19 research outputs found

    An Analysis of the Korean Bioscientists Shifting Perspectives on Bioethics

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ณผํ•™์‚ฌ๋ฐ๊ณผํ•™์ฒ ํ•™์ „๊ณต,2019. 8. ์ด๋‘๊ฐ‘.The discovery of CRISPR-Cas9 and development of related technologies promised a much more accurate and easier to use method of genome editing. Amidst the research fervor prompted by this discovery, some Korean bioscientists are arguing that the Korean Bioethics and Safety Act imposes excessive limitations on gene therapy research. Calling for deregulation at the 112nd Roundtable Discussions, a policy discussion hosted by the Korean Academy of Science and Technology, three of these bioscientists presented their arguments. Interestingly, some arguments utilized naturalness as a moral value, which stands in contrast to the expected skepticism towards assigning value on naturalness. Following the line of thought presented by Daston and Vidal in The Moral Authority of Nature, this paper studies the naturalness-based arguments presented by scientists at the 112nd Roundtable not in their own but as a window to their other arguments, their position in relation to the debate, and what moral imperatives their position is based upon. Scientists at the Roundtable used their scientific expertise to argue for the naturalness of their proposed gene therapy, and argued that only science could provide proper answers to questions of naturalness, adopting a supposedly moral argument to support the imperative of scientific research. This paper argues that a Q&A relationship is being established by the scientists between themselves and the public, which I believe suggests a shift, but not a break, from a simplistic top-down relationship dictated by the deficit model.์œ ์ „์ž์น˜๋ฃŒ๋Š” ์ธ๊ฐ„ ์œ ์ „์ž ์กฐ์ž‘์˜ ๋‹น์œ„์„ฑ๊ณผ ๋‹ค์–‘ํ•œ ์‹ ์ฒด์ , ์‚ฌํšŒ์  ๋ถ€์ž‘์šฉ์— ๋Œ€ํ•œ ๋…ผ๋ž€ ์†์—์„œ ์ฐฌ๋ฐ˜ ์ž…์žฅ ๊ฐ„์˜ ์ฒจ์˜ˆํ•œ ๋Œ€๋ฆฝ์œผ๋กœ ๋‘˜๋Ÿฌ์Œ“์—ฌ์žˆ๋‹ค. ์ด์ „์˜ ์ˆ˜๋‹จ ๋ณด๋‹ค ํ›จ์”ฌ ์‚ฌ์šฉ์ด ์šฉ์ดํ•˜๊ณ  ์ •ํ™•ํ•œ 3์„ธ๋Œ€ ์œ ์ „์ž๊ฐ€์œ„ CRISPR-Cas9์˜ ๋ฐœ๊ฒฌ๊ณผ ๊ด€๋ จ ๊ธฐ์ˆ  ๊ฐœ๋ฐœ์— ํž˜์ž…์–ด ์„ธ๊ณ„ ๊ณณ๊ณณ์—์„œ ์œ ์ „์ž์น˜๋ฃŒ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰ ์ค‘์ธ ์˜ค๋Š˜๋‚  ํ˜„ํ–‰ ์ƒ๋ช…์œค๋ฆฌ์•ˆ์ „๋ฒ•์ด ๊ณผ๋„ํ•œ ๊ทœ์ œ๋ผ๊ณ  