋ฌด์—‡๋ณด๋‹ค ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„์˜ ์ง์ ‘์ ์ธ ์ดํ•ด๊ด€๊ณ„์ž์ธ ํ”ผํ‰๊ฐ€์ž๋“ค์˜ ํ–‰ํƒœ(behavior)์™€ ๋ฐ˜์‘(response)์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์ด ์„ ํ–‰๋˜์–ด์•ผ ํ•˜๋ฉฐ, ์ด๋ฅผ ์œ„ํ•œ ์ฒด๊ณ„์ ์ธ ์‹ค์ฆ๋ถ„์„์ด ์ˆ˜๋ฐ˜๋˜์–ด์•ผ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์˜์‹ ํ•˜์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๋ช‡ ๊ฐ€์ง€ ๊ตฌ์ฒด์ ์ธ ๋ชฉ์ ์„ ๊ฐ–๋Š”๋‹ค. ์ฒซ์งธ๋Š” ์„ ํ–‰์—ฐ๊ตฌ๋“ค์„ ํ†ตํ•ด ์ œ์‹œ๋˜์–ด์˜จ ์ „๋žต์  ํ–‰์œ„์˜ ์œ ํ˜•ํ™” ๋ฐ ๋งค์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•œ ์ด๋ก ์  ๋…ผ์˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๊ณต๊ณต์กฐ์ง์—์„œ๋„ ์ด๋Ÿฌํ•œ ์ „๋žต์  ํ–‰์œ„(์‹ค์ œ์ ์œผ๋กœ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„)๊ฐ€ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋Š” ์ง€๋ฅผ ์‹ค์ฆ์ ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ํŠนํžˆ Hood(2007)๋Š” ์„ฑ๊ณผํ‰๊ฐ€๊ณผ์ •์—์„œ์˜ ์ „๋žต์  ํ–‰์œ„๋Š” ์„ฑ๊ณผ์ธก์ •๋ฐฉ์‹์— ๋”ฐ๋ผ ๋‹ฌ๋ฆฌ์งˆ ์ˆ˜ ์žˆ๋‹ค๊ณ  ๋ณด๋ฉด์„œ, ๋ชฉํ‘œ๋‹ฌ์„ฑ๋ฐฉ์‹์˜ ์„ฑ๊ณผ์ธก์ •์‹œ์Šคํ…œ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์ „๋žต์  ํ–‰์œ„๋ฅผ ํ†ฑ๋‹ˆํšจ๊ณผ(ratchet effect), ๋ฌธํ„ฑํšจ๊ณผ(threshold effect), ๊ทธ๋ฆฌ๊ณ  ์‚ฐ์ถœ์™œ๊ณก(output distortion)์œผ๋กœ ๊ตฌ๋ถ„ํ•˜๊ณ  ์žˆ๋‹ค. ์ฃผ๋ชฉํ•  ์ ์€ ์‚ฌ์‹ค์ƒ ์„ธ ๊ฐ€์ง€ ์ „๋žต์  ํ–‰์œ„๊ฐ€ ์‹ค์ฒด์ ์œผ๋กœ ๋“œ๋Ÿฌ๋‚˜๋Š” ๋ฐฉ์‹์—๋Š” ๋‹ค์†Œ ์ฐจ์ด๊ฐ€ ์žˆ์ง€๋งŒ, ์ด๋“ค ๋ชจ๋‘ ๋ชฉํ‘œ์„ค์ •(goal setting)๊ณผ ๋‹ฌ์„ฑ(targeting)์ด๋ผ๋Š” ๊ธฐ์ œ ์†์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํ˜„์ƒ์ด๋ผ๋Š” ์ ์—์„œ ๊ณตํ†ต์ ์„ ๊ฐ–๋Š”๋‹ค. ๋”ฐ๋ผ์„œ ๋ชฉํ‘œ๋ถ€์—ฌ๋ฐฉ์‹์˜ ์„ฑ๊ณผํ‰๊ฐ€์‹œ์Šคํ…œ์—์„œ ํ‰๊ฐ€๋Œ€์ƒ์ž๋“ค์€ ์„ฑ๊ณผ๊ณ„์•ฝ์„ ํ†ตํ•ด ์ฃผ์–ด์ง„ ๋ชฉํ‘œ์˜ ๋‹ฌ์„ฑ์„ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ๋…ธ๋ ฅ๋ฐฐ๋ถ„์„ ์‹œ๋„ํ•  ๊ฒƒ์ด๋ฉฐ, ์ด๋Ÿฌํ•œ ๋…ธ๋ ฅ๋ฐฐ๋ถ„ ์ค‘ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๋Š” ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ์ •๋‹นํ•œ ๋…ธ๋ ฅ๊ณผ๋Š” ์ฐจ๋ณ„ํ™”๋œ ์ „๋žต์  ํ–‰์œ„์˜ ๋˜ ๋‹ค๋ฅธ ์ธก๋ฉด์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ชฉํ‘œ๋ถ€์—ฌ๋ฐฉ์‹์˜ ์„ฑ๊ณผํ‰๊ฐ€์—์„œ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„๋Š” ์„ฑ๊ณผ์กฐ์ •๊ณผ ๊ฐ™์€ ์ „๋žต์  ํ–‰์œ„์˜ ์ค‘์š” ์š”์ธ์œผ๋กœ ๊ฐ„์ฃผ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•˜์—ฌ, ๊ณต๊ณต๋ถ€๋ฌธ์˜ ์„ฑ๊ณผํ‰๊ฐ€๊ณผ์ •์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์ „๋žต์  ํ–‰์œ„๋กœ์„œ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์™€ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์˜ ๊ด€๊ณ„๋ฅผ ์‹ค์ฆ์ ์œผ๋กœ ๋ถ„์„ํ•˜๋Š” ๋ฐ 1์ฐจ์ ์ธ ๋ชฉ์ ์„ ๋‘๊ณ ์ž ํ•œ๋‹ค. ๋‘˜์งธ๋Š” ๋ชฉํ‘œ๋ถ€์—ฌ๋ฐฉ์‹(target system)์˜ ์„ฑ๊ณผํ‰๊ฐ€์‹œ์Šคํ…œ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์ „๋žต์  ํ–‰์œ„๋กœ์„œ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์˜ ๊ด€๊ณ„์— ๊ด€ํ•œ ๋ฏผ๊ฐ„๋ถ€๋ฌธ์˜ ๋…ผ์˜๋ฅผ ๊ณต๊ณต๋ถ€๋ฌธ์— ํ™•๋Œ€ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์ง€๋ฅผ ์ด๋ก ์ ยท์‹ค์ฆ์ ์œผ๋กœ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ์‚ฌ์‹ค์ƒ ๊ทธ๋™์•ˆ ๋ฏผ๊ฐ„๋ถ€๋ฌธ์—์„œ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋Š” ์„ฑ๊ณผํ‰๊ฐ€ ๋ฐฉ์‹์˜ ๊ณต๊ณต๋ถ€๋ฌธ์— ๋Œ€ํ•œ ์ ์šฉ๊ฐ€๋Šฅ์„ฑ์€ ๊ณต๊ณต๋ถ€๋ฌธ์ด ๊ฐ–๊ณ  ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์ œ์•ฝ ์š”๊ฑด๋“ค๊ณผ ๋…ธ์ •๋œ ํ•œ๊ณ„๋“ค๋กœ ์ธํ•ด ๋‹ค์–‘ํ•œ ์˜๊ตฌ์‹ฌ์„ ๋ฐ›์•„์™”์œผ๋‚˜(Smith, 1995Kim, P. S. & K. P. Hong, 2013), ์‹ค์ œ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ๋“ค์— ๋Œ€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ๋“ค์ด ๋Œ€๋ถ€๋ถ„ ๋‹จ์ผ ์‚ฌ๋ก€๋‚˜ ์ง€๊ฐ๋œ ๊ณต๊ณต๋ถ€๋ฌธ ์ข…์‚ฌ์ž์˜ ์ธ์‹์ˆ˜์ค€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์— ๊ทธ์น˜๊ณ  ์žˆ์–ด ๊ณต๊ณต๋ถ€๋ฌธ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์–‘์ž์˜ ๋งค์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ์—ฐ๊ตฌ๋Š” ๊ฑฐ์˜ ์ง„ํ–‰๋˜๊ณ  ์žˆ์ง€ ์•Š๋‹ค. ํŠนํžˆ ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ณต๊ณต์กฐ์ง์˜ ์„ฑ๊ณผํ‰๊ฐ€๋Š” ๋ฏผ๊ฐ„์กฐ์ง์˜ ๋‹ค๊ธฐ๊ฐ„(multi-period) ์„ฑ๊ณผํ‰๊ฐ€์™€ ๋‹ฌ๋ฆฌ (โ…ฐ) ๋ฐ˜๋ณต์  ์„ฑ๊ณผํ‰๊ฐ€์—์„œ ๋Œ€๋ฆฌ์ธ์˜ ํ‰๊ฐ€๊ด€๋ จ ์‹œ๊ณ„(time horizon)๊ฐ€ ์งง๊ณ  (โ…ฑ) ๊ณต๊ณต์กฐ์ง์€ ๋ฏผ๊ฐ„์กฐ์ง์˜ ์ด์œค๊ณผ ๊ฐ™์€ ์ƒ์œ„๋ชฉํ‘œ๊ฐ€ ์—†์–ด ์„ฑ๊ณผํ‰๊ฐ€์ฒด๊ณ„ ๋‚ด์—์„œ์˜ ์„ฑ๊ณผ ๋ชฉํ‘œ๊ฐ€ ๋ชจํ˜ธ(goal ambiguity)ํ•˜๊ณ  ๋‹ค์–‘(goal diversity)ํ•˜๋‹ค๋Š” ์ , ๊ทธ๋ฆฌ๊ณ  (โ…ฒ) ์„ฑ๊ณผํ‰๊ฐ€ ๊ฒฐ๊ณผ์˜ ํ™œ์šฉ๋ชฉ์  ๋‹ค์–‘ํ•˜๊ณ , ์ด๋“ค ๋ชฉ์ ๋“ค์ด ์ˆœ์ฐจ์ ์œผ๋กœ ๊ด€๋ฆฌ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ๊ณต๊ณต์กฐ์ง์—์„œ์˜ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์ „๋žต์  ํ–‰์œ„๋กœ์„œ์˜ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์˜ ๊ด€๊ณ„๋Š” ๋ฏผ๊ฐ„๋ถ€๋ถ„์—์„œ์˜ ์–‘์ž์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์ด๋ก ์  ์˜ˆ์ธก๊ณผ ์ฐจ๋ณ„์ ์ผ ์ˆ˜ ์žˆ์Œ์„ ์ œ์‹œํ•˜์˜€์œผ๋ฉฐ, ์‹ค์ œ ์šฐ๋ฆฌ์‚ฌํšŒ์˜ ๊ณต๊ณต์กฐ์ง์—์„œ ์ƒ์„ฑ๋œ ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ด์— ๋Œ€ํ•œ ๊ฒฝํ—˜์  ์ฆ๊ฑฐ๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ใ€Œ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ใ€์— ํฌํ•จ๋œ ๊ณต๊ธฐ์—…๊ณผ ์ค€์ •๋ถ€๊ธฐ๊ด€(๋‹จ, ์ค‘์†Œํ˜•์€ ์ œ์™ธ) 51๊ฐœ ๊ธฐ์—…์˜ 2,518๊ฐœ ์„ฑ๊ณผ์ง€ํ‘œ๋ฅผ ๋ถ„์„๋Œ€์ƒ์œผ๋กœ ์„ ์ •ํ•˜์˜€์œผ๋ฉฐ, ์—ฐ๊ตฌ์˜ ์‹œ๊ฐ„์  ๋ฒ”์œ„๋Š” 2008๋…„โˆผ2012๋…„์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ ๋Œ€์ƒ ๊ณต๊ธฐ์—…์„ ๋ถ„์„๋Œ€์ƒ์œผ๋กœ ์„ ์ •ํ•œ ์ด์œ ๋Š” ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ๋ชฉํ‘œ๋‹ฌ์„ฑ๊ณผ ์ „๋žต์  ํ–‰์œ„๋กœ์„œ์˜ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด์„  ์กฐ์ง์˜ ์šด์˜๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์„ฑ๊ณผ๋ชฉํ‘œ์™€ ์„ฑ๊ณผ๋‹ฌ์„ฑ์น˜์— ๋Œ€ํ•œ ๋ช…ํ™•ํ•œ ์ž๋ฃŒ๊ฐ€ ํ•„์š”ํ•œ๋ฐ, ๊ณต๊ธฐ์—…๊ณผ ์ค€์ •๋ถ€๊ธฐ๊ด€๋“ค์€ ๊ฒฝ์˜ํ‰๊ฐ€ ๊ณผ์ •์„ ํ†ตํ•ด ์ž์‹ ๋“ค์˜ ์กฐ์ง ์šด์˜์˜ ์„ฑ๊ณผ๋ชฉํ‘œ์™€ ์„ฑ๊ณผ๋‹ฌ์„ฑ์น˜๋ฅผ ๋ช…ํ™•ํ•˜๊ฒŒ ๋ฌธ์„œํ™”ํ•˜์—ฌ ์ œ์‹œํ•˜๋„๋ก ๋˜์–ด์žˆ์–ด ์ด๋ฅผ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€๋Š” ๋ณด๊ณ -๊ฒ€์ฆ-์กฐ์ •์ด๋ผ๋Š” ์ ˆ์ฐจ์  ํŠน์ˆ˜์„ฑ์„ ๊ฐ–๊ณ  ์žˆ์–ด ์„ฑ๊ณผํ‰๊ฐ€๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๋ฅผ ๋ณด๋‹ค ๊ฐ๊ด€ํ™” ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ๋„ ์žˆ๋‹ค. ์ฃผ์š” ๋ถ„์„๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€์—์„œ์˜ ์„ฑ๊ณผ์ง€ํ‘œ๋“ค์˜ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„๋Š” ํ‰๊ท ์ ์œผ๋กœ 100%๋ฅผ ์ƒํšŒํ•˜๋Š” ์ˆ˜์ค€(ํ‰๊ท  105.33%)์—์„œ ๋ถ„ํฌ๋˜์–ด ์žˆ์œผ๋ฉฐ, ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„๋Š” ์—ฐ๋„, ๊ธฐ๊ด€์œ ํ˜•, ์ง€ํ‘œํ‰๊ฐ€๋ฐฉ์‹ ๋“ฑ๊ณผ ๊ฐ™์€ ์ง€ํ‘œ๋“ค์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ์„œ๋Š” ๋ถ„ํฌ์ƒ์˜ ์ฐจ์ด๋Š” ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜์œผ๋‚˜, ๋ชจ๋“  ๊ธฐ๊ด€์ด ๋™์ผํ•˜๊ฒŒ ์ ์šฉ๋ฐ›๋Š” ๊ณตํ†ต์ง€ํ‘œ์™€ ๊ฐ ๊ธฐ๊ด€๋ณ„๋กœ ๋‹ค๋ฅด๊ฒŒ ์ ์šฉ๋ฐ›๋Š” ๊ฐœ๋ณ„์ง€ํ‘œ์˜ ๊ตฌ๋ถ„์— ๋”ฐ๋ผ์„œ๋Š” ์‹๋ณ„๊ฐ€๋Šฅํ•œ ์ˆ˜์ค€์˜ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ํ•œํŽธ ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์„ฑ๊ณผ์ง€ํ‘œ๋“ค์˜ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๋Š” ์ „์ฒด ๋ถ„์„๋Œ€์ƒ ์„ฑ๊ณผ์ง€ํ‘œ ์ค‘ 31.82%์— ํ•ด๋‹นํ•˜๋Š” ์ง€ํ‘œ์—์„œ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์—ˆ์œผ๋ฉฐ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๊ฐ€ ๋ฐœ์ƒํ•œ ์„ฑ๊ณผ์ง€ํ‘œ์˜ ํ‰๊ท ์ ์ธ ์„ฑ๊ณผ์กฐ์ •๋ฅ ์€ (+) 3.73%์— ์ˆ˜์ค€์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ํ‰๊ท ์ ์ธ ์ˆ˜์ค€์—์„œ ์—ฐ๋„, ๊ธฐ๊ด€์œ ํ˜•, ํ‰๊ฐ€์œ ํ˜• ๋“ฑ์˜ ์š”์†Œ์—์„œ ๋Œ€์ฒด๋กœ (+)๋ฐฉํ–ฅ ์„ฑ๊ณผ์กฐ์ •์ด ๋ฐœ์ƒํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜ ํ‰๊ฐ€๋Œ€์ƒ๊ธฐ๊ด€๋“ค์ด ์ž์‹ ๋“ค์˜ ์„ฑ๊ณผ์‹ค์ ์„ ๊ฒฝ์˜ํ‰๊ฐ€๋‹จ์ด ํ™•์ธํ•œ ์‹ค์ ๋ณด๋‹ค ๋†’๊ฒŒ ๋ณด๊ณ ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ฐœ๊ฒฌํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํŠน๊ธฐํ•  ๋งŒ ํ•œ ์ ์€ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์„ฑ๊ณผ์กฐ์ •๋ฅ  ์—ญ์‹œ ํ‰๊ฐ€์ง€ํ‘œ๊ฐ€ ๊ณตํ†ต์ง€ํ‘œ์ธ์ง€ ๊ฐœ๋ณ„์ง€ํ‘œ์ธ์ง€ ์—ฌ๋ถ€์— ๋”ฐ๋ผ์„œ ์ƒ๋‹นํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด๊ณ  ์žˆ์œผ๋ฉฐ, ๊ฐ ๊ธฐ๊ด€์ด ๊ณตํ†ต์œผ๋กœ ์ ์šฉ๋ฐ›๋Š” ๊ณตํ†ต์ง€ํ‘œ์—์„œ ๋ณด๋‹ค ๋†’์€ ์ˆ˜์ค€์˜ ์„ฑ๊ณผ์กฐ์ •์ด ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘˜์งธ, ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ ๊ณผ์ •์—์„œ ์ „๋žต์  ํ–‰์œ„์˜ ์‹ค์ฒด์ ์ธ ๋ชจ์Šต์œผ๋กœ์˜ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์˜ ๊ด€๊ณ„๋Š” ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜์ง€ ๋ชปํ•œ ์„ฑ๊ณผ์ง€ํ‘œ๋“ค ์‚ฌ์ด์—์„œ๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„๊ฐ€ ๋‚ฎ์„์ˆ˜๋ก ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๊ฐ€ ๋งŽ์ด ๋‚˜ํƒ€๋‚  ๊ฒƒ์ด๋ผ๋Š” ๋ฏผ๊ฐ„์กฐ์ง์˜ ๋‹ค๊ธฐ๊ฐ„ ์„ฑ๊ณผ๊ณ„์•ฝ์—์„œ์˜ ์ด๋ก ์  ์˜ˆ์ธก๊ณผ ์ผ์น˜ํ•˜๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ์ฃผ์ง€ํ•  ์ ์€ ์ด๋Ÿฌํ•œ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ์—ฌ๋ถ€์— ๋”ฐ๋ผ ๋งค์šฐ ์ฐจ๋ณ„์ ์ธ ์–‘ํƒœ๋ฅผ ๋ณด์ด๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค๋Š” ์ ์ด๋‹ค. ๋‹ค์‹œ ๋งํ•ด, ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜์ง€ ๋ชปํ•œ ๊ฒฝ์šฐ์— ์žˆ์–ด์„œ๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ๋ฅ ์ด ๋‚ฎ์„์ˆ˜๋ก ์„ฑ๊ณผ์กฐ์ •์˜ ๊ฐ•ํ•˜๊ฒŒ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์—ˆ๋Š”๋ฐ ๋ฐ˜ํ•ด, ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•œ ์„ฑ๊ณผ์ง€ํ‘œ๋“ค ์‚ฌ์ด์—์„œ๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์˜ ๊ด€๊ณ„๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„๊ฐ€ ๋†’์„์ˆ˜๋ก ์„ฑ๊ณผ๋ฅผ ๋‚ฎ์ถ”๋Š” ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๊ฐ€ ๋ฐœ์ƒํ•  ๊ฒƒ์ด๋ผ๋Š” ๋ฏผ๊ฐ„๋ถ€๋ฌธ์˜ ์˜ˆ์ธก๊ณผ๋Š” ์ผ์น˜ํ•˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณด๋‹ค ๊ตฌ์ฒด์ ์œผ๋กœ ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ๋ชฉํ‘œ๋‹ฌ์„ฑ๊ณผ ์ „๋žต์  ํ–‰์œ„๋กœ์„œ์˜ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๋Š” ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•œ ์ดํ›„์—๋Š” ์ฐจ๊ธฐ ๋ชฉํ‘œ๋ฅผ ์‰ฝ๊ฒŒ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ๋‹นํ•ด์—ฐ๋„ ์„ฑ๊ณผ๋ฅผ ๋‚ฎ์ถฐ๋ณด๊ณ ํ•  ๊ฒƒ์ด๋ผ๋Š” ๋ฏผ๊ฐ„์กฐ์ง์˜ ์˜ˆ์ธก๊ณผ ๋‹ฌ๋ฆฌ, ๊ณต๊ณต์กฐ์ง์˜ ๊ฒฝ์šฐ ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•œ ์ดํ›„์— ์„ฑ๊ณผ๋ฅผ ๋†’์—ฌ ๋ณด๊ณ ํ•˜๋Š” ํ–‰์œ„์™€ ์„ฑ๊ณผ๋ฅผ ๋‚ฎ์ถฐ ๋ณด๊ณ ํ•˜๋Š” ํ–‰์œ„๊ฐ€ ๋™์‹œ์— ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ์–ด, ๋ชฉํ‘œ๋‹ฌ์„ฑ์ดํ›„ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์˜ ๊ด€๊ณ„๋Š” ์–‘ํ–ฅ์ ์ธ ๋ฐฉํ–ฅ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค๋งŒ ๋ชฉํ‘œ๋‹ฌ์„ฑ์ดํ›„ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์ •(+)์˜ ์œ ์˜๋ฏธํ•œ ๊ด€๊ณ„๋ฅผ ๋ณด์ด๊ณ  ์žˆ์—ˆ์œผ๋‚˜, ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜์ง€ ๋ชปํ•œ ๊ฒฝ์šฐ์— ๋ฐœ์ƒํ•˜๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„์— ๋น„ํ•ด ๊ทธ ๊ฐ•๋„๊ฐ€ ๋งค์šฐ ์ž‘์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜ ๋ชฉํ‘œ๋‹ฌ์„ฑ์ดํ›„์—๋„ ํ‰๊ท ์ ์œผ๋กœ ๋ชฉํ‘œ๋ฅผ ๋†’์—ฌ ๋ณด๊ณ ํ•˜๋Š” ์„ฑ๊ณผ์กฐ์ •์˜ ์œ ์ธ์ด ์žˆ์œผ๋‚˜ ์‹ค์งˆ์ ์ธ ์˜๋ฏธ์—์„œ ๊ทธ ์ •๋„์˜ ํญ์€ ๋งค์šฐ ์ž‘์€ ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ์…‹์งธ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฌํ•œ ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์˜ ๊ด€๊ณ„๊ฐ€ ๋ชฉํ‘œ๋‹ฌ์„ฑ์—ฌ๋ถ€์— ๋”ฐ๋ผ ์ฐจ๋ณ„์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ์ด์œ ๋กœ์„œ ์„ฑ๊ณผํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ์ถ”๊ตฌํ•˜๋Š” ๋ชฉํ‘œ๊ฐ€ ์กฐ์ง๊ณผ ๊ตฌ์„ฑ์›์— ๋”ฐ๋ผ ๋‹ค์–‘ํ•œ ํ‰๊ฐ€ํ™œ์šฉ์˜ ๋‹ค์–‘์„ฑ ์ƒํ™ฉ์— ๋†“์—ฌ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‹ค์–‘ํ•œ ๋ชฉํ‘œ๊ฐ€ ์ˆœ์ฐจ์ ์œผ๋กœ ์ถ”๊ตฌ๋  ์ˆ˜ ์žˆ์–ด, ํ‰๊ฐ€ํ™œ์šฉ์˜ ๋ชฉ์ ์— ๋”ฐ๋ผ ์„ฑ๊ณผ์กฐ์ •์˜ ์œ ์ธ์˜ ๋ฐฉํ–ฅ๊ณผ ํฌ๊ธฐ๋Š” ์ฐจ๋ณ„์ ์ผ ์ˆ˜ ์žˆ์Œ ์ œ์‹œํ•˜์˜€์œผ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ์‹ค์ œ ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ ์ƒํ™ฉ์—์„œ ๊ณ ๋ ค๋  ์ˆ˜ ์žˆ๋Š” ์š”์ธ๋“ค์„ ํ™œ์šฉํ•ด ๊ทธ ๊ฐ€๋Šฅ์„ฑ์„ ๊ฒ€์ฆํ•ด ๋ณด์•˜๋‹ค. ํŠนํžˆ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ์ดํ›„์— ์„ฑ๊ณผํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ๊ธฐ๊ด€ ๋ฐ ๊ตฌ์„ฑ์ด ์ถ”๊ตฌํ•  ์ˆ˜ ์žˆ๋Š” ๋ชฉํ‘œ์˜ ํ•˜๋‚˜๋กœ ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€๊ฐ€ ํ‰๊ฐ€ ์ดํ•ด์ž๋“ค์˜ ๋‚ด์™ธ๋ถ€์ ์ธ ์„ฑ๊ณผํ‰ํŒ์˜ ๋„๊ตฌ๋กœ๋„ ์‚ฌ์šฉ๋œ๋‹ค๋Š” ์ ์— ์ฐฉ์•ˆํ•˜์—ฌ (โ…ฐ) ๊ฐ ๊ธฐ๊ด€์ด ์ „๋…„๋„์™€ ๋‹นํ•ด์—ฐ๋„์— ๊ฒฝ์˜ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ํš๋“ํ•œ ์ ์ˆ˜์˜ ์ฐจ์ด, ๊ทธ๋ฆฌ๊ณ  (โ…ฑ) ๊ฒฝ์˜ํ‰๊ฐ€์—์„œ์˜ ๋‚ด๋ถ€ํ‰๊ฐ€์™€ ์™ธ๋ถ€ํ‰๊ฐ€์˜ ์—ฐ๊ณ„๊ฐ€ ์ง€์†์ ์œผ๋กœ ๊ฐ•์กฐ๋˜๊ณ  ์žˆ๊ณ  ๋‚ด๋ถ€ํ‰๊ฐ€๋Š” ์กฐ์ง ๊ตฌ์„ฑ์›์˜ ์™ธ์ ๋ณด์ƒ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‚ด๋ถ€ํ‰ํŒ์œ ์ง€์˜ ๋„๊ตฌ๋ผ๋Š” ์ ์— ๊ธฐ์ธํ•˜์—ฌ ๋‚ด์™ธํ‰ ์—ฐ๊ณ„์ •๋„์™€ ๊ฐ ์„ฑ๊ณผ์ง€ํ‘œ๊ฐ€ ์ „๋…„๋„์™€ ๋‹นํ•ด์—ฐ๋„์— ๊ฒฝ์˜ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ๋‹ฌ์„ฑํ•œ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์˜ ์ฐจ์ด๋ฅผ ๋Œ€๋ฆฌ์ง€ํ‘œ๋กœ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด์— ๋Œ€ํ•œ ๋ถ„์„๊ฒฐ๊ณผ๋Š” ์ „๋…„๋Œ€๋น„ ๊ฒฝ์˜ํ‰๊ฐ€ ์ด์ ์ด ํ•˜๋ฝํ–ˆ๋‹ค๋Š” ์‚ฌ์‹ค๊ณผ ๋‚ดยท์™ธ๋ถ€ ์ง€ํ‘œ์—ฐ๊ณ„์œจ์ด ๊ฐ•ํ™”๋œ๋‹ค๋Š” ์‚ฌ์‹ค์€ ๋ชฉํ‘œ๋‹ฌ์„ฑ์„ ํ•˜์ง€ ๋ชปํ•œ ์ง€ํ‘œ๋“ค์— ์žˆ์–ด์„œ ์„ฑ๊ณผ๋ฅผ ๋†’์—ฌ ๋ณด๊ณ ํ•  ์œ ์ธ์œผ๋กœ ์ž‘์šฉํ•˜๊ณ  ์žˆ์—ˆ์œผ๋‚˜, ํ‰๊ฐ€์ง€ํ‘œ์˜ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋ฅ ์ด ์ „๋…„๋„์— ๋น„ํ•ด ๋‚ฎ์•„์กŒ๋‹ค๋Š” ์‚ฌ์‹ค์€ ๋ชฉํ‘œ๋‹ฌ์„ฑ์ดํ›„์—๋„ ์„ฑ๊ณผ๋ฅผ ๋†’์—ฌ๋ณด๊ณ ํ•  ์œ ์ธ์œผ๋กœ ์ž‘์šฉํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜ ์ผ๋ถ€ ํ‰๊ฐ€ํ™œ์šฉ์˜ ๋‹ค์–‘์„ฑ์œผ๋กœ ์ธํ•œ ๋ชฉํ‘œ์ˆœ์ฐจ๊ฐ€์„ค์„ ์ง€์ง€ํ•˜๋Š” ์ƒํ™ฉ์ด ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๋ช‡ ๊ฐ€์ง€ ์ •์ฑ…์  ํ•จ์˜๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ์ฒซ์งธ๋Š” ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ์ „์ฒด ๊ณต๊ณต๊ธฐ๊ด€์ด ์•„๋‹Œ 51๊ฐœ ๊ธฐ๊ด€์˜ 2008๋…„๋ถ€ํ„ฐ 2012๋…„๊นŒ์ง€ ์„ฑ๊ณผํ‰๊ฐ€ ์ž๋ฃŒ๋ฅผ ๋ถ„์„์— ํ™œ์šฉํ•˜๊ณ  ์žˆ์–ด ๋ถ„์„๊ธฐ๊ฐ„๊ณผ ๋Œ€์ƒ์ด ์ œ์•ฝ์ด ์žˆ๊ธฐ๋Š” ํ•˜์ง€๋งŒ ์ œ์•ฝ๋œ ์ž๋ฃŒ ์•ˆ์—์„œ๋„ ์„ฑ๊ณผํ‰๊ฐ€ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๊ฐ€ ์ƒ๋‹น์ˆ˜ ๋ฐœ๊ฒฌ๋˜๊ณ  ์žˆ์—ˆ๋‹ค. ์‚ฌ์‹ค์ƒ ์ด๋Ÿฌํ•œ ๋ฐ˜๋ณต์ ์ธ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๊ฐ€ ๋ฐœ๊ฒฌ๋˜๊ณ  ์žˆ๋Š” ๊ฐ€์žฅ ํฐ ์ด์œ ๋กœ๋Š” ๊ฒฝ์˜ํ‰๊ฐ€์˜ ๊ณ„๋Ÿ‰ํ‰๊ฐ€๊ฐ€ ๊ฐ–๊ณ  ์žˆ๋Š” ํ•œ๊ณ„์—์„œ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์‹œ ๋งํ•ด ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€์˜ ๊ณ„๋Ÿ‰ํ‰๊ฐ€์˜ ๊ฒฝ์šฐ ํ‰๊ฐ€์˜ ๋ฐฉ์‹๊ณผ ์‚ฐ์‹ ๋“ฑ์˜ ํ‰๊ฐ€์ง€์นจ์ด ํ‰๊ฐ€๋Œ€์ƒ๊ธฐ๊ด€๊ณผ ๊ธฐํš์žฌ์ •๋ถ€์˜ ์‚ฌ์ „์ ์ธ ํ˜‘์˜๋ฅผ ํ†ตํ•ด ํ™•์ •๋˜์–ด ์ „๋‹ฌ๋จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ํ‰๊ฐ€์ง€ํ‘œ์— ์žˆ์–ด์„œ ํ‰๊ฐ€๋Œ€์ƒ๊ธฐ๊ด€๋“ค์˜ ํ‰๊ฐ€ ์‚ฐ์‹์ด๋‚˜ ๊ตฌ์„ฑ์š”์†Œ์— ๋Œ€ํ•œ ์ž์˜์ ์ธ ํ•ด์„์˜ ์—ฌ์ง€๋ฅผ ์›์ฒœ์ ์œผ๋กœ ์ œ๊ฑฐํ•˜๋Š” ๊ฒƒ์ด ์–ด๋ ต๋‹ค๋Š” ์ ์ด๋‹ค. ํ•˜์ง€๋งŒ ๊ฒฝ์˜ํ‰๊ฐ€์—์„œ์˜ ์ด๋Ÿฐ ์ž์˜์  ํ•ด์„์˜ ๊ฐ€๋Šฅ์„ฑ ๋“ฑ์˜ ๋ฌธ์ œ๋Š” ์„ค๋ น ํ•ด์„์˜ ํƒ€๋‹น์„ฑ์ด ์ธ์ •๋˜์–ด ์„ฑ๊ณผ์กฐ์ •์˜ ์ •๋‹น์„ฑ์ด ์ธ์ •๋˜๋Š” ์ƒํ™ฉ ํ•˜์—์„œ๋„ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๋Š” ํ‰๊ฐ€๋Œ€์ƒ์ž์ธ ๊ธฐ๊ด€๊ณผ ํ‰๊ฐ€์ฃผ์ฒด์ธ ๊ฒฝ์˜ํ‰๊ฐ€๋‹จ์˜ ํ˜‘์ƒ๊ณผ ์กฐ์ •๋น„์šฉ์„ ๋ฐ˜๋ณต์ ์œผ๋กœ ๋ฐœ์ƒ์‹œํ‚ด์œผ๋กœ์จ ์„ฑ๊ณผํ‰๊ฐ€์˜ ๋น„์šฉ๊ณผ ๋น„ํšจ์œจ์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ์ง์ ‘์ ์ธ ์š”์†Œ๋ผ๋Š” ์ ์—์„œ ์ด์— ๋Œ€ํ•œ ๊ฐœ์„ ์˜ ๋…ธ๋ ฅ์€ ์ง€์†์ ์œผ๋กœ ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ์ธก๋ฉด์—์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์„๋Œ€์ƒ์ธ ๊ฒฝ์˜ํ‰๊ฐ€์—์„œ์˜ ๊ณ„๋Ÿ‰์ง€ํ‘œ์˜ ๊ฒฝ์šฐ ๊ฐ ๊ธฐ๊ด€์˜ ์ž์˜์  ํ•ด์„์ด๋‚˜ ์˜๋„์ ์œผ๋กœ ์œ ๋ฆฌํ•œ ํ•ด์„์„ ๋ฐฉ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ์„ค๊ณ„ํ•˜๊ณ , ๊ฐ ๊ธฐ๊ด€์ด๋‚˜ ํ‰๊ฐ€๊ตฐ์— ๋”ฐ๋ผ ๊ณ„๋Ÿ‰์ง€ํ‘œ์˜ ํ‰๊ฐ€๋ฐฉ์‹๊ณผ ์‚ฐ์‹์— ๋Œ€ํ•œ ํ‘œ์ค€ํ™”ํ•˜๋Š” ๋ฐฉ์‹์„ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์ด ๋ฌด์—‡๋ณด๋‹ค๋„ ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์ด๋‹ค. ๋‘˜์งธ๋Š” ๋ณด๋‹ค ์ผ๋ฐ˜์ ์ธ ์ˆ˜์ค€์—์„œ ๊ณต๊ณต์กฐ์ง์˜ ์„ฑ๊ณผํ‰๊ฐ€๋ฅผ ๊ตฌ์ถ•ํ•จ์— ์žˆ์–ด ๊ณต๊ณต์กฐ์ง์˜ ๋‚ด์žฌ๋œ ํŠน์ง•์„ ๋ฐ˜์˜ํ•œ ์ œ๋„์„ค๊ณ„๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์„๊ฒฐ๊ณผ์—์„œ ์•Œ ์ˆ˜ ์žˆ๋“ฏ์ด ๊ณต๊ณต์กฐ์ง์˜ ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๋Š” ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์— ๋”ฐ๋ฅธ ์ฐจ๊ธฐ๋…„๋„ ์ธ์„ผํ‹ฐ๋ธŒ์™€ ๊ฐ™์€ ๋‹จ๊ธฐ์ ์ธ ๊ธˆ์ „์  ์ธ์„ผํ‹ฐ๋ธŒ์— ๊ธฐ์ธํ•˜์ง€ ์•Š๊ณ  ์žˆ์œผ๋ฉฐ, ๋ชฉํ‘œ๋‹ฌ์„ฑ์—ฌ๋ถ€์— ์ƒ๊ด€์—†์ด(๋ชฉํ‘œ๋‹ฌ์„ฑ์„ ํ•œ ์ดํ›„์—๋„) ์ง€์†์ ์ธ ์„ฑ๊ณผ์กฐ์ •๊ณผ ์ง€ํ‘œ์— ๋Œ€ํ•œ ์ž์˜์ ์ธ ํ•ด์„์ด ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋‹ค๋Š” ๊ฒƒ์€ ๊ถ๊ทน์ ์œผ๋กœ ๋‘ ๊ฐ€์ง€ ์ฐจ์›์— ๋Œ€ํ•œ ๊ณ ๋ ค๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ์‹œ์‚ฌ์ ์„ ์ œ๊ณตํ•œ๋‹ค. ๋จผ์ €, ๊ณต๊ณต๊ธฐ๊ด€ ์กฐ์ง ๊ด€๋ฆฌ์˜ ์ œ๋„์ ์ธ ์ธก๋ฉด์—์„œ ๋ณด๋ฉด ๊ณต๊ณต๊ธฐ๊ด€ ์žฅ์˜ ์งง์€ ์ž„๊ธฐ์™€ ๊ณต๊ณต๊ธฐ๊ด€ ๊ตฌ์„ฑ์›๋“ค์˜ ์žฆ์€ ์ˆœํ™˜๋ณด์ง์˜ ๋ฌธ์ œ์— ๋Œ€ํ•œ ์ฒ˜๋ฐฉ์ด ํ•„์š”ํ•จ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์„๋Œ€์ƒ์ธ ๊ณต๊ณต๊ธฐ๊ด€์˜ ๊ฒฝ์šฐ ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜์ž๋“ค์˜ ์ž„๊ธฐ๊ฐ€ 3๋…„์œผ๋กœ ์ •ํ•ด์ ธ ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๊ณต๊ณต๊ธฐ๊ด€ ์ข…์‚ฌ์ž์˜ ์žฆ์€ ์ˆœํ™˜๋ณด์ง์€ ์ •๋ถ€๊ฐ€ ์ถ”์ง„ํ•˜๋Š” ๊ณต๊ณต๊ธฐ๊ด€ ์ •์ƒํ™”์˜ ์ค‘์ ๊ณผ์ œ์˜ ํ•˜๋‚˜๋กœ ์ž๋ฆฌ์žก๊ณ  ์žˆ์„ ์ •๋„๋กœ ๊ทธ ๋ฌธ์ œ์ ์ด ๋“œ๋Ÿฌ๋‚˜๊ณ  ์žˆ๋‹ค.