ํŒ๋‹จํ•œ ํ•œ๊ตญ์˜ ์ผ๋ถ€ ๊ณผํ•™์ž๋“ค์€ 2017๋…„ 8์›” ์ œ112ํšŒ ํ•œ๋ฆผ์›ํƒํ† ๋ก ํšŒ์—์„œ ๋ฒ•์˜ ์™„ํ™”๋ฅผ ์ฃผ์žฅํ•˜์˜€๋‹ค. ์ด๋“ค ๊ณผํ•™์ž๋“ค์ด ์ž์‹ ์˜ ์ฃผ์žฅ์„ ๋’ท๋ฐ›์นจํ•˜๊ธฐ ์œ„ํ•ด ๋‚ด์„ธ์šด ์ฃผ์žฅ ์ค‘์—๋Š” ์ž์—ฐ์„ฑ์— ๊ธฐ๋ฐ˜ํ•œ ๋…ผ๋ฆฌ๋„ ํฌํ•จ๋˜์–ด ์žˆ์—ˆ๋Š”๋ฐ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๋Œ€์Šคํ„ด(Lorraine Daston)๊ณผ ๋น„๋‹ฌ(Fernando Vidal) ํŽธ์ € ์ž์—ฐ์˜ ์œค๋ฆฌ์  ๊ถŒ์œ„์˜ ์—ญ์‚ฌ(Moral Authority of Nature)์˜ ํ•ด์„์— ๋™์˜ํ•˜์—ฌ ๊ณผํ•™์ž๋“ค์˜ ์ž์—ฐ์„ฑ ๊ธฐ๋ฐ˜ ๋…ผ๋ฆฌ๋ฅผ ๊ทธ ์ž์ฒด๋กœ ๋ฐ›์•„๋“ค์ด๊ธฐ ๋ณด๋‹ค ๊ด€๋ จ๋œ ๋‹ค๋ฅธ ๋‹น์œ„์„ฑ๊ณผ ๋…ผ๋ฆฌ์  ์ฃผ์žฅ์„ ํŒŒํ•ด์น  ์—ด์‡ ๋กœ ๋ฐ›์•„๋“ค์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๊ณผํ•™์ž๋“ค์˜ ์ฃผ์žฅ ์†์—์„œ ํ•œ๊ตญ ๊ณผํ•™์ž๋“ค์ด ์ดํ•ดํ•˜๊ณ  ์žˆ๋Š” ๊ณผํ•™๊ณ„์™€ ์ผ๋ฐ˜ ๋Œ€์ค‘์˜ ๊ด€๊ณ„๊ฐ€ ๋‹จ์ˆœ ๋ถ€์กฑ๋ชจ๋ธ (deficit model)๋กœ ๊ตญํ•œ๋˜์ง€ ์•Š์€ ์ผ์ข…์˜ ๋ฌธ๋‹ตํ˜•์˜ ๊ด€๊ณ„๋ผ๊ณ  ๋ณผ ๊ทผ๊ฑฐ๊ฐ€ ์žˆ๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค.1. Introduction: Biotech, Nature, and a Policy Discussion 5 2. About the Invocation and Abjuration of Naturalness 14 2.1. Existing literature on the relationship between bioscience and the public 14 2.2. A Brief History of Naturalness as a Source of Moral Authority 22 3. Naturalness and CRISPR: A Collection of Arguments 29 3.1. The naturalness of minimalist CRISPR interventions. 34 3.2. No dangerous insertions: a non-transgenic argument 42 3.3. Human enhancement and the natural human gene pool 50 3.4. Negative connotations of mutation and the publics irrationality 59 4. The Public and the Authority of Science 64 4.1. The positioning of the scientists arguments and the Q&A relationship 64 4.2. History of the Korean Bioethics and Safety Act and the Legacy of Hwang 74 4.3. Appeal to the authority of the NAS report and Asilomar-in-Memory 83 5. Conclusion: Scientists of the Roundtable 93 References 98Maste

    Implementation of Fire Detection System Using Raspberry Pi-based SSD

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    ๋ผ์ฆˆ๋ฒ ๋ฆฌํŒŒ์ด ๊ธฐ๋ฐ˜ SSD๋ฅผ ์ด์šฉํ•œ ํ™”์žฌ ๊ฒ€์ถœ ์‹œ์Šคํ…œ ๊ตฌํ˜„ ์–‘์Šนํ˜ธ ํ•œ๊ตญ ํ•ด์–‘ ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์ „์ž ํ†ต์‹  ๊ณตํ•™๊ณผ ์ดˆ๋ก ์ตœ๊ทผ ๊ตญ๋‚ด์™€ ๊ตญ์™ธ์— ํ™”์žฌ๊ฐ€ ์—ฐ๋‹ฌ์•„ ์ผ์–ด๋‚˜ ์ด์Šˆ๊ฐ€ ๋˜์—ˆ๋‹ค. ์‚ฌ๋žŒ์ด ์ž์ฃผ ์ฐพ์ง€ ์•Š๋Š” ์ง€์—ญ์€ ํ™”์žฌ๊ฐ€ ์ผ์–ด๋‚  ๊ฒฝ์šฐ ์ดˆ๊ธฐ ๋Œ€์‘๊นŒ์ง€ ๊ธด ์‹œ๊ฐ„์ด ๊ฑธ๋ฆฌ๊ธฐ ๋•Œ๋ฌธ์— ๋Œ€ํ˜• ํ™”์žฌ๋กœ ์ปค์ง€๋Š” ๊ฒƒ์„ ๋ง‰์ง€ ๋ชปํ•˜๊ฒŒ ๋œ๋‹ค. ๋•Œ๋ฌธ์— ํ™”์žฌ๊ฐ€ ๋Œ€ํ˜•์œผ๋กœ ์ปค์ง€๊ธฐ ์ „์— ๊ฐ์ง€ํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•ด ๊ด€์‹ฌ์„ ๊ฐ€์ง€๊ฒŒ ๋˜์—ˆ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ๋„“์€ ์ง€์—ญ์˜ ํ™”์žฌ๊ฐ์ง€ ์‹œ์Šคํ…œ์„ ๊ตฌ์„ฑํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ผ์ฆˆ๋ฒ ๋ฆฌํŒŒ์ด์™€ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ์ˆ ์ธ ๊ฐ์ฒด๊ฐ์ง€์™€ ์ „๋ ฅ์„  ํ†ต์‹  ๊ธฐ์ˆ ์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ฒ€์ถœ์†๋„๊ฐ€ ๋Š๋ฆฐ F-RCNN๊ณผ ๋น ๋ฅธ SSD์„ ์‚ฌ์šฉํ•˜์—ฌ ํ•™์Šต ๋ชจ๋ธ์„ ํ˜•์„ฑํ•˜์—ฌ ๋ผ์ฆˆ๋ฒ ๋ฆฌํŒŒ์ด์—์„œ ๋” ์ ํ•ฉํ•œ ๋ชจ๋ธ์ธ์ง€๋ฅผ ๊ฒ€์ถœ์†๋„๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ๋˜ํ•œ ์‹œ์Šคํ…œ ๊ตฌ์„ฑ์— ์‚ฌ์šฉ๋œ ์ „๋ ฅ์„  ํ†ต์‹ ์€ ๊ณ ์ „์••๊ณผ ๊ณ ์ „๋ฅ˜ ํ™˜๊ฒฝ์—์„œ๋„ ํ†ต์‹ ์ด ์ž˜ ์ด๋ฃจ์–ด์ง€๋Š” ๊ฐ€๋ฅผ ํ™•์ธํ•˜๊ธฐ๋กœ ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ผ์ฆˆ๋ฒ ๋ฆฌํŒŒ์ด์—์„œ ํŒŒ์ด์นด๋ฉ”๋ผ๋ฅผ ํ†ตํ•ด ํ™”์žฌ๋ฅผ ๊ฐ์ง€ํ•˜๋ฉด ์œ ๋„ํ˜• ์ „๋ ฅ์„  ํ†ต์‹  ๊ธฐ์ˆ ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ชจ๋‹ˆํ„ฐ๋ง PC๋กœ ํ™”์žฌ๊ฐ€ ์ผ์–ด๋‚ฌ์Œ์„ ์•Œ๋ฆฌ๋Š” ํ…์ŠคํŠธ์™€ ์ด๋ฏธ์ง€๋ฅผ ์ „์†กํ•˜๋Š” ์‹คํ—˜์— ์„ฑ๊ณตํ•˜์˜€๋‹ค.1. ์„œ ๋ก  1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์—ฐ๊ตฌ ๋ชฉ์  1 1.2 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 5 2. ๊ฐ์ฒด ๊ฒ€์ถœ์„ ์œ„ํ•œ ๋”ฅ๋Ÿฌ๋‹ 2.1 F-RCNN 7 2.1 SSD 7 3. ํ™”์žฌ ์ด๋ฏธ์ง€ ํ•™์Šต ๋ฐ ๊ฒ€์ถœ ์†๋„ ๋น„๊ต 3.1 ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์„ฑ ๋ฐ ํ•™์Šต 18 3.2 ๊ฐ์ฒด ๊ฒ€์ถœ ์†๋„ ๋น„๊ต 20 4. ์ „๋ ฅ์„  ํ†ต์‹  ๊ธฐ๋ฐ˜ ํ™”์žฌ ๊ฒ€์ถœ ์‹œ์Šคํ…œ ๊ตฌํ˜„ 4.1 ์ „๋ ฅ์„  ํ†ต์‹  ์ ์šฉ์‹œํ—˜ 26 4.2 ํ™”์žฌ ๊ฒ€์ถœ ์‹คํ—˜ ๊ฒฐ๊ณผ 37 5. ๊ฒฐ๋ก  45 ์ฐธ๊ณ ๋ฌธํ—Œ 46 ๋ถ€๋ก 49Maste

    ์—ฌ๋Ÿฌ ์‹œ์Šคํ…œ๊ฐ„ ๋น„๊ณ ์ „์  ์ƒ๊ด€๊ด€๊ณ„์˜ ํŠน์„ฑํ™”: ์ž์›๋น„์šฉ๊ณผ ๊ฐ€์‚ฐ์„ฑ ๊ด€์ ์—์„œ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€,2016. 