๋”ฐ๋ผ์„œ ์งง์€ ์ž„๊ธฐ์™€ ์žฆ์€ ์ˆœํ™˜๋ณด์ง์œผ๋กœ ์ธํ•œ ๊ทผ์‹œ์•ˆ์  ์„ฑ๊ณผ๊ด€๋ฆฌ๋Š” ์กฐ์ง์˜ ์„ฑ๊ณผ๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•œ ์ „๋žต์  ํˆฌ์ž๋‚˜ ํ˜์‹ ์ ์ธ ์กฐ์ง๊ฐœํŽธ๊ณผ ๊ฐ™์€ ๋ฐฉํ–ฅ์— ๋Œ€ํ•œ ๊ด€์‹ฌ๋ณด๋‹ค๋Š” ์„ฑ๊ณผ์กฐ์ •์„ ํ†ตํ•œ ํ‰ํŒ๊ตฌ์ถ•๊ณผ ๊ฐ™์€ ์ „๋žต์  ํ–‰์œ„์— ๋Œ€ํ•œ ๋ณด๋‹ค ๋งŽ์€ ๋…ธ๋ ฅ๋ฐฐ๋ถ„์ด ์ด๋ฃจ์–ด์งˆ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๊ณต๊ณต๋ถ€๋ฌธ์˜ ์ข…์‚ฌ์ž๋“ค์˜ ํ–‰ํƒœ์ ์ธ ์ธก๋ฉด์—์„œ ๋ณด๋ฉด ์„ฑ๊ณผํ‰๊ฐ€์‹œ์Šคํ…œ์ด ๊ฐ–๋Š” ์„ฑ๊ณผ์™€ ๋ณด์ƒ์˜ ์—ฐ๊ณ„๋ฅผ ํ†ตํ•œ ๋™๊ธฐ์œ ๋ฐœ์ด๋ผ๋Š” ๋งค์ปค๋‹ˆ์ฆ˜์„ ๊ตฌ์ถ•ํ•จ์— ์žˆ์–ด์„œ ๊ณต๊ณต์กฐ์ง ์ข…์‚ฌ์ž๋“ค์˜ ๋ฐ˜์‘ํ–‰ํƒœ์— ๋Œ€ํ•œ ๊ณ ๋ ค๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์ด๋‹ค. ์‚ฌ์‹ค์ƒ ๋ฏผ๊ฐ„๋ถ€๋ถ„์˜ ๋‹ค๊ธฐ๊ฐ„ ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ์ „๋žต์  ํ–‰์œ„์ด๋ก ์ด ๊ณต๊ณต์กฐ์ง์— ์ ์šฉ๋˜๊ธฐ ์–ด๋ ค์šด ์ด์œ  ์ค‘ ํ•˜๋‚˜๋Š” ๊ณต๊ณต๋ถ€๋ฌธ ์ข…์‚ฌ์ž๋“ค์ด ๊ฐ–๋Š” ๊ธˆ์ „์  ๋ณด์ƒ๋™๊ธฐ ์ด์™ธ์˜ ๋™๊ธฐ๋ถ€์—ฌ ๋งค์ปค๋‹ˆ์ฆ˜์˜ ๋‹ค์–‘์„ฑ์— ๊ธฐ์ธํ•œ๋‹ค. ๋‹ค์‹œ ๋งํ•ด ๊ธˆ์ „์  ๋ณด์ƒ์ฒด๊ณ„๊ฐ€ ์„ฑ๊ณผํ–ฅ์ƒ์„ ์œ„ํ•œ ๋™๊ธฐ๋ถ€์—ฌ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค๋Š” ๋‹จ์ˆœํ•œ ๊ฐ€์ •์ด ๊ณต๊ณต๋ถ€๋ฌธ ์ข…์‚ฌ์ž๋“ค์˜ ๊ทผ๋ฌด ๋™๊ธฐ๋ฅผ ๋ชจ๋‘ ์„ค๋ช…ํ•  ์ˆ˜ ์—†์œผ๋ฉฐ ๊ทธ๋“ค์ด ๊ฐ–๋Š” ๊ธˆ์ „์  ๋ณด์ƒ๋™๊ธฐ ์ด์™ธ์˜ ์Šน์ง„๊ณผ ํ‰ํŒ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ๋ณด์ƒ๋™๊ธฐ์™€ ๋‚ด์žฌ์  ๋ณด์ƒ๋™๊ธฐ์— ๋Œ€ํ•œ ๊ณ ๋ ค๊ฐ€ ํ•„์š”ํ•˜๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋ฐํžŒ๋ฐ”์™€ ๊ฐ™์ด ์กฐ์ง ๊ตฌ์„ฑ์›์˜ ์„ฑ๊ณผํ–ฅ์ƒ์— ๋Œ€ํ•œ ๋…ธ๋ ฅ์ด ์กฐ์ง์˜ ์„ฑ๊ณผํ–ฅ์ƒ์œผ๋กœ ์ด์–ด์งˆ ์ˆ˜ ์žˆ๋„๋กํ•˜๊ธฐ ์œ„ํ•œ ๋‚ด๋ถ€ํ‰๊ฐ€์ง€ํ‘œ์™€ ์™ธ๋ถ€ํ‰๊ฐ€์ง€ํ‘œ์˜ ์ง€์†์ ์ธ ์—ฐ๊ณ„๊ฐ•ํ™”๋Š” ์—ญ๋Œ€์‘์ ์œผ๋กœ ๋‚ด๋ถ€ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๊ฐ€ ์กฐ์ง์˜ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๋กœ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์˜คํžˆ๋ ค ์„ฑ๊ณผํ‰๊ฐ€์ฒด๊ณ„์—์„œ์˜ ์กฐ์ง๊ตฌ์„ฑ์›์˜ ๊ฐ–๋Š” ์ง€ํ–ฅ์ ๊ณผ ๋™๊ธฐ๋ถ€์—ฌ ์š”์ธ์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ๊ณ ๋ ค์™€ ๋ถ„์„์ด ๋ฐ”ํƒ•์ด ๋œ ์ œ๋„ ์„ค๊ณ„๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ์‚ฌ์‹ค์„ ๋ฐ˜์ฆํ•œ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‹ค๋งŒ ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์„ ์ž๋ฃŒ๊ฐ€ ๊ณต๊ณต๊ธฐ๊ด€์—์„œ ์‚ฐ์ถœ๋œ ๋ฌธ์„œ์—๋งŒ ์˜์กดํ•˜๊ณ  ์žˆ์–ด ์„ฑ๊ณผ์กฐ์ •๋ฐœ์ƒ์˜ ์˜๋„์„ฑ(on purpose)์„ ์™„๋ฒฝํ•˜๊ฒŒ ์‹๋ณ„ํ•  ์ˆ˜ ์—†์—ˆ๋‹ค๋Š” ์ , ๊ทธ๋ฆฌ๊ณ  ๋ถ„์„๋Œ€์ƒ์˜ ์ˆ˜์™€ ์‹œ๊ณ„๊ฐ€ ๋‹ค์†Œ ์งง๋‹ค๋Š” ์ , ๊ทธ๋ฆฌ๊ณ  ๋ถ„์„์ž๋ฃŒ์˜ ํ•œ๊ณ„๋กœ ์ธํ•˜์—ฌ ์ „๋žต์  ํ–‰์œ„์˜ ์‹ค์ œ์  ๋ชจ์Šต์œผ๋กœ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„๋งŒ์„ ๋‹ค๋ฃจ๊ณ  ์žˆ๋‹ค๋Š” ์ ์€ ํ•œ๊ณ„์ ์œผ๋กœ ์ง€์ ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ–ฅํ›„ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ๋“ค์ด ๋ณด์™„๋œ ์ง€์†์ ์ธ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์ด๋‹ค.์ œ1์žฅ ์„œ ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ์˜์˜ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  3 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ๊ณผ ๊ตฌ์„ฑ 6 ์ œ2์žฅ ์ด๋ก ์  ๋…ผ์˜์™€ ์„ ํ–‰์—ฐ๊ตฌ์˜ ๊ฒ€ํ†  9 ์ œ1์ ˆ ์„ฑ๊ณผํ‰๊ฐ€์™€ ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ์™œ๊ณก 9 1. ์„ฑ๊ณผ๊ด€๋ฆฌ์ฒด๊ณ„์— ์žˆ์–ด์„œ์˜ ์„ฑ๊ณผํ‰๊ฐ€์˜ ์ค‘์š”์„ฑ 9 1) ์„ฑ๊ณผ์™€ ์„ฑ๊ณผ๊ด€๋ฆฌ์ฒด๊ณ„ 9 2) ์„ฑ๊ณผํ‰๊ฐ€์˜ ์ค‘์š”์„ฑ 16 3) ์„ฑ๊ณผํ‰๊ฐ€์˜ ๋ชฉ์  18 2. ์„ฑ๊ณผํ‰๊ฐ€์˜ ์ˆœ๊ธฐ๋Šฅ๊ณผ ์—ญ๊ธฐ๋Šฅ 21 1) ์„ฑ๊ณผํ‰๊ฐ€์˜ ์ˆœ๊ธฐ๋Šฅ 21 2) ์„ฑ๊ณผํ‰๊ฐ€์˜ ์—ญ๊ธฐ๋Šฅ๊ณผ ์™œ๊ณก 22 3. ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ์™œ๊ณกํ˜„์ƒ์˜ ์œ ํ˜•ํ™” 25 4. ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ์™œ๊ณกํ˜„์ƒ ๋ฐœ์ƒ ์›์ธ์— ๋Œ€ํ•œ ์ด๋ก ์  ๋…ผ์˜ 32 1) ๊ธฐ๋Œ€์ด๋ก (expectancy theory)๊ณผ ์ด์ต์กฐ์ •ํ–‰์œ„์˜ ๋ฐœ์ƒ 32 2) ๋ชฉํ‘œ์„ค์ •์ด๋ก (goal-setting theory)๊ณผ ๊ด€๋ฃŒ์˜ ๋Œ€์‘ 33 3) ์ฃผ์ธ-๋Œ€๋ฆฌ์ธ ์ด๋ก (Principle-agent theory) 35 ์ œ2์ ˆ ์šฐ๋ฆฌ๋‚˜๋ผ ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„์—์„œ์˜ ์™œ๊ณกํ–‰ํƒœ ์‹คํƒœ 37 1. ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„์—์„œ์˜ ์™œ๊ณกํ–‰ํƒœ์— ๋Œ€ํ•œ ์ธ์‹ 37 1) ์„ฑ๊ณผ๊ด€๋ฆฌ์ œ๋„ ๋ฐ ์„ฑ๊ณผํ‰๊ฐ€ ์ œ๋„ 37 2) ์„ฑ๊ณผ๊ด€๋ฆฌ์ œ๋„์˜ ๋ฌธ์ œ์ ์— ๋Œ€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 42 3) ์„ฑ๊ณผํ‰๊ฐ€์ œ๋„์—์„œ์˜ ์™œ๊ณก ํ–‰์œ„์— ๋Œ€ํ•œ ์ธ์‹์˜ ์žฌ๋ถ„์„ 47 2. ๊ณต๊ณต๋ถ€๋ฌธ ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ์„ฑ๊ณผ์™œ๊ณก ๋ฐ ์กฐ์ • ์‚ฌ๋ก€ 51 1) ์žฌ์ •์„ฑ๊ณผ๋ณด๊ณ ์„œ์— ๋‚˜ํƒ€๋‚œ ์™œ๊ณกํ–‰ํƒœ ๋ถ„์„ 51 2) ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€์—์„œ ๋‚˜ํƒ€๋‚œ ์„ฑ๊ณผ์™œ๊ณก ๋ฐ ์กฐ์ •์‚ฌ๋ก€ : ๊ฐ์‚ฌ์› ์ ๋ฐœ ์‚ฌ๋ก€ 57 3) ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€์—์„œ ๋‚˜ํƒ€๋‚œ ์„ฑ๊ณผ์™œ๊ณก ๋ฐ ์กฐ์ •์‚ฌ๋ก€ : ์–ธ๋ก ๋ณด๋„ ๋ฐ ์˜ํ˜น๋“ค 61 ์ œ3์ ˆ ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ์ „๋žต์  ํ–‰์œ„์™€ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„ 65 1. ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ์ „๋žต์  ํ–‰์œ„ 65 2. ์ „๋žต์  ํ–‰์œ„์˜ ์œ ํ˜•ํ™” 67 3. ์ „๋žต์  ํ–‰์œ„์— ๋Œ€ํ•œ ์ด๋ก ์  ๋…ผ์˜ 73 1) ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ๋ชฉํ‘œ์„ค์ • ๋ฐ ๊ฐ€์ • 73 2) ์ „๋žต์  ํ–‰์œ„์— ๋Œ€ํ•œ ์ด๋ก ์  ๋…ผ์˜๋“ค 75 3) ์†Œ๊ฒฐ : ์„ฑ๊ณผํ‰๊ฐ€์™€ ์ „๋žต์  ํ–‰์œ„์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ์— ๋Œ€ํ•œ ๋น„ํŒ์  ๊ฒ€ํ†  87 4. ๊ณต๊ณต์กฐ์ง์˜ ์„ฑ๊ณผํ‰๊ฐ€์—์„œ ์ „๋žต์  ํ–‰์œ„๋กœ์„œ์˜ ๋ชฉํ‘œ์„ค์ •๊ณผ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์˜ ๊ด€๊ณ„ 90 1) ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ๋ชฉํ‘œ์„ค์ •๊ณผ ์„ฑ๊ณผ์กฐ์ •ํ–‰์œ„์˜ ๊ด€๊ณ„ 90 2) ๊ณต๊ณต์กฐ์ง์˜ ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ๋ชฉํ‘œ์„ค์ •๊ณผ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„ 93 5. ๊ณต๊ณต์กฐ์ง์—์„œ์˜ ํ‰๊ฐ€ํ™œ์šฉ ๋ฐ ๋ชฉ์ ์˜ ๋‹ค์–‘์„ฑ๊ณผ ๋ชฉํ‘œ๊ด€๋ฆฌ 97 1) ๊ณต๊ณต์กฐ์ง์—์„œ์˜ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์žฌ๊ฒ€ํ†  97 2) ๊ณต๊ณต๋ถ€๋ฌธ์—์„œ์˜ ๋ชฉํ‘œ๊ด€๋ฆฌ : ๋ชฉํ‘œ์ˆœ์ฐจ๊ฐ€์„ค(goal-sequence hypothesis) 102 6. ๊ณต๊ณต์กฐ์ง ์„ฑ๊ณผํ‰๊ฐ€์—์„œ์˜ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ๊ฐ€์„ค์˜ ๋„์ถœ 105 ์ œ3์žฅ ์—ฐ๊ตฌ ์„ค๊ณ„ ๋ฐ ๋ถ„์„ ๋ชจํ˜• 111 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ์ด๋ก ์  ๋ถ„์„ํ‹€ 111 ์ œ2์ ˆ ๋ถ„์„๋Œ€์ƒ์˜ ์„ ์ • ๋ฐ ํŠน์ง• 114 1. ๋ถ„์„๋Œ€์ƒ์˜ ์„ ์ • : ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ 114 2. ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ ์ œ๋„์˜ ํŠน์ง• 116 1) ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ ์ œ๋„ ๋ฐ ์ง€ํ‘œ์ฒด๊ณ„ 116 2) ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€ ์ ˆ์ฐจ์˜ ํŠน์„ฑ๊ณผ ์„ฑ๊ณผ์™œ๊ณก ๊ฐ€๋Šฅ์„ฑ 122 ์ œ3์ ˆ ์ฃผ์š”๋ณ€์ˆ˜์˜ ์ธก์ • ๋ฐ ๋ถ„์„์ž๋ฃŒ 128 1. ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€๊ณผ์ •์—์„œ์˜ ์„ฑ๊ณผ์กฐ์ •๊ณผ ๋ชฉํ‘œ๋‹ฌ์„ฑ์˜ ์ธก์ • 128 2. ๊ณต๊ณต๊ธฐ๊ด€ ๊ฒฝ์˜ํ‰๊ฐ€์˜ ํ‰๊ฐ€ํ™œ์šฉ์˜ ๋‹ค์–‘์„ฑ์— ๋”ฐ๋ฅธ ์„ฑ๊ณผ์กฐ์ •์œ ์ธ์— ๋Œ€ํ•œ ๊ณ ๋ ค 133 3. ๋ถ„์„์ž๋ฃŒ ๋ฐ ๊ธฐํƒ€ ๋ณ€์ˆ˜์ธก์ • 138 1) ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐ ๊ธฐ์ˆ ํ†ต๊ณ„ 138 2) ๊ธฐํƒ€ ๋ณ€์ˆ˜์˜ ์ธก์ • 140 4. ๋ถ„์„๋ชจํ˜•์˜ ์„ ์ • 145 ์ œ4์žฅ ๋ถ„์„ ๊ฒฐ๊ณผ 148 ์ œ1์ ˆ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •์˜ ๋ถ„ํฌ 148 1. ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์˜ ๋ถ„ํฌ 148 2. ์„ฑ๊ณผ์กฐ์ •๋„์˜ ๋ถ„ํฌ 154 ์ œ2์ ˆ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„ 159 1. ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„ 159 2. ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„๊ฐ€ ์„ฑ๊ณผ์กฐ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 164 ์ œ3์ ˆ ํ‰๊ฐ€ํ™œ์šฉ์˜ ๋ชฉ์  ๋‹ค์–‘์„ฑํ•˜์—์„œ ๋ชฉํ‘œ๋‹ฌ์„ฑ๋„์™€ ์„ฑ๊ณผ์กฐ์ •์˜ ๊ด€๊ณ„ 180 1. ํ‰๊ฐ€ํ™œ์šฉ์˜ ๋ชฉ์  ๋‹ค์–‘์„ฑ์ด ์„ฑ๊ณผ์กฐ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ๋ถ„์„ (1) 180 2. ํ‰๊ฐ€ํ™œ์šฉ์˜ ๋ชฉ์  ๋‹ค์–‘์„ฑ์ด ์„ฑ๊ณผ์กฐ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ๋ถ„์„ (2) 183 3. ์†Œ๊ฒฐ 188 ์ œ5์žฅ ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  190 ์ œ1์ ˆ ๋ถ„์„ ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 190 ์ œ2์ ˆ ์ •์ฑ…์  ํ•จ์˜ ๋ฐ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 195 1. ์ •์ฑ…์  ํ•จ์˜ 195 2. ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 198 ์ฐธ๊ณ ๋ฌธํ—Œ 200 ๋ถ€๋ก 214 Abstract 217Docto

    Public Pensions, Private Transfers, and Subjective Well-Being: A Focus on the Role of Income Instability Reduction

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    ๊ณต์ ์—ฐ๊ธˆ์ œ๋„์˜ ๋ชฉํ‘œ๊ฐ€ ์•ˆ์ •์ ์ธ ์†Œ๋“๋ณด์žฅ์„ ํ†ตํ•œ ์ˆ˜๊ธ‰์ž์˜ ๊ฒฝ์ œ์  ์ƒํ™œ์•ˆ์ •๊ณผ ๋ณต๋ฆฌ์ฆ์ง„์— ์žˆ ๋‹ค๋Š” ์ ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๊ทธ ๋™์•ˆ ๊ณต์ ์—ฐ๊ธˆ์ œ๋„์˜ ํšจ๊ณผ๋Š” ๋นˆ๊ณค์™„ํ™”์™€ ์†Œ๋“์žฌ๋ถ„๋ฐฐ ์ธก๋ฉด์— ์ง‘์ค‘๋˜์–ด ์™”๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ณต์ ์—ฐ๊ธˆ์ œ๋„๊ฐ€ ๊ฐ–๊ณ  ์žˆ๋Š” ์†Œ๋“์•ˆ์ •ํ™” ๊ธฐ๋Šฅ์— ์ดˆ์ ์„ ๋‘๊ณ , ์šฐ๋ฆฌ๋‚˜๋ผ ๋…ธ์ธ๊ฐ€๊ตฌ๋“ค์ด ๊ฒฝํ—˜ํ•˜๊ณ  ์žˆ๋Š” ๊ณต์ ์—ฐ๊ธˆ์†Œ๋“๊ณผ ์‚ฌ์ ์ด์ „์†Œ๋“์˜ ์†Œ๋“๋ถˆ์•ˆ์ •์„ฑ(income instability) ์™„ํ™” ๊ธฐ๋Šฅ์„ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ์–‘์ž์˜ ๊ธฐ๋Šฅ์ด ๋…ธ๋…„์ธต์˜ ํ›„์ƒ(์‚ถ์˜ ๋งŒ์กฑ๋„)์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ ์šฐ๋ฆฌ๋‚˜๋ผ ๋…ธ์ธ๊ฐ€๊ตฌ์˜ ์†Œ๋“๋ถˆ์•ˆ์ •์„ฑ์„ ๊ฐ€์žฅ ๋งŽ์ด ์™„ํ™”ํ•˜๋Š” ์†Œ๋“์›์ฒœ์€ ์‚ฌ์ ์ด์ „์†Œ๋“์ด์—ˆ์œผ๋‚˜, ๊ณต์ ์—ฐ๊ธˆ์„ ์ˆ˜๊ธ‰ํ•˜๋Š” ๊ฐ€๊ตฌ๋“ค๋กœ ํ•œ์ •ํ•˜๋Š” ๊ฒฝ์šฐ ๊ณต์ ์—ฐ๊ธˆ์†Œ๋“์˜ ์—ญํ• ์ด ๋” ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ๋…ธ์ธ๋“ค์ด ๊ฒฝํ—˜ํ•˜๊ณ  ์žˆ๋Š” ์†Œ๋“๋ถˆ์•ˆ์ •์„ฑ์€ ๋…ธ์ธ๋“ค์˜ ์‚ถ์˜ ๋งŒ์กฑ๋„์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ์—ˆ์œผ๋ฉฐ, ์‚ฌ์ ์ด์ „๊ณผ ๊ณต์ ์—ฐ๊ธˆ์˜ ์—ญํ• ์€ ์ฐจ๋ณ„์ ์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ฆ‰, ๊ณต์ ์—ฐ๊ธˆ์˜ ์†Œ๋“์•ˆ์ •ํ™”๊ธฐ๋Šฅ์€ ์‚ถ์˜ ๋งŒ์กฑ๋„์— ๊ธ์ •์ ์ธ ์—ญํ• ์„ ํ•˜๋Š” ๋ฐ˜๋ฉด, ์‚ฌ์ ์ด์ „์†Œ๋“์˜ ์†Œ๋“์•ˆ์ •ํ™”๊ธฐ๋Šฅ์€ ๋…ธ์ธ๋“ค์˜ ์‚ถ์˜ ๋งŒ์กฑ๋„์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ณต์ ์—ฐ๊ธˆ์ œ๋„๊ฐ€ ๊ฐ–๊ณ  ์žˆ๋Š” ์†Œ๋“์•ˆ์ •ํ™” ๊ธฐ๋Šฅ, ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ํ†ตํ•œ ์ฐจ๋ณ„์ ์ธ ์‹ฌ๋ฆฌ์  ํŽธ์ต์˜ ์กด์žฌ๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.Various studies about the public pension system has focused on poverty reduction and the income redistribution effect even though the public pension system aims at improving well-being and ensuring economic security for the elderly by providing ongoing pension payments. Instead, this study analyzes the role of public pension income, together with private transfer income, on the reduction of elderly household income insecurity, and investigates the impact of the reduction in insecurity on the subjective well-being of the elderly. The results show that, first, private transfer income lowers the income insecurity of the elderly most, but public pension income lowers income insecurity more when the elderly receive a pension payment. Second, income insecurity of the elderly has a negative impact on their life satisfaction but there are noticeable differentials. The impact of private transfer income on reducing income insecurity does not have a significant impact on the life satisfaction of the elderly while the impact of public pension income has a positive impact on life satisfaction. This work underscores the public pension role in securing income grounded on this evidence and emphasizes the psychological benefits of public pension income to the elderly

    ํ•จ์ˆ˜์˜ ์—ฐ์† ๊ฐœ๋…์˜ ์—ญ์‚ฌ์  ๊ณ ์ฐฐ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ˆ˜ํ•™๊ต์œก๊ณผ, 2015. 2. ์ตœ์˜๊ธฐ.๋ณธ ๋…ผ๋ฌธ์€ ํ•จ์ˆ˜์˜ ์—ฐ์†์— ๋Œ€ํ•œ ํ•™์ƒ๋“ค์˜ ๊ด€๋…๊ณผ ํ•™๋ฌธ์ˆ˜ํ•™์˜ ๊ฐœ๋…์˜ ์ฐจ์ด์— ๋Œ€ํ•œ ์—ฌ๋Ÿฌ ๋…ผ๋ฌธ๋“ค์˜ ๋ณด๊ณ ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์œผ๋กœ๋ถ€ํ„ฐ ์ถœ๋ฐœํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๋‘ ๊ฐ€์ง€ ์—ฐ๊ตฌ ๋ฌธ์ œ๋ฅผ ์„ค์ •ํ–ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ํ•จ์ˆ˜์˜ ์—ฐ์†์— ๋Œ€ํ•œ ํ•™๋ฌธ์ˆ˜ํ•™๊ณผ ํ•™๊ต์ˆ˜ํ•™ ๊ทธ๋ฆฌ๊ณ  ํ•™์ƒ๋“ค์˜ ๊ฐœ๋…์ด ์–ด๋–ป๊ฒŒ ๋‹ค๋ฅธ๊ฐ€ ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋‘ ๋ฒˆ์งธ๋Š” ์—ฐ์†์— ๋Œ€ํ•œ ํ•™์ƒ๋“ค์˜ ๊ด€๋…๊ณผ ํ•™๋ฌธ์ˆ˜ํ•™์˜ ๊ฐœ๋…์ด ๋‹ค๋ฅด๋‹ค๋ฉด ํ•™์ƒ๋“ค์€ ์™œ ๊ทธ๋Ÿฌํ•œ ๊ด€๋…์„ ๊ฐ–๊ฒŒ ๋˜์—ˆ์œผ๋ฉฐ, ๋˜ ํ•™๋ฌธ์ˆ˜ํ•™์€ ์™œ ํ˜„๋Œ€์ ์ธ ์—ฐ์†์˜ ๊ฐœ๋…์„ ๊ฐ–๊ฒŒ ๋˜์—ˆ๋Š”์ง€๋ฅผ ํƒ๊ตฌํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋ฌธ์ œ๋ฅผ ์œ„ํ•ด ํ•™๋ฌธ์ˆ˜ํ•™์˜ ํ•ด์„ํ•™๊ณผ ์œ„์ƒ์ˆ˜ํ•™์˜ ํ•จ์ˆ˜์˜ ์—ฐ์†์˜ ์ •์˜์™€ ๊ต์œก๊ณผ์ •, ๊ต๊ณผ์„œ, ๊ทธ๋ฆฌ๊ณ  ํ•™์ƒ๋“ค์˜ ์—ฐ์†์˜ ๊ฐœ๋…์„ ๋น„๊ตํ•˜์˜€๊ณ , ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋ฌธ์ œ์— ๋‹ตํ•˜๊ธฐ ์œ„ํ•ด ํ•จ์ˆ˜์˜ ์—ฐ์†์˜ ์—ญ์‚ฌ์  ๋ฐœ๋‹ฌ ๊ณผ์ •๊ณผ ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค ์ฒ ํ•™์˜ ์—ฐ์†์˜ ๊ฐœ๋…์„ ๊ณ ์ฐฐํ•˜์˜€๋‹ค. ํ•™์ƒ๋“ค์€ ํ•œ ๊ฐœ์˜ ์‹ ๋˜๋Š” ๋Š์–ด์ง€์ง€ ์•Š๊ณ  ์ด์–ด์ง„ ๊ณก์„ ์œผ๋กœ ํ•จ์ˆ˜์˜ ์—ฐ์†์„ ์ƒ๊ฐํ•˜๊ณ  ์žˆ์—ˆ๋Š”๋ฐ ๊ทธ๊ฒƒ์€ ํ•™๋ฌธ์ˆ˜ํ•™์˜ ๊ฐœ๋…๊ณผ๋Š” ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ์ง€๋งŒ ์—ญ์‚ฌ์ ์œผ๋กœ ์ˆ˜ํ•™์ž๋“ค์ด ์—ฐ์†์˜ ๊ฐœ๋…์„ ํ˜„๋Œ€์ ์œผ๋กœ ์ •๋ฆฝํ•˜๊ธฐ๊นŒ์ง€ ๊ฒช์—ˆ๋˜ ๊ณผ์ •๊ณผ ๋น„์Šทํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ํ˜„์ƒ์„ ์˜ค๋ฅ˜๋กœ์„œ ๋ณด๊ธฐ๋ณด๋‹ค๋Š” ํ•™๋ฌธ์ˆ˜ํ•™์˜ ์—ฐ์†์˜ ๊ฐœ๋…์„ ํ•™์Šตํ•˜๊ธฐ ์œ„ํ•œ ํ•˜๋‚˜์˜ ๊ด€๋ฌธ์œผ๋กœ ๋ฐ”๋ผ๋ณผ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. Leibniz์™€ Euler, Fourier ๋“ฑ์— ์˜ํ•ด ํ•จ์ˆ˜์˜ ์—ฐ์† ๊ฐœ๋…์€ ์กฐ๊ธˆ์”ฉ ๋ณ€ํ™”ํ•˜์˜€์œผ๋ฉฐ, 19์„ธ๊ธฐ Cauchy์™€ Weierstrass์— ์˜ํ•ด ์—„๋ฐ€์„ฑ๊ณผ ์‚ฐ์ˆ ํ™”๊ฐ€ ๋ถ€์—ฌ๋˜์—ˆ๋‹ค. Cauchy์™€ Weierstrass๋Š” ๊ณต๊ฐ„์  ์ง๊ด€์œผ๋กœ๋ถ€ํ„ฐ ์ถœ๋ฐœํ•œ ๋ฏธ์ ๋ถ„ํ•™์— ์‚ฐ์ˆ ์  ํ•ด์„์„ ํ•จ์œผ๋กœ์จ ์ˆ˜ํ•™์ด ๊ณต๊ฐ„์—์„œ ํƒˆํ”ผํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด ์‚ฐ์ˆ ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•œ ๊ฒƒ์€ ์ง‘ํ•ฉ๋ก ์˜ ๋ฐœ๋‹ฌ ๋•๋ถ„์ด์—ˆ๋‹ค. Grabiner์™€ Lakatos, Koetsier๋Š” ํ•จ์ˆ˜์˜ ์—ฐ์†์— ๋Œ€ํ•œ Cauchy์˜ ์—…์ ์„ ํ˜๋ช…์ ์ธ ๊ฒƒ์œผ๋กœ ๋ณด์•˜๋‹ค. ์—ญ์‚ฌ์ ์œผ๋กœ ์ˆ˜ํ•™์ž๋“ค์—๊ฒŒ๋„ ํ•จ์ˆ˜์˜ ์—ฐ์†์— ๋Œ€ํ•œ ์‚ฐ์ˆ ํ™”๋œ ์˜ ๋ฐฉ๋ฒ•์€ ํ˜๋ช…์ด๋ผ๊ณ  ๋ถˆ๋ฆด ์ •๋„๋กœ ์ถฉ๊ฒฉ์ ์ด๊ณ , ์ƒˆ๋กœ์šด ์ด๋ก ์ด์—ˆ์œผ๋ฉฐ, ์ด๊ฒƒ์€ ๊ต์œก์ ์œผ๋กœ ์‹œ์‚ฌํ•˜๋Š” ๋ฐ”๊ฐ€ ํฌ๋‹ค. ์ˆ˜ํ•™์ž๋“ค์—๊ฒŒ๋„ ํ˜๋ช…์ ์ด์—ˆ๋˜ ํ•™๋ฌธ ์ˆ˜ํ•™์  ๊ฐœ๋…์„ ํ•™์ƒ๋“ค์—๊ฒŒ ์ œ์‹œํ•˜๊ณ  ๊ทธ๊ฒƒ์ด ๋ฐ”๋กœ ํ•จ์ˆ˜์˜ ์—ฐ์†์˜ ์ •์˜๋ผ๊ณ  ๋ง์„ ํ•œ๋‹ค๋ฉด ํ•™์ƒ๋“ค์€ ์™œ ๊ทธ๋Ÿฐ์ง€ ๋ชจ๋ฅธ ์ฑ„ ์ˆ˜ํ•™์„ ํ•ด์•ผ ํ•˜๊ณ  ์ˆ˜ํ•™์— ๋Œ€ํ•œ ๋ฐ˜๊ฐ์„ ์šฐ๋ ค๊ฐ€ ์žˆ๋‹ค. ์—ญ์‚ฌ์  ๊ณ ์ฐฐ์˜ ์ผ๋ถ€๋กœ ์—ฐ์†์— ๋Œ€ํ•ด ์ฒด๊ณ„์  ์ •๋ฆฌ๋ฅผ ์‹œ๋„ํ–ˆ๋˜ ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค์˜ ์ •์˜๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค์—๊ฒŒ ์ง„์ •ํ•œ ์—ฐ์†์˜ ์˜๋ฏธ๋Š” ๋ถ„ํ• ๋ถˆ๊ฐ€๋Šฅํ•œ ํ•˜๋‚˜์˜ ์ „์ฒด๋กœ ๋ณด์ธ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ๋Ÿฌํ•œ ์—ฐ์†์˜ ๊ฐœ๋…์€ Cantor์˜ ์—ฐ์†์ฒด์˜ ๊ฐœ๋…๊ณผ๋„ ๋งค์šฐ ์œ ์‚ฌํ•˜๋ฉฐ ๋ช‡๋ช‡ ๋…ผ๋ฌธ๋“ค์€ ํ•™๋ฌธ์ˆ˜ํ•™์˜ ์—ฐ์†์˜ ๊ฐœ๋…์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค๊ณ  ๋ณด๊ณ ํ•˜๊ณ  ์žˆ๋‹ค. ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค์˜ ์—ฐ์†์€ ๊ณต๊ฐ„์ƒ์—์„œ ์ƒ๋Œ€์ ์ธ ์œ„์น˜๋ฅผ ๊ฐ–๋Š” ๋ถ€๋ถ„๋“ค์ด ๊ณตํ†ต์˜ ๊ฒฝ๊ณ„์—์„œ ๋‹ฟ์•„ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ํ‘œํ˜„๋จ์„ ๋ณด์•˜๋‹ค(์œ ์žฌ๋ฏผ, 2014). ์ด๋Ÿฌํ•œ ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค์˜ ์—ฐ์†์˜ ์ •์˜๋Š” ํ•™๋ฌธ์ˆ˜ํ•™์ ์ธ ์ •์˜์™€๋Š” ๋‹ค๋ฅด์ง€๋งŒ, ํ•™์ƒ๋“ค์˜ ์—ฐ์†์˜ ๊ด€๋…๊ณผ ๋งŽ์€ ๋ฉด์—์„œ ๋น„์Šทํ•œ ์–‘์ƒ์„ ๋ณด์˜€๋‹ค. ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค์˜ ์—ฐ์†์— ๋Œ€ํ•œ ๊ณ ์ฐฐ์„ ํ†ตํ•ด ํ•™์ƒ๋“ค๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ฒ ํ•™์ž๋“ค ๊ทธ๋ฆฌ๊ณ  ์šฐ๋ฆฌ ์ผ๋ฐ˜์ธ๋“ค์ด ์ผ์ƒ์ƒํ™œ ์†์˜ ๋˜๋Š” ์ž์—ฐ์˜ ์—ฐ์†์— ๋Œ€ํ•ด ์–ด๋–ค ๊ด€๋…์„ ๊ฐ–๊ณ  ์žˆ๋Š”์ง€ ์‚ดํŽด ๋ณผ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ ๊ทธ๊ฒƒ์ด ํ•™๋ฌธ์ˆ˜ํ•™์˜ ์—ฐ์†๊ณผ ๋‹ค๋ฅธ ์–‘์ƒ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค๋Š” ๊ฒƒ๋„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ•™์ƒ๋“ค์˜ ๊ด€๋…์„ ์˜ค๋ฅ˜๋กœ์„œ๊ฐ€ ์•„๋‹Œ ๋‹ค๋ฅธ ๋ชฉ์ ๊ณผ ํ•„์š”์— ์˜ํ•œ ๊ด€์ ์œผ๋กœ ๋ณด์•„์•ผ ํ•˜๋ฉฐ, ๋™์‹œ์— ๊ต์ˆ˜์™€ ํ•™์Šต์„ ๊ณ„ํšํ•  ๋•Œ ์ด ์ ์„ ์ฃผ์ง€ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์—ฐ์† ๊ฐœ๋…์˜ ์—ญ์‚ฌ์  ๋ฐœ๋‹ฌ๊ณผ์ •๊ณผ ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค์˜ ์ •์˜์— ๋Œ€ํ•œ ๊ณ ์ฐฐ์€ ํ•™๊ต ์ˆ˜ํ•™์—๋„ ๋ช‡ ๊ฐ€์ง€ ์‹œ์‚ฌ์ ์„ ์ฃผ์—ˆ๋‹ค. ์ฒซ์งธ๋กœ ์‚ฐ์ˆ ํ™”๋œ ํ•™๋ฌธ์ˆ˜ํ•™์˜ ์—ฐ์†์˜ ๊ฐœ๋…์€ ์‹œ๊ฐ์ ์ธ ์—ฐ์†์˜ ๊ด€๋…์ด๋‚˜, ์‚ฌ์ „์˜ ์—ฐ์†์˜ ์ •์˜์™€๋„ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ, ์‹œ๊ฐ์  ์ด๋ฏธ์ง€์™€ ๋‹ค๋ฅธ ์—ฐ์†์˜ ํ•™๋ฌธ์  ์ •์˜๋Š” ํ•™์ƒ๋“ค์˜ ๊ฐœ๋…ํ•™์Šต์— ์žฅ์• ๋กœ ์ž‘์šฉํ•  ์ˆ˜๋„ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ํ•จ์ˆ˜์˜ ์—ฐ์†์— ๋Œ€ํ•œ ํ•™์ƒ๋“ค์˜ ๊ด€๋…๊ณผ ํ•™๋ฌธ์ˆ˜ํ•™์˜ ๊ฐœ๋…์€ ์ˆ˜ํ•™์  ์‚ฌ๊ณ ์˜ ์ˆ˜์ค€์—์„œ ์ฐจ์ด๊ฐ€ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์•˜๋‹ค. Tall๊ณผ Katz(2014)์˜ ์ˆ˜ํ•™์  ์‚ฌ๊ณ  ๋ฐœ๋‹ฌ์˜ ์„ธ ๋‹จ๊ณ„๋Š” ๊ฐœ๋…์  ๊ตฌ์ฒดํ™”, proceptual ์ƒ์ง•์ฃผ์˜, ๊ณต๋ฆฌ์  ํ˜•์‹์ฃผ์˜๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ•™์ƒ๋“ค์€ ๊ธฐํ•˜ํ•™์ ์ด๋ฉฐ ์—ฐ๊ฒฐ์„ฑ์— ๊ธฐ์ดˆํ•œ ์—ฐ์†์˜ ๊ฐœ๋…์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ ๊ฐœ๋…์  ๊ตฌ์ฒดํ™”์˜ ์ˆ˜์ค€์— ๋จธ๋ฌผ๋Ÿฌ ์žˆ๋Š” ๋ฐ˜๋ฉด ํ•™๋ฌธ์ˆ˜ํ•™์˜ ์—ฐ์†์˜ ๊ฐœ๋…์€ ์—„๋ฐ€ํ•œ ํ˜•์‹ํ™”์˜ ์ˆ˜์ค€์ด๋‹ค. ๊ฐœ๋…์  ๊ตฌ์ฒดํ™” ์ˆ˜์ค€์˜ ํ•™์ƒ๋“ค์—๊ฒŒ ์„ธ ๋ฒˆ์งธ ์ˆ˜์ค€์˜ ํ˜•์‹์ ์ธ ์ •์˜๋ฅผ ๋ฐ”๋กœ ์ œ์‹œํ•œ๋‹ค๋ฉด ์ดํ•ดํ•˜๊ธฐ ์–ด๋ ค์šธ ๋ฟ ์•„๋‹ˆ๋ผ ์ข‹์€ ํ•™์Šต์ด ์ผ์–ด๋‚˜๊ธฐ๋„ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ํ•™์ƒ๋“ค์˜ ์ธ์ง€ ์ƒํƒœ์™€ ์‚ฌ์ „์— ๊ฐ–๊ณ  ์žˆ๋Š” ์ง€์‹์„ ๊ต์ˆ˜์™€ ํ•™์Šต์ด ์ผ์–ด๋‚˜๊ธฐ ์ „์— ์•Œ์•„๋ณด๊ณ  ๊ทธ๊ฒƒ์„ ์ถœ๋ฐœ์ ์œผ๋กœ ์‚ผ์•„์•ผ ํ•œ๋‹ค.โ… . ์„œ๋ก  ---------------------------------------- 1 1. ์—ฐ๊ตฌ์˜ ๋ชฉ์  ------------------------------ 1 2. ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  ๋ฐ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ ------------- 2 3. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ๋ฐ ์—ฐ๊ตฌ์˜ ๊ธฐ๋Œ€ํšจ๊ณผ ---------------- 6 โ…ก. ํ•™๋ฌธ์ˆ˜ํ•™๊ณผ ํ•™๊ต์ˆ˜ํ•™์˜ ํ•จ์ˆ˜์˜ ์—ฐ์† ------------- 9 1. ํ•™๋ฌธ์ˆ˜ํ•™์—์„œ ํ•จ์ˆ˜์˜ ์—ฐ์† ------------------- 9 2. ํ•™๊ต์ˆ˜ํ•™์—์„œ ํ•จ์ˆ˜์˜ ์—ฐ์† ------------------- 12 2.1 ๊ต์œก๊ณผ์ •์˜ ์—ฐ์†-2007๊ฐœ์ •, 2009๊ฐœ์ • ๊ต์œก๊ณผ์ •์„ ์ค‘์‹ฌ์œผ๋กœ ------------------------------- 12 2.2 ๊ต๊ณผ์„œ์˜ ์—ฐ์† --------------------------- 14 3. ํ•™์ƒ๋“ค์˜ ํ•จ์ˆ˜์˜ ์—ฐ์†์— ๋Œ€ํ•œ ์ธ์‹ ---------- 17 4. ํ•™๋ฌธ์ˆ˜ํ•™๊ณผ ํ•™๊ต์ˆ˜ํ•™์˜ ์ฐจ์ด์— ๋Œ€ํ•œ ๋ถ„์„ ---- 22 โ…ข. ํ•จ์ˆ˜์˜ ์—ฐ์† ๊ฐœ๋…์˜ ์—ญ์‚ฌ์  ๋ถ„์„ --------------- 26 1. ํ•จ์ˆ˜์˜ ์—ฐ์† ๊ฐœ๋…์˜ ์—ญ์‚ฌ์  ๋ฐœ๋‹ฌ๊ณผ์ • -------- 26 1.1 ํ•จ์ˆ˜ ๊ฐœ๋…์˜ ์ถœํ˜„๊ณผ ๋ฐœ๋‹ฌ ---------------- 26 1.2 ํ•จ์ˆ˜์˜ ์—ฐ์† ๊ฐœ๋…์˜ ์ฒด๊ณ„ํ™” --------------- 31 1.3 Weierstrass์˜ ๋ฐฉ๋ฒ• ----------------- 33 2. ์‚ฐ์ˆ ํ™”์˜ ๊ด€์ ์—์„œ ๋ณ€ํ™” ๋ถ„์„ ---------------- 35 2.1 ๊ณต๊ฐ„์  ์ง๊ด€์œผ๋กœ๋ถ€ํ„ฐ ํƒˆํ”ผ ---------------- 35 2.2 ์ง‘ํ•ฉ๋ก ์˜ ์˜ํ–ฅ -------------------------- 38 2.3 ํ˜๋ช…์  ๋ฐœ๋‹ฌ๋กœ์„œ์˜ ์‚ฐ์ˆ ํ™” --------------- 41 โ…ฃ. ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค์˜ ์—ฐ์† ๊ฐœ๋… ๋ถ„์„ -------------- 46 1. ๋ฌดํ•œ ๋ถ„ํ•  ๊ฐ€๋Šฅ ----------------------- 46 2. ๋ถ„ํ•  ๋ถˆ๊ฐ€๋Šฅํ•œ ํ•˜๋‚˜์˜ ์ „์ฒด -------------- 48 3. ์ˆ˜ํ•™์  ๊ฐœ๋…๊ณผ ๋น„๊ต ------------------------ 53 3.1 ์—ฐ์†์ฒด์™€ ๋น„๊ต -------------------------- 53 3.2 ์—ฐ์†ํ•จ์ˆ˜์™€ ๋น„๊ต ------------------------ 55 3.3 ํ•™์ƒ๋“ค์˜ ์—ฐ์†์˜ ๊ด€๋…๊ณผ ๋น„๊ต ------------ 61 4. ๋ถ„์„์˜ ๊ฒฐ๊ณผ ------------------------------- 63 โ…ค. ํ•™๊ต์ˆ˜ํ•™์—์˜ ์‹œ์‚ฌ์  ------------------------- 67 1. ์‹œ๊ฐํ™” ๋ฐ ์‚ฌ์ „์  ์ •์˜์˜ ๊ด€์  -------------- 67 2. ์ˆ˜ํ•™์  ์‚ฌ๊ณ  ๋ฐœ๋‹ฌ ๋‹จ๊ณ„ --------------------- 70 โ…ฅ. ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  -------------------------------- 73 ์ฐธ๊ณ  ๋ฌธํ—Œ -------------------------------------- 80Maste

    ๋ฌดํ•œ์†Œ ๋‹ด๋ก ๊ณผ ๊ทนํ•œ ๋‹ด๋ก ์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์ˆ˜ํ•™๊ต์œก๊ณผ, 2022. 8. ์ตœ์˜๊ธฐ.์„œ๋กœ ๋‹ค๋ฅธ ๋ฐฐ๊ฒฝ์ง€์‹์„ ๊ฐ€์ง„ ์‚ฌ๋žŒ๋“ค์˜ ์˜์‚ฌ์†Œํ†ต์ด ํšจ๊ณผ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š๋Š” ๊ฒฝ์šฐ๊ฐ€ ์กด์žฌํ•œ๋‹ค. ๊ณผํ•™๋ถ„์•ผ์—์„œ๋Š” ๋‹ค๋ฅธ ์ด๋ก ์œผ๋กœ ์‚ฌ๊ณ ํ•˜๋Š” ์‚ฌ๋žŒ๋“ค์ด ๋™์ผํ•œ ์–ธ์–ด๋ฅผ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ์ง€๋งŒ ์„œ๋กœ๋ฅผ ์ดํ•ดํ•˜์ง€ ๋ชปํ•˜๋Š” ํ˜„์ƒ์„ ํŒจ๋Ÿฌ๋‹ค์ž„์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์œผ๋กœ ์„ค๋ช…ํ•œ๋‹ค. ์ด์™€ ์œ ์‚ฌํ•œ ๋ชจ์Šต์„ ์ˆ˜ํ•™๊ต์‹ค์—์„œ๋„ ์–ด๋ ต์ง€ ์•Š๊ฒŒ ์ฐพ์•„๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋•Œ๋•Œ๋กœ ํ•™๊ตํ˜„์žฅ์—์„œ ๊ต์‚ฌ์™€ ํ•™์ƒ์€ ๋™์ผํ•œ ์ฃผ์ œ์— ๋Œ€ํ•ด ๋งํ•˜๊ณ  ์žˆ์ง€๋งŒ ์„œ๋กœ์˜ ๊ด€์ ์„ ์ดํ•ดํ•˜์ง€ ๋ชปํ•˜๋ฉฐ ์˜์‚ฌ์†Œํ†ต์ด ํšจ๊ณผ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š๋Š” ํ˜„์ƒ์ด ๋ฐœ์ƒํ•œ๋‹ค. ๋งŒ์•ฝ ์ˆ˜ํ•™๊ต์‹ค์—์„œ ์˜์‚ฌ์†Œํ†ต์ด ์ ์ ˆํžˆ ์ด๋ฃจ์–ด์ง€์ง€ ๋ชปํ•œ๋‹ค๋ฉด ์ˆ˜ํ•™ํ•™์Šต์ด ํšจ๊ณผ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๊ธฐ ์–ด๋ ต๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์ˆ˜ํ•™๊ต์œก ํ˜„์žฅ์˜ ์ด๋Ÿฌํ•œ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ ํ˜„์ƒ์„ ์ดํ•ดํ•˜๋Š” ๋ชฉ์ ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋จผ์ € ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด ์ด๋ก ์  ํƒ์ƒ‰์„ ํ•˜์˜€๋‹ค. ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์ด๋ผ๋Š” ์šฉ์–ด์˜ ๊ธฐ์›์ด ๋˜๋Š” ๊ทธ๋ฆฌ์Šค ์ˆ˜ํ•™์˜ โ€˜๊ณตํ†ต ๋‹จ์œ„โ€™๋กœ ์žด ์ˆ˜ ์—†๋Š” ์–‘์ด ์กด์žฌํ•œ๋‹ค๋Š” ๋ฐœ๊ฒฌ์œผ๋กœ๋ถ€ํ„ฐ, ์ฟค์˜ ๊ณผํ•™ํ˜๋ช… ์ด๋ก ์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ ๊ฐœ๋…, ๊ทธ๋ฆฌ๊ณ  ์ˆ˜ํ•™๊ต์œก์˜ ๋‹ด๋ก ์  ๊ด€์ ์—์„œ ์‚ฌ์šฉ๋˜๋Š” ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์˜๋ฏธ๋ฅผ ์„ ํ–‰์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ฒ€ํ† ํ•˜์˜€๋‹ค. ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์ˆ˜ํ•™์‚ฌ์  ๋ถ„์„ ๊ฒฐ๊ณผ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅํ•œ ์–‘์˜ ๋ฐœ๊ฒฌ์€ ๋‹จ์ง€ ๊ธฐ์กด์— ์•Œ์ง€ ๋ชปํ–ˆ๋˜ ์–‘์˜ ์กด์žฌ์„ฑ ๋ฐœ๊ฒฌ ์ด์ƒ์˜, ๊ทธ๋ฆฌ์Šค ์ˆ˜ํ•™ ์ „๋ฐ˜์— ๊ทผ๋ณธ์ ์ธ ๋ณ€ํ™”๋ฅผ ๊ฐ€์ ธ์˜จ ์ค‘๋Œ€ํ•œ ์‚ฌ๊ฑด์ด์—ˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ์ฟค์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ ๊ฐœ๋…์— ๋Œ€ํ•ด ์ด๋ก ์  ๊ณ ์ฐฐ์„ ํ•˜์˜€๋‹ค. ์ฟค์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์€ ์ดˆ๊ธฐ ๋ฐฉ๋ฒ•๋ก ์  ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ, ์˜๋ฏธ๋ก ์  ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ, ๊ด€์ฐฐ์  โ€ง ์กด์žฌ๋ก ์  ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์„ธ ๊ฐ€์ง€ ์ธก๋ฉด์ด ์žˆ์—ˆ์ง€๋งŒ ํ›„๊ธฐ์—๋Š” ํŒจ๋Ÿฌ๋‹ค์ž„ ์‚ฌ์ด์˜ ๋ถ„๋ฅ˜์ฒด๊ณ„์˜ ์ „ํ™˜์ด๋ผ๋Š” ์˜๋ฏธ๋ก ์  ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์— ์ง‘์ค‘๋˜์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋Ÿฌํ•œ ์ฟค์˜ ๋ถ„๋ฅ˜ํ•™์  ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์€ ์กด์žฌ๋ก ์  ์ „ํ™˜์œผ๋กœ์„œ ์ผ๋ฐ˜ํ™”๋  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ตœ๊ทผ ์ˆ˜ํ•™๊ต์œก์˜ ๋‹ด๋ก ์  ์ ‘๊ทผ์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•œ ์ด๋ก ์  ๋…ผ์˜๋“ค์„ ๊ฒ€ํ† ํ•˜์˜€๋‹ค. ์ด๋ก ์  ํƒ์ƒ‰์˜ ๊ฒฐ๊ณผ ๋‹ด๋ก  ์‚ฌ์ด์— ๋ฉ”ํƒ€๊ทœ์น™์ด ๋ณ€ํ™”ํ•  ๋•Œ์™€ ์ˆ˜ํ•™์  ๋Œ€์ƒ์˜ ์กด์žฌ๋ก ์  ์ „ํ™˜์ด ์ผ์–ด๋‚  ๋•Œ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์ด ๋ฐœ์ƒํ•จ์„ ํ™•์ธํ•˜๊ณ , ์ด๋ฅผ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ ํ˜„์ƒ ๋ถ„์„์— ์‚ฌ์šฉํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์ด๋ก ์  ํƒ์ƒ‰์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•™์ƒ๋“ค์˜ ๋ฌดํ•œ์†Œ ๋‹ด๋ก ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ํ•™์ƒ๋“ค์ด ๊ทนํ•œ ๋‹ด๋ก ์˜ ๊ต์œก๊ณผ์ •์„ ํ•™์Šตํ•˜์ง€๋งŒ, ์—ฌ์ „ํžˆ ๋ฌดํ•œ์†Œ ๊ด€์ ์„ ๊ฐ–๋Š” ํ˜„์ƒ๋“ค์ด ๋ณด๊ณ ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋™์•ˆ ์ˆ˜ํ•™๊ต์œก ์—ฐ๊ตฌ๋“ค์€ ํ•™์ƒ๋“ค์ด ์–ด๋–ค ์ˆ˜ํ•™์  ๋‚ด์šฉ๊ณผ ๊ด€๋ จํ•˜์—ฌ ๋ฌดํ•œ์†Œ ๊ด€์ ์„ ๊ฐ–๋Š”์ง€, ๊ทธ๋ฆฌ๊ณ  ๋ฌดํ•œ์†Œ ์‚ฌ๊ณ ๊ฐ€ ์–ด๋–ค ์–‘์ƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š”์ง€๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์™”๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•™์ƒ๋“ค์ด ๊ทนํ•œ ๋‹ด๋ก ์˜ ๊ต์‚ฌ์™€ ์˜์‚ฌ์†Œํ†ตํ•˜๊ณ , ๊ทนํ•œ ๋‹ด๋ก ์˜ ๊ต๊ณผ์„œ๋ฅผ ๊ณต๋ถ€ํ•˜๋ฉด์„œ๋„ ๋ฌดํ•œ์†Œ ๋‹ด๋ก ์„ ๊ฐ€์ง„ ํ˜„์ƒ์ด ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ๊ณผ ๊ด€๋ จ์ด ์žˆ๋‹ค๊ณ  ๋ณด๊ณ  ์ด๋ฅผ ๋ถ„์„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์šฐ์„  ํ•™์ƒ๋“ค์˜ ๋ฌดํ•œ์†Œ ๊ด€์ ์˜ ๊ธฐ์›์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜ํ•™์‚ฌ์—์„œ ๋ฌดํ•œ์†Œ์™€ ๊ด€๋ จ ์žˆ๋Š” ์—ฐ์†์ฒด์˜ ๊ตฌ์„ฑ๊ณผ ๋ฌดํ•œ๋ถ„ํ•  ๋ฐ ๊ทนํ•œ ๊ฐœ๋…์˜ ์—ญ์‚ฌ๋ฅผ ์•Œ์•„๋ณด์•˜๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ˆ˜ํ•™์‚ฌ์—์„œ ๋ฌดํ•œ์†Œ ๊ฐœ๋…์ด ๋‚˜ํƒ€๋‚œ ์–‘์ƒ๊ณผ ๋งฅ๋ฝ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ทธ๋ฆฌ์Šค ์‹œ๋Œ€๋ถ€ํ„ฐ ์ˆ˜ํ•™์ž๋“ค์—๊ฒŒ๋„ ๊พธ์ค€ํžˆ ๋‚˜ํƒ€๋‚ฌ๋˜ ๋ฌดํ•œ์†Œ ๊ฐœ๋…์ด ํ˜„๋Œ€ ํ•™๋ฌธ์ˆ˜ํ•™์˜ ์ •๋ฆฌ๋“ค๋กœ ์ธํ•ด ์ œ์™ธ๋˜์—ˆ๋‹ค๋Š” ์‚ฌ์‹ค๋กœ๋ถ€ํ„ฐ, ๋ฌดํ•œ์†Œ ๊ด€์ ์ด ํ•™์ƒ๋“ค์—๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์€ ์ž์—ฐ์Šค๋Ÿฌ์šฐ๋ฉฐ ๋˜ํ•œ ๊ทน๋ณตํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฐœ๋…์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์ด๋ก ์  ํƒ์ƒ‰ ๊ฒฐ๊ณผ๋ฅผ ์ ์šฉํ•˜์—ฌ ๋ฌดํ•œ์†Œ ๋‹ด๋ก ๊ณผ ๊ทนํ•œ ๋‹ด๋ก ์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ˆ˜ํ•™์  ๋Œ€์ƒ์˜ ์กด์žฌ๋ก ์  ๋ณ€ํ™”์™€ ๋ฉ”ํƒ€๊ทœ์น™์„ ์ค‘์‹ฌ์œผ๋กœ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ ๋ฌดํ•œ์†Œ ๋‹ด๋ก ๊ณผ ๊ทนํ•œ ๋‹ด๋ก ์€ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ˆ˜ํ•™๊ต์œก ํ˜„์žฅ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋ฌดํ•œ์†Œ ๋‹ด๋ก ๊ณผ ๊ทนํ•œ ๋‹ด๋ก ์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ ์‚ฌ๋ก€๋ฅผ ๋‘ ๊ฐ€์ง€ ์ œ์‹œํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ๋Š” ์˜ˆ๋น„๊ต์‚ฌ์™€ ๊ณ ๋“ฑํ•™์ƒ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์ด๋“ค์ด ๋ฌดํ•œ์†Œ ๊ด€์ ์„ ๊ฐ€์กŒ๋Š”์ง€ ์—ฌ๋ถ€์™€ ๊ทธ๋“ค์ด ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๊ตฌ์ฒด์  ๋‹ด๋ก  ์–‘์ƒ ๋ฐ ๊ทธ๋“ค์˜ ๋‹ด๋ก ์ด ๊ทนํ•œ ๋‹ด๋ก ๊ณผ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅํ•œ์ง€๋ฅผ ์‚ฌ๋ก€์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ํ•ด์„ํ•™์„ ํ•™์Šตํ•œ ๊ฒฝํ—˜์ด ์žˆ๋Š” ์ผ๋ถ€ ์˜ˆ๋น„๊ต์‚ฌ๋“ค์€ ๋ฌดํ•œ์†Œ ๊ด€์ ์„ ๊ฐ–๊ณ  ์žˆ์—ˆ๋‹ค. ์ผ๋ถ€ ์˜ˆ๋น„๊ต์‚ฌ๋“ค์˜ ๋ฉ”ํƒ€๊ทœ์น™์€ ํ•ด์„ํ•™์˜ ๋ฉ”ํƒ€๊ทœ์น™ ๋‹ฌ๋ž์œผ๋ฉฐ, ๋ฌดํ•œ์†Œ์™€ ๋ฌดํ•œํžˆ ํฐ ์ˆ˜๊ฐ€ ์กด์žฌํ•œ๋‹ค๋Š” ๊ด€์ ์„ ๊ฐ–๊ณ  ์žˆ์—ˆ์œผ๋ฏ€๋กœ ์ด๋“ค์€ ๊ทนํ•œ ๋‹ด๋ก ์œผ๋กœ ์ „ํ™˜ํ•˜์ง€ ๋ชปํ•˜๊ณ  ๋ฌดํ•œ์†Œ ๋‹ด๋ก ์— ๋จธ๋ฌผ๋Ÿฌ ์žˆ์Œ์„ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ณ ๋“ฑํ•™์ƒ๋“ค์˜ ๊ฒฝ์šฐ ์ผ๋ถ€ ํ•™์ƒ๋“ค์ด ๋ฌดํ•œ์†Œ์™€ ๋ฌดํ•œํžˆ ํฐ ์ˆ˜๊ฐ€ ์กด์žฌํ•œ๋‹ค๋Š” ์ธ์‹์„ ๋ณด์˜€๊ณ  ์ด์™€ ๊ด€๋ จ๋œ ๊ตฌ์ฒด์  ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ๋“ค์„ ํ™•์ธํ•˜์˜€๋‹ค. ํŠนํžˆ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ผ๋ถ€ ํ•™์ƒ๋“ค์ด ํ•™๊ต์ˆ˜ํ•™์˜ ๋ฉ”ํƒ€๊ทœ์น™์— ๋Œ€ํ•ด ์˜๋ฌธ์„ ํ’ˆ๊ณ  ์žˆ์—ˆ์œผ๋ฉฐ, ํ•™๊ต์ˆ˜ํ•™์˜ ๋ฉ”ํƒ€๊ทœ์น™์€ ์ž ์ •์ ์ด๊ณ  ์ผ์‹œ์ ์ด๋ผ๊ณ  ์ƒ๊ฐํ•˜๊ณ  ์žˆ์Œ์„ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์ด๋ก ์  ํƒ์ƒ‰์„ ํ†ตํ•ด ๋ฉ”ํƒ€๊ทœ์น™์˜ ๋ณ€ํ™”์™€ ์ˆ˜ํ•™์  ๋Œ€์ƒ์˜ ์กด์žฌ๋ก ์  ์ „ํ™˜์ด ๋‹ด๋ก ์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์š”์†Œ์ž„์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์˜ˆ๋น„๊ต์‚ฌ์™€ ํ•™์ƒ๋“ค์—๊ฒŒ์„œ ๋ณด์ธ ๋ฌดํ•œ์†Œ ์‚ฌ๊ณ ๋ฅผ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ๊ด€์ ์—์„œ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์ด ์ˆ˜ํ•™๊ต์œก์˜ ๋‹ค์–‘ํ•œ ํ˜„์ƒ๊ณผ ๋ฌธ์ œ๋ฅผ ๋ถ„์„ํ•˜๋Š” ๋„๊ตฌ๋กœ์„œ ๊ธฐ๋Šฅํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.It is difficult to communicate effectively between people with different theories. Science explains this phenomenon as incommensurability between paradigms. Even if people with different paradigms seem to speak the same language, they do not understand each other and it is difficult to communicate. A similar situation occurs in mathematics classrooms. Sometimes in classrooms, although a teacher and students are talking about the same subject, they do not understand each other. If communication is not done properly in a mathematics classroom, it is difficult to learn mathematics effectively. Therefore, The purpose of this study is to understand the phenomenon of incommensurable discourses in mathematics education. This study explored incommensurability theoretically. First this study identified the context and implications of the Greek discovery that there are quantities that cannot be measured in common measure, which is the origin of the term incommensurability. As a result of the historical analysis of incommensurability, it was identified that the discovery of the incommensurable quantity was a significant event that brought fundamental changes in Greek mathematics, beyond the discovery that there was a previously unknown quantity. Second, a theoretical study was conducted on Kuhn's concept of incommensurability. Kuhn's incommensurability had three aspects which are methodological incommensurability, semantic incommensurability, and observational-ontological incommensurability. However, in the later period, the focus was on the semantic incommensurability between paradigms, which was characterized by a taxonomic shift. It was also identified that this can be generalized to ontological transformation. Third, the meaning of incommensurability used from the discursive approach to mathematics education was reviewed. In this perspective, the transition between incommensurable discourses is essential for a qualitative leap. In mathematics learning, it was identified that the change of a mathematical object and the change of the meta-rule bring a leap in the level. As a result of the theoretical investigation, it was identified that the incommensurable phenomenon occurs when meta-rules change and when the ontological transformation of mathematical objects occurs between discourses, and this was used for the analysis of the incommensurable discourses. Based on the theoretical analysis of incommensurability, the students' discourse on infinitesimal was examined. Although students learn the curriculum of limit discourse, it is reported that they still have an infinitesimal discourse. The research on the students' infinitesimal thinking was focused on in what context the students showed the infinitesimal perspective and what form it appeared in. This study focused on the fact that students have infinitesimal discourse while they communicate with teachers of limit discourse and study textbooks of limit discourse. Therefore, this study explored the phenomenon focusing on the incommensurability of discourses. First, to understand the origin of the students' thought on infinitesimal, this study explored the composition and infinite division of the continuum and limit concept in the history of mathematics. Through this, it was identified how the concept of infinitesimal appeared in history. Also, from the fact that the concept of infinitesimal, which had been steadily appearing to mathematicians since the Greek era, was excluded due to analysis in the 19th and 20th centuries, it was identified that it is natural for students to have the infinitesimal conception and the conception of infinitesimal is difficult to overcome. Next, the relationship between infinitesimal discourse and limit discourse was analyzed focusing on the ontological transformations of mathematical objects and meta-rules. As a result, it was shown that the infinitesimal discourse and the limit discourse are incommensurable. In addition, two cases of incommensurability of the infinitesimal discourse and the limit discourse appearing in mathematics education were presented. Finally, this study tried to empirically examine whether pre-service teachers and high school students have an infinitesimal conception. Even though they had already learned analysis, some pre-service teachers stayed in the infinitesimal discourse. In this case, the meta-rules of pre-service teachers were different from those of analysis. Also, since some pre-service teachers had the view that infinitesimals and infinitely large numbers exist, it was shown that they could not transfer to the limit discourse and remained in the infinitesimal discourse. In the case of high school students, some students also showed the thought that infinitesimals and infinitely large numbers exist, and specific narratives were identified. In particular, this study showed that the students had doubts about the meta-rules of school mathematics and thought that the meta-rules of school mathematics were a provisional and temporary convenient method for teaching. This study showed that changes of meta-rules and ontological transformation of mathematical objects are factors of incommensurability between discourses through theoretical exploration of incommensurability. In addition, this study showed incommensurability can be used as a tool to analyze the phenomenon of mathematics education.โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ ๋ฌธ์ œ 3 3. ์šฉ์–ด ์ •์˜ 4 โ…ก. ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์ด๋ก ์  ํƒ์ƒ‰ 7 1. ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์ˆ˜ํ•™์‚ฌ์  ์˜์˜ 8 1.1. ๊ทธ๋ฆฌ์Šค ์‹œ๋Œ€ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅํ•œ ์–‘์˜ ๋ฐœ๊ฒฌ 8 1.2. ์ƒˆ๋กœ์šด ๋น„ ์ด๋ก ๊ณผ ๋ฌดํ•œ๊ณผ์ •์— ๋Œ€ํ•œ ์ •๋ฆฌ ๋“ฑ์žฅ 18 1.3. ๊ฒฝํ—˜์  ์ˆ˜ํ•™์—์„œ ์ด๋ก ์  ์ˆ˜ํ•™์œผ๋กœ์˜ ์ „ํ™˜ 21 1.4. ์ˆ˜ํ•™์  ๋Œ€์ƒ์˜ ์กด์žฌ์„ฑ์— ๋Œ€ํ•œ ์งˆ๋ฌธ์˜ ์ค‘์š”์„ฑ ์ธ์‹ 23 1.5. ๊ฒฐ๋ก  26 2. ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ์˜ ์ฒ ํ•™์  ๋…ผ์˜ 28 2.1. ์ฟค์˜ ํŒจ๋Ÿฌ๋‹ค์ž„ ์ด๋ก ์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ 28 2.2. ์กด์žฌ๋ก ์  ๋ฒ”์ฃผ์™€ ๊ณผํ•™์˜ ๊ฐœ๋…๋ณ€ํ™” 34 3. ์ˆ˜ํ•™ํ•™์Šต์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ 38 3.1. ์ˆ˜ํ•™ํ•™์Šต์˜ ๋‹ด๋ก ์  ์ ‘๊ทผ 38 3.2. ๋‹ด๋ก ์  ์ ‘๊ทผ์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ 41 3.2. ์ˆ˜ํ•™์  ๊ฐœ๋…์˜ ์กด์žฌ๋ก ์  ๋ณ€ํ™” 45 3.3. ๋ฉ”ํƒ€๊ทœ์น™์˜ ๋ณ€ํ™” 57 4. ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ ๋…ผ์˜์˜ ๊ฒฐ๋ก  62 โ…ข. ๋ฌดํ•œ์†Œ ๋‹ด๋ก ๊ณผ ๊ทนํ•œ ๋‹ด๋ก ์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ 66 1. ๋ฌดํ•œ์†Œ์˜ ์ˆ˜ํ•™์‚ฌ์  ๋ถ„์„ 66 1.1. ์—ฐ์†์ฒด์˜ ๊ตฌ์„ฑ๊ณผ ๋ฌดํ•œ๋ถ„ํ• ์˜ ์ดํ•ด 67 1.2. ๊ทนํ•œ๊ณผ ์ˆœ๊ฐ„๋ณ€ํ™”์œจ์˜ ์ดํ•ด 77 1.3. ๋…ผ์˜ 84 2. ๋ฌดํ•œ์†Œ ๋‹ด๋ก ๊ณผ ๊ทนํ•œ ๋‹ด๋ก ์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ 86 3. ์ˆ˜ํ•™ํ•™์Šต์˜ ๊ณต์•ฝ๋ถˆ๊ฐ€๋Šฅ์„ฑ ์‚ฌ๋ก€ 89 3.1. '์ œ๋…ผ์˜ ์—ญ์„ค'์˜ ์ดํ•ด 89 3.2. ์ˆœ๊ฐ„์†๋„์˜ ์ดํ•ด 98 โ…ฃ. ์˜ˆ๋น„๊ต์‚ฌ ๋ฐ ๊ณ ๋“ฑํ•™์ƒ๋“ค์˜ ๋‹ด๋ก  ๋ถ„์„ 102 1. ์˜ˆ๋น„๊ต์‚ฌ๋“ค์˜ ๋‹ด๋ก  ๋ถ„์„ 102 1.1. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 103 1.2. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 105 1.3. ๋…ผ์˜ 117 2. ๊ณ ๋“ฑํ•™์ƒ๋“ค์˜ ๋‹ด๋ก  ๋ถ„์„ 121 2.1. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 121 2.2. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 124 2.3. ๋…ผ์˜ 152 โ…ค. ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  156 1. ์š”์•ฝ 156 2. ๊ฒฐ๋ก  162 ์ฐธ๊ณ ๋ฌธํ—Œ 166 Abstract 183๋ฐ•

    A Study on Development of Anti-Money Laundering System through Homomorphic Encryption and AI Analysis

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์ˆ˜๋ฆฌ์ •๋ณด๊ณผํ•™๊ณผ, 2021. 2. ์ฒœ์ •ํฌ.์š”์•ฝ(๊ตญ๋ฌธ์ดˆ๋ก) ํ˜„ํ–‰ ์ž๊ธˆ์„ธํƒ๋ฐฉ์ง€ ์ œ๋„์— ๋”ฐ๋ฅด๋ฉด, FIU๋Š” ์˜์‹ฌ๊ฑฐ๋ž˜๋ฅผ ๋ถ„์„ํ•˜์—ฌ ๊ฒ€์ฐฐ์— ์ˆ˜์‚ฌ์˜๋ขฐ(FIU ์ด์ฒฉ ์‚ฌ๊ฑด)๋ฅผ ํ•  ์ˆ˜ ์žˆ์œผ๋‚˜, ๊ฒ€์ฐฐ์— ๋Œ€ํ•œ ์ด๋Ÿฌํ•œ ์˜์‹ฌ๊ฑฐ๋ž˜ ๊ด€๋ จ ํŠน์ •๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด ์ œ๊ณต ๋ฒ”์œ„๊ฐ€ ์ง€๋‚˜์น˜๊ฒŒ ํ˜‘์†Œํ•˜์—ฌ FIU ์ด์ฒฉ ์‚ฌ๊ฑด ์ˆ˜์‚ฌ ๋“ฑ์— ์žˆ์–ด ํšจ์œจ์„ฑ์ด ๋–จ์–ด์ง„๋‹ค. ํ˜„ํ–‰ ์ œ๋„์— ๋”ฐ๋ฅด๋ฉด, FIU์—์„œ ํ™•์ธํ•œ ์ •๋ณด๋ฅผ ๊ฒ€์ฐฐ ์ˆ˜์‚ฌ์—์„œ ์ค‘๋ณต์ ์œผ๋กœ ๋‹ค์‹œ ํ™•์ธํ•˜์—ฌ์•ผ ํ•˜๊ณ , ์ž๊ธˆ์„ธํƒ๋ฒ”์ฃ„์ธ์ง€ ์—ฌ๋ถ€ ํ™•์ธ์„ ์œ„ํ•ด ๊ธˆ์œต๊ณ„์ขŒ์ถ”์ ์šฉ ์˜์žฅ์„ ๋ฐœ๋ถ€๋ฐ›๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ž๊ธˆ์„ธํƒ๋ฒ”์ฃ„ ํ˜์˜์— ๋Œ€ํ•œ ์†Œ๋ช…์ด ํ•„์š”ํ•˜๊ฒŒ ๋œ๋‹ค. ๊ทธ๋Ÿฐ๋ฐ, ์ž๊ธˆ์„ธํƒ๋ฒ”์ฃ„์— ํ•ด๋‹นํ•˜๋Š”์ง€ ์—ฌ๋ถ€๋Š” ๋งŽ์€ ์–‘์˜ ๊ธˆ์œต๊ฑฐ๋ž˜ ์ •๋ณด๋ฅผ ๋ถ„์„ํ•ด์•ผ๋งŒ ์•Œ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ๋ชจ์ˆœ์ผ ์ˆ˜๋ฐ–์— ์—†๋‹ค. ๋˜ํ•œ, ์˜์žฅ์„ ๋ฐœ๋ถ€๋ฐ›์ง€ ๋ชปํ•œ ์‚ฌ๊ฑด์€ ๋” ์ด์ƒ ์ˆ˜์‚ฌ๊ฐ€ ์ง„ํ–‰๋  ์ˆ˜ ์—†๊ฒŒ ๋˜์–ด ๋Œ€๋ถ€๋ถ„์˜ ์‚ฌ๊ฑด๋“ค์€ ์‚ฌ์žฅ๋˜๊ฒŒ ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ œ๋„ ์šด์šฉ ํ•˜์—์„œ๋Š” ํšจ์œจ์ ์ธ ์ž๊ธˆ์„ธํƒ๋ฐฉ์ง€๋Š” ์‚ฌ์‹ค์ƒ ๋ถˆ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋˜๋ฏ€๋กœ, ์œ„์™€ ๊ฐ™์€ ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” FIU์—์„œ ์ œ๊ณตํ•˜๋Š” ์ •๋ณด์˜ ๋ฒ”์œ„๋ฅผ ๋Œ€ํญ ํ™•๋Œ€ํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฝ์šฐ ๋ฐœ์ƒํ•˜๋Š” ๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์˜ ๋ฌธ์ œ์— ๋Œ€ํ•ด์„œ๋Š” ๋™ํ˜•์•”ํ˜ธ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ, FIU์˜ ๋ถ„์„ ๊ธฐ๋Šฅ์„ ๊ฐ•ํ™”ํ•˜์—ฌ ๊ธˆ์œต๊ธฐ๊ด€ ๋“ฑ์˜ ์˜์‹ฌ๊ฑฐ๋ž˜ ๋ณด๊ณ  ์‚ฌ๊ฑด ์ค‘ ์ž๊ธˆ์„ธํƒ๋ฒ”์ฃ„์˜ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ๊ฑฐ๋ž˜๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์„ ๋ณ„ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์—ฌ์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„์„ ๊ธฐ๋Šฅ ๊ฐ•ํ™”๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋™ํ˜•์•”ํ˜ธ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ AI๋ถ„์„(๋™ํ˜•๊ธฐ๊ณ„ํ•™์Šต) ๊ธฐ๋ฒ•์ด ๋„์ž…๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋‚˜์•„๊ฐ€, FIU์˜ ๋ถ„์„ ๊ธฐ๋Šฅ ๊ฐ•ํ™”์˜ ์ผํ™˜์œผ๋กœ ๊ฒ€์ฐฐ์—์„œ ์ˆ˜์‚ฌ๋ฅผ ํ†ตํ•ด ํš๋“ํ•œ ๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด์— ๋Œ€ํ•ด์„œ๋Š” FIU์™€ ๊ณต์œ ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์—ฌ FIU์˜ ๋ถ„์„์— ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ์ด๋•Œ์—๋„ ๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์˜ ๋ฌธ์ œ๋Š” ๋™ํ˜•์•”ํ˜ธ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํ•ด๊ฒฐ์ด ๊ฐ€๋Šฅํ•  ๊ฒƒ์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ด๋Ÿฌํ•œ ์ž๊ธˆ์„ธํƒ๋ฐฉ์ง€ ์ œ๋„ ๊ฐœ์„ ์— ํ•„์š”ํ•œ ํŠน์ • ๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด์˜ ๋ณด๊ณ  ๋ฐ ์ด์šฉ ๋“ฑ์— ๊ด€ํ•œ ๋ฒ•๋ฅ  ๊ฐœ์ •๋„ ๋ณ‘ํ–‰๋˜์–ด์•ผ ํ•  ๊ฒƒ์œผ๋กœ ๋ณธ๋‹ค. ์ฃผ์š”์–ด : ์ž๊ธˆ์„ธํƒ, ๋™ํ˜•์•”ํ˜ธ, ๋™ํ˜•๊ธฐ๊ณ„ํ•™์Šต, AI, ๊ธฐ๊ณ„ํ•™์Šต, ๋จธ์‹ ๋Ÿฌ๋‹, ํŠน์ •๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด, FIU ํ•™ ๋ฒˆ : 2019-24460 A Study on Development of Anti-Money Laundering System through Homomorphic Encryption and AI Analysis Beik Seung Joo According to Korean Anti-Money Laundering System, The KFIU may send some information, so called STR and CTR to the Prosecution Service. The system is, however, very inefficient due to the far narrow range of information. The Prosecution, sometimes conduct a needless investigation process to get specific information which the KFIU has got already but which does not provide to the Prosecution. Ironically, Even if the Prosecution has to work a lot to get the search and seizure warrant in order to get more information for the analysis. the Court will not issue the warrant because the Prosecution could not prove the probability of the crime. The proof is impossible before the Prosecution get a wide range of financial information. To solve this dilemma, KFIU should provide sufficient information enough to decide to start criminal investigation after the analysis. This solution raises Privacy issue. So we can adopt homomorphic encryption(HE) scheme for Data and Information Protection, especially fully homomorphic encryption model(FHE). Also, the KFIU has to strengthen its capability to analyse big data for the highly efficiency of financial data analysis. To meet this goal, we can choose a machine learning model with FHE. At a glance, it can be said that this approach is very dangerous because it seems to avoid the limit and control of the court through warrant over the Prosecution. However, this approach applies only to money laundering which is very rare compared with other criminal cases. The exception of the warrant system, Moreover, the information which the Prosection has gathered in criminal cases should be shared with KFIU for machine learning- based analysis. Lastly, We should solve some legal issues and to consider the amendment of the related law such as the Act on Reporting and Use of Certain Financial Transaction Information. Keyword : Money Laundering, Homomorphic Encryption(HE), Machine Learning, AI, Certain Financial Transaction Information, FIU Student Number : 2019-24460๋ชฉ ์ฐจ ์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 2 ์ œ 2 ์žฅ ํ˜„ํ–‰ ์ž๊ธˆ์„ธํƒ ๋ฐฉ์ง€ ์ œ๋„ ๊ฐœ๊ด€ 3 ์ œ 1 ์ ˆ ์ž๊ธˆ์„ธํƒ์˜ ์˜์˜ 3 ์ œ 2 ์ ˆ ์ž๊ธˆ์„ธํƒ ๋ฐฉ์ง€ ์ œ๋„์˜ ์ฒด๊ณ„ 6 1. ์„œ์„ค 6 2. ์ž๊ธˆ์„ธํƒ์˜ ๋ฒ”์ฃ„ํ™” 7 3. ์˜์‹ฌ๊ฑฐ๋ž˜ ๋ณด๊ณ (STR) ์ œ๋„ 9 4. ๊ณ ์•ก ํ˜„๊ธˆ๊ฑฐ๋ž˜ ๋ณด๊ณ (CTR) ์ œ๋„ 10 5. ๊ณ ๊ฐํ™•์ธ ์ œ๋„ 11 ์ œ 3 ์ ˆ ๊ธˆ์œต์ •๋ณด ๋ถ„์„ ๊ธฐ๊ตฌ: ๊ธˆ์œต์ •๋ณด๋ถ„์„์›(FIU) 12 1. FIU์˜ ์˜์˜ ๋ฐ ์ฃผ์š” ์ž„๋ฌด 12 2. ์˜์‹ฌ๊ฑฐ๋ž˜๊ณ ์•ก ํ˜„๊ธˆ๊ฑฐ๋ž˜ ๋ณด๊ณ ์™€ ๋ฒ•์ง‘ํ–‰๊ธฐ๊ด€ ์ •๋ณด์ œ๊ณต 13 3. ํŠน์ •๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด ์‹ฌ์‚ฌ๋ถ„์„ ์ ˆ์ฐจ 16 4. FIU๊ฐ€ ์ž…์ˆ˜ ๊ฐ€๋Šฅํ•œ ์ž๋ฃŒ 18 ์ œ 4 ์ ˆ FIU ํ†ต๋ณด(์ด์ฒฉ) ์‚ฌ๊ฑด์˜ ์ฒ˜๋ฆฌ 20 1. ์ผ์„ ์ฒญ ์ด์ฒฉ ์ ˆ์ฐจ 20 2. ์ผ์„ ์ฒญ์˜ ๋‚ด์‚ฌ ๋ฐ ์ˆ˜์‚ฌ ๋‹จ๊ณ„ 21 ์ œ 5 ์ ˆ ๋ฒ•์ง‘ํ–‰๊ธฐ๊ด€์˜ ํŠน์ •๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด ์ œ๊ณต ์š”์ฒญ 21 ์ œ 6 ์ ˆ ํŠน์ •๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด ์ œ๊ณต ์‚ฌ๊ฑด ์ฒ˜๋ฆฌ๊ฒฐ๊ณผ๋ณด๊ณ  25 ์ œ 3 ์žฅ ํ˜„ํ–‰ ์ž๊ธˆ์„ธํƒ ๋ฐฉ์ง€ ์ œ๋„์˜ ๋ฌธ์ œ์  27 ์ œ 1 ์ ˆ ์„œ ์„ค 27 ์ œ 2 ์ ˆ ์ž๊ธˆ์„ธํƒ์˜ ๋‹จ๊ณ„ ๋ฐ ์œ ํ˜• 27 1. ์ž๊ธˆ์„ธํƒ์˜ ๋‹จ๊ณ„ 27 ๊ฐ€. ๊ฐœ์š” 27 ๋‚˜. ๋ฐฐ์น˜ ๋‹จ๊ณ„ 28 ๋‹ค. ๋ฐ˜๋ณต ๋‹จ๊ณ„ 28 ๋ผ. ํ†ตํ•ฉ ๋‹จ๊ณ„ 29 2. ์ž๊ธˆ์„ธํƒ์˜ ์œ ํ˜• 30 ๊ฐ€. ๊ฐœ์„ค 30 ๋‚˜. ๊ณต์ธ๋œ ์‚ฌํ–‰์„ฑ ๊ฒŒ์ž„์„ ํ†ตํ•œ ๋ฐฉ์‹ 30 ๋‹ค. ์ผ๋ฐ˜์  ๋ฌด์—ญ ๋ฐ ์†ก๊ธˆ๊ฑฐ๋ž˜๋ฅผ ํ†ตํ•œ ๋ฐฉ์‹ 31 ๋ผ. ๊ธˆ์œต๊ธฐ๊ด€ ๊ฑฐ๋ž˜๋ฅผ ํ†ตํ•œ ๋ฐฉ์‹ 31 ๋งˆ. ๋Œ€์ฒด์†ก๊ธˆ๋ฐฉ์‹์„ ์ด์šฉํ•˜๋Š” ๋ฐฉ์‹ 32 ์ œ 3 ์ ˆ ์ž๊ธˆ์„ธํƒ ๋ฐฉ์ง€ ์ œ๋„์˜ ์ค‘์š”์„ฑ 33 ์ œ 4 ์ ˆ ํ˜„ํ–‰ ์ž๊ธˆ์„ธํƒ ๋ฐฉ์ง€ ์ œ๋„์˜ ๋ฌธ์ œ์  34 1. FIU๊ฐ€ ์ œ๊ณตํ•˜๋Š” ์ •๋ณด์˜ ๋ฒ”์œ„ ํ˜‘์†Œ 34 ๊ฐ€. ์ค‘๋ณต์กฐ์‚ฌ์˜ ๋ฌธ์ œ ๋ฐœ์ƒ 34 ๋‚˜. ์ž๊ธˆ์„ธํƒ์˜ ํŠน์„ฑ ๊ฐ„๊ณผ: ์ˆœํ™˜๋ก ์  ๋ชจ์ˆœ ๋ฐœ์ƒ 35 2. FIU์˜ ์‹ฌ์‚ฌ๋ถ„์„ ๊ธฐ๋Šฅ์˜ ํ•œ๊ณ„ 36 3. ๋ฒ•์ง‘ํ–‰๊ธฐ๊ด€์˜ ํ”ผ๋“œ๋ฐฑ ๊ฒฐ์—ฌ 39 ์ œ 4 ์žฅ ์ž๊ธˆ์„ธํƒ ๋ฐฉ์ง€ ์ œ๋„ ๊ฐœ์„ ๋ฐฉ์•ˆ 40 ์ œ 1 ์ ˆ FIU๊ฐ€ ์ œ๊ณตํ•˜๋Š” ์ •๋ณด์˜ ๋ฒ”์œ„ ํ™•๋Œ€ 40 1. FIU ์ œ๊ณต ์ •๋ณด ํ™•๋Œ€์˜ ํ•„์š”์„ฑ 40 2. ๋ฏธ๊ตญ FinCEN์˜ ์ •๋ณด ๊ณต์œ  ์ œ๋„๊ฐ€ ์ฃผ๋Š” ์‹œ์‚ฌ์  42 ๊ฐ€. FinCEN Program์˜ ์˜์˜ ๋ฐ ์žฅ์  42 ๋‚˜. ์‹œ์‚ฌ์  44 3. ํ™•๋Œ€ํ•  ์ •๋ณด์˜ ๋ฒ”์œ„ 45 ์ œ 2 ์ ˆ FIU์˜ ๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด ๋ถ„์„ ๊ธฐ๋Šฅ์˜ ๊ฐ•ํ™” 46 ์ œ 3 ์ ˆ AI ๋ถ„์„ ๊ธฐ๋ฒ•์˜ ๋„์ž…: ๋น…๋ฐ์ดํ„ฐ์™€ ๊ธฐ๊ณ„ํ•™์Šต 47 1. 4์ฐจ ์‚ฐ์—…ํ˜๋ช…๊ณผ AI 47 2. AI์˜ ์ ์šฉ ๋ถ„์•ผ 50 3. ๋น…๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•œ AI ๊ธฐ๋ฒ•๊ณผ ๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ 54 ์ œ 4 ์ ˆ ๊ฒ€์ฐฐ์˜ ์ˆ˜์‚ฌ๊ฒฐ๊ณผ ์ •๋ณด ํ”ผ๋“œ๋ฐฑ ๊ฐ•ํ™” 56 ์ œ 5 ์ ˆ ์ •๋ณด๋ณดํ˜ธ๋ฅผ ์œ„ํ•œ AI ๊ธฐ๋ฐ˜ ๋™ํ˜•์•”ํ˜ธ์˜ ํ™œ์šฉ 57 1. ๊ธฐ์กด ์•”ํ˜ธ๊ธฐ์ˆ ์˜ ํ•œ๊ณ„ 57 2. ๋™ํ˜•์•”ํ˜ธ(Homomorphic Encryption)์˜ ์˜์˜ 59 3. ๋™ํ˜•์•”ํ˜ธ์˜ ์ข…๋ฅ˜ 61 ๊ฐ€. Gentry์˜ ์™„์ „๋™ํ˜• ์•”ํ˜ธ ๋ชจ๋ธ 61 ๋‚˜. CRT๊ธฐ๋ฐ˜ BGV ์™„์ „๋™ํ˜•์•”ํ˜ธ์˜ ์ˆ˜ํ•™์  ๋ชจ๋ธ 62 4. ๋™ํ˜•์•”ํ˜ธ์˜ ์žฅ์ ๊ณผ ์‘์šฉ๋ถ„์•ผ 64 5. AI ๊ธฐ๋ฐ˜ "๋™ํ˜• ๊ธฐ๊ณ„ํ•™์Šต"์˜ ์ ์šฉ 66 ๊ฐ€. ๊ธฐ๊ณ„ํ•™์Šต์˜ ์˜์˜ 66 ๋‚˜. ํ•™์Šต์˜ ์œ ํ˜• 67 ๋‹ค. ๊ธฐ๊ณ„ํ•™์Šต์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 69 (1) ๋‚˜์ด๋ธŒ ๋ฒ ์ด์ฆˆ๋ฒ•(Naive Bayes) 69 (2) ์˜์‚ฌ๊ฒฐ์ • ๋‚˜๋ฌด(Decision Tree) 71 (3) ์„œํฌํŠธ ๋ฒกํ„ฐ ๋จธ์‹ (Support Vector Machine) 72 (4) ๋กœ์ง€์Šคํ‹ฑ ํฌ๊ท€๋ถ„์„(Logistic Regression) 74 (5) ๋žœ๋ค ํฌ๋ ˆ์ŠคํŠธ(Random Forest) 75 (6) ์ธ๊ณต ์‹ ๊ฒฝ๋ง(Artificial Neural Network) 77 ๋ผ. ๋™ํ˜• ๊ธฐ๊ณ„ํ•™์Šต ์•Œ๊ณ ๋ฆฌ๋“ฌ์˜ ๊ฐœ์š” 82 ์ œ 6 ์ ˆ AI ๊ธฐ๋ฐ˜ ๋™ํ˜•์•”ํ˜ธ์˜ ์‹ค๋ฌด ์ ์šฉ ๋ชจ๋ธ ๊ตฌ์ƒ 84 1. ๊ธฐ์กด ๋™ํ˜•์•”ํ˜ธ์˜ ํ™œ์šฉ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 84 ๊ฐ€. ๋™ํ˜•์•”ํ˜ธ ๊ธฐ์ˆ ์˜ ๋ฐœ์ „ ์ƒํ™ฉ 86 ๋‚˜. ๊ทผ์‚ฌ๋™ํ˜•์•”ํ˜ธ์™€ ๋™ํ˜•์•”ํ˜ธ ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ ๊ฒ€ํ†  88 ๋‹ค. ์†Œ๊ฒฐ๋ก  90 2. ๊ตฌ์ฒด์ ์ธ ๋™ํ˜• ๊ธฐ๊ณ„ํ•™์Šต ๋ชจ๋ธ 90 ๊ฐ€. ๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด์— ๋Œ€ํ•œ ๋™ํ˜•์•”ํ˜ธ ํ™œ์šฉ ๋ฐฉ์•ˆ 90 ๋‚˜. ๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด์— ๋Œ€ํ•œ ๋™ํ˜• ๊ธฐ๊ณ„ํ•™์Šต ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ 95 ๋‹ค. ๋™ํ˜• ๊ธฐ๊ณ„ํ•™์Šต์˜ ํŒจํ„ด ์œ ํ˜•ํ™” ๋ฐฉ์•ˆ 97 ๋ผ. ์ตœ์ ์˜ ๋™ํ˜• ๊ธฐ๊ณ„ํ•™์Šต ์•Œ๊ณ ๋ฆฌ๋“ฌ ๋ชจ๋ธ ๊ฒ€ํ†  99 3. ๊ธˆ์œต๊ฑฐ๋ž˜ ์ •๋ณด ๊ณต์œ  ์‹œ์Šคํ…œ์˜ ๊ตฌ์กฐ 102 4. ๋น„๋ฐ€ํ‚ค์˜ ๋ถ„์‚ฐ ๊ด€๋ฆฌ 103 5. ํ–ฅํ›„ ๊ณผ์ œ 105 ๊ฐ€. ๋™ํ˜•์•”ํ˜ธ์™€ ๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด์˜ ์ ‘๋ชฉ ์‹œ๋„ ์‹œ๊ธ‰ 105 ๋‚˜. ํ•™์Šต๋ฐ์ดํ„ฐ์˜ ํ™•๋ณด ๋ฌธ์ œ 105 ๋‹ค. ์ตœ์ ์˜ ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ์ฐพ๋Š” ๋ฌธ์ œ 106 ๋ผ. ์ด์ข…์˜ ๋ฐ์ดํ„ฐ ๊ฒฐํ•ฉ์— ๋”ฐ๋ฅธ ๋™ํ˜•์•”ํ˜ธ ๊ธฐ์ˆ ์˜ ํ•œ๊ณ„ ๊ทน๋ณต 107 ์ œ 7์ ˆ ๋ฒ•์  ์Ÿ์ ์˜ ๊ฒ€ํ†  108 1. ์˜์žฅ์ฃผ์˜ ์œ„๋ฐ˜์ธ์ง€ ์—ฌ๋ถ€ 108 2. ๊ด‘๋ฒ”์œ„ํ•œ ์‚ฌ์ฐฐ์— ํ•ด๋‹นํ•˜๋Š”์ง€ ์—ฌ๋ถ€ 110 3. ํŠน์ • ๊ธˆ์œต๊ฑฐ๋ž˜์ •๋ณด์˜ ๋ณด๊ณ  ๋ฐ ์ด์šฉ ๋“ฑ์— ๊ด€ํ•œ ๋ฒ•๋ฅ  ๊ฐœ์ • ๋ฐฉํ–ฅ 112 ์ œ 5 ์žฅ ๊ฒฐ ๋ก  114 ์ฐธ๊ณ ๋ฌธํ—Œ 116Maste

    Experimental and numerical analysis of nanoindentation on copper single crystal

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    MasterThis thesis investigates deformation behaviors of Cu single crystals such as the indentation size effect (ISE), hardening behaviors, and pile-up pattern experimentally during nanoindentation. The experiment that has been used in the deformation behaviors analysis of Cu single crystal can be largely divided into two as simple compression and nanoindentation. The numerical simulation is formulated based on the phenomenological crystal plasticity hardening model and implemented into the user subroutines of the commercial finite element program ABAQUS. The time integration of the constitutive model uses the fully implicit backward Euler method. The simple compression experiment are performed in Cu single crystals with (100), (110) and (111) crystallograhpic orientation and compared with the crystal plasticity finite element (CPFEM) results. The deformed shape, hardening response, and rotation of crystallographic orientation of the Cu single crystal are presented using the INSTRON 8521 and the electron backscattered diffraction (EBSD) system. The macroscopic shape changes are explained by the slip system. The experimental resultants presented in this paper are reasonably good agreement as well as numerical results. This shows the validity of the current constitutive modeling at the single crystal level. The nanoindentation experiment are conducted in three different crystallographic orientations, i.e., (100), (110) and (111), by using a three different indenter tips (Vikers, Berkovich, Conical). The MTS Nano Indenter Xp system and VEECO Dimension 3100 was used for the elastic modulus- displacement relationship, the hardness-displacement relationships, and the surface topography near the nanoindents. The scattering of the elastic modulus in Cu single crystal is due to the thermal drift effect. The ISE of Cu single is characterized by various parameters such as the indentation depth, geometry of the indenter tips and the crystallographic orientations and analysed by the strain gradient model. The pile-up patterns observed near the nanoindents are measured by atomic force microscopy (AFM). These surface deformation patterns explained by the crystallographic slip systems. The resultant of nanoindentation reflects the crystallographic orientation. It also display the crystallographic orientation of material has more strong influence on the nanoindentation results than the loading components such as the azimutal orientation, the geometry of the indenter tips

    Inequality and Political Trust : A Focus on Attitude toward Inequality

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    ์†Œ๋“๋ถˆํ‰๋“ฑ ๋ฌธ์ œ๋Š” ์ •๋ถ€๊ฐ€ ๋Œ€์ฒ˜ํ•ด์•ผ ํ•  ์ค‘์š”ํ•œ ์ •์ฑ…๊ณผ์ œ ์ค‘ ํ•˜๋‚˜๋กœ์„œ ๋‹ค์–‘ํ•œ ์ •์ฑ…์˜์—ญ์—์„œ ๊ทธ ์›์ธ๊ณผ ์‹ฌํ™”๊ฐ€ ๊ฐ€์ ธ์˜ฌ ์‚ฌํšŒ ๊ฒฝ์ œ ์ •์น˜์  ๋ณ€ํ™”์™€ ์–‘ํƒœ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋“ค์ด ์ด๋ฃจ์–ด์ ธ ์™”๋‹ค. ํ•˜์ง€๋งŒ ์ด๋Ÿฌํ•œ ๋…ธ๋ ฅ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ •๋ถ€์‹ ๋ขฐ์™€์˜ ๊ด€๊ณ„๋Š” ๊ทธ ์ค‘์š”์„ฑ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์—ฌ์ „ํžˆ ๋ชจํ˜ธํ•œ ์—ฐ ๊ตฌ์˜์—ญ์œผ๋กœ ๋‚จ์•„์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ •๋ถ€์‹ ๋ขฐ์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ํ‰๊ฐ€์ค€๊ฑฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์†Œ๋“๋ถˆํ‰๋“ฑ ์ˆ˜์ค€์ด ์ •์ฑ…๊ณผ์ •์—์„œ์˜ ์ ˆ์ฐจ์  ๋ถ„๋ฐฐ์  ๊ณต์ •์„ฑ ์ธก๋ฉด์—์„œ, ๊ทธ๋ฆฌ๊ณ  ์‚ฐ์ถœ๋ฌผ๋กœ์„œ ์ •๋ถ€์„ฑ๊ณผ์  ์ฐจ์›์—์„œ ํ‰๊ฐ€์˜ ๋Œ€์ƒ์œผ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋”ฐ๋ผ์„œ ๊ทธ ์‹ฌํ™”๋Š” ์ •๋ถ€์‹ ๋ขฐ์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ์Œ์„ ํƒ์ƒ‰์ ์ธ ์‹œ๊ฐ์—์„œ ๋ถ„์„ํ•˜์˜€๋‹ค. ํŠนํžˆ ์‚ฌํšŒ ๊ตฌ์„ฑ์›๋“ค์ด ๊ฐ–๊ณ  ์žˆ๋Š” ๋ถˆํ‰๋“ฑ์— ๋Œ€ํ•œ ํƒœ๋„์™€ ์ธ์‹์€ ๊ฒฐ์ฝ” ๋™์ผํ•˜์ง€ ์•Š๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•˜์—ฌ, ๋ถˆํ‰๋“ฑ์— ๋Œ€ํ•œ ๊ธฐํ”ผ ๋˜๋Š” ์„ ํ˜ธ ์ •๋„์— ๋”ฐ๋ผ ์†Œ๋“๋ถˆํ‰๋“ฑ๊ณผ ์ •๋ถ€์‹ ๋ขฐ ๊ฐ„์˜ ๊ด€๊ณ„๊ฐ€ ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ์Œ์„ ๊ตญ๊ฐ€ ๊ฐ„ ๋น„๊ต ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ, ์†Œ๋“๋ถˆํ‰๋“ฑ๊ณผ ์ •๋ถ€์‹ ๋ขฐ ๊ฐ„์˜ ๊ด€๊ณ„๋Š” ๊ตญ๊ฐ€์˜ ๊ฒฝ์ œ๋ฐœ์ „์ˆ˜์ค€์— ๋”ฐ๋ผ ์ฐจ์ด๊ฐ€ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋จผ์ € ์„ ์ง„๊ตญ์—์„œ๋Š” ์†Œ๋“๋ถˆํ‰๋“ฑ์ด ์ฆ๊ฐ€ํ• ์ˆ˜๋ก ์ •๋ถ€์‹ ๋ขฐ๋Š” ๋‚ฎ์•„์ง€์ง€๋งŒ, ๋ถˆํ‰๋“ฑ์— ๋Œ€ํ•œ ์„ ํ˜ธ(์ฆ‰, ์šฉ์ธ์ •๋„)๊ฐ€ ๋†’์€ ์‚ฌ๋žŒ๋“ค์—๊ฒŒ์„œ๋Š” ์†Œ๋“๋ถˆํ‰๋“ฑ์˜ ์‹ฌํ™”์— ๋”ฐ๋ฅธ ์ •๋ถ€์‹ ๋ขฐ์˜ ํ•˜๋ฝ์ด ํ›จ์”ฌ ์™„ํ™”๋˜๊ณ  ์žˆ์—ˆ๊ณ , ๊ฐœ๋ฐœ๋„์ƒ๊ตญ์—์„œ๋Š” ์†Œ๋“๋ถˆํ‰๋“ฑ๊ณผ ์ •๋ถ€์‹ ๋ขฐ ๊ฐ„์˜ ํŠน์ •ํ•œ ํŒจํ„ด์„ ๋ฐœ๊ฒฌํ•  ์ˆ˜ ์—†์—ˆ์œผ๋ฉฐ, ๋ถˆํ‰๋“ฑ์— ๋Œ€ํ•œ ์„ ํ˜ธ(์ฆ‰, ์šฉ์ธ์ •๋„)๋ฅผ ๊ณ ๋ คํ•˜๋Š” ๊ฒฝ์šฐ์—๋„ ์ •๋ถ€์‹ ๋ขฐ์˜ ์ƒ์Šน๊ณผ ํ•˜๋ฝ์ด ์ƒ์‡„๋˜๋Š” ํ˜„์ƒ์„ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์ •๋ถ€์‹ ๋ขฐ๋ฅผ ์ •๋ถ€์— ๋Œ€ํ•œ ์‹ ๋ขฐ์™€ ๊ด€๋ฃŒ์— ๋Œ€ํ•œ ์‹ ๋ขฐ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ์‚ดํŽด๋ณด๋ฉด ์„ ์ง„๊ตญ์˜ ๊ฒฝ์šฐ ์–‘์ž์˜ ๊ตฌ๋ณ„์ด ํฌ๊ฒŒ ๋‹ค๋ฅด์ง€ ์•Š์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋Š” ๋ฐ˜๋ฉด, ๊ฐœ๋„๊ตญ์˜ ๊ฒฝ์šฐ ์˜ค์ง ์ •๋ถ€๊ด€๋ฃŒ์˜ ๊ฒฝ์šฐ์—์„œ๋งŒ ๋ถˆํ‰๋“ฑ๊ณผ์˜ ๊ด€๋ จ์„ฑ์„ ์ผ๋ถ€ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ์ •๋ถ€์‹ ๋ขฐ์— ๋Œ€ํ•œ ๋…ผ์˜์— ์žˆ์–ด์„œ ์†Œ๋“๋ถˆํ‰๋“ฑ ๋ฌธ์ œ์— ๋Œ€ํ•œ ๊ณ ๋ ค ํ•„์š”์„ฑ์„ ์ œ๊ธฐํ•  ๋ฟ ์•„๋‹ˆ๋ผ ์ด๋“ค๊ฐ„์˜ ๊ด€๊ณ„๊ฐ€ ํ•ด๋‹น ๊ตญ๊ฐ€์˜ ๊ฒฝ์ œ๋ฐœ์ „์ˆ˜์ค€ ๋ฐ ์‚ฌํšŒ ๊ตฌ์„ฑ์›์ด ๊ฐ–๊ณ  ์žˆ๋Š” ๋ถˆํ‰๋“ฑ์— ๋Œ€ํ•œ ์„ ํ˜ธ ํšŒํ”ผํƒœ๋„์— ๋”ฐ๋ผ์„œ ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์ •๋ถ€์‹ ๋ขฐ ์ œ๊ณ ๋ฅผ ์œ„ํ•œ ๋‹ค์ฐจ์›์ ์ธ ์ •์ฑ…์  ๋…ธ๋ ฅ์˜ ํ•„์š”์„ฑ์„ ์‹œ์‚ฌ ํ•œ๋‹ค๊ณ  ํ•˜๊ฒ ๋‹ค

    The Functions and Aspects of the Visual Signs in The Learnerโ€™s Dictionary of Korean

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