2. ์ •ํ˜„์„.Non-classical correlations between multiple systems lie at the heart of notable features of quantum systems, especially in application to quantum information processing. However, their structures and properties are not yet well understood because of the complexity of multipartite quantum systems. In this thesis, we look into non-classical correlations between multiple systems from two points of view, with the aim to deepen the understanding of multipartite non-classical correlation. In the first point of view, multipartite nonclassical correlations are considered as a resource, which cannot be generated by a restricted set of operations, local operations and classical communication (LOCC). Then, we ask how much amount of multipartite entanglement is required to prepare a given multipartite state by LOCC. In other words, we define the Greenberger-Horne-Zeilinger (GHZ) entanglement cost as a generalization of the entanglement cost to the multipartite setting. We provide a LOCC procedure for preparing an arbitrary pure multipartite state from GHZ states, and show that the conversion rate of this procedure is given by multipartite quantum discord of the state to be prepared, which means that multipartite quantum discord is an upper bound for the GHZ entanglement cost. In the second point of view, bipartite correlations of pairs of subsystems are summed to yield the multipartite correlation of the total system. In this sense, we seek to find general relations between multipartite correlations and bipartite correlations of pairs of subsystems. We provide a general concept of the additivity relations, which is derived in terms of total correlation, and examine them for entanglement and discord.Chapter 1 Introduction 1 Chapter 2 Background 5 2.1 Classical information 5 2.1.1 Shannon entropy and mutual information 5 2.1.2 Classical data compression 7 2.2 Quantum information 8 2.2.1 Quantum entropy and correlation 8 2.2.2 Quantum data compression 10 2.3 Quantum entanglement 11 2.3.1 Entanglement from the resource point of view 12 2.3.2 Relative entropy of entanglement 20 2.4 Quantum discord 21 2.4.1 De nition 22 2.4.2 Relative entropy of discord 26 2.5 Quantum correlations in terms of the relative entropy 27 Chapter 3 Quantum discord as an upper bound for the GHZ entanglement cost 30 3.1 Introduction 30 3.2 Preliminaries 32 3.3 LOCC preparation from GHZ states 34 3.3.1 Introduction of an approximate state 34 3.3.2 Preparation of the approximate state 37 3.3.3 Asymptotic preparation from GHZ states 41 3.4 Applications and examples 44 3.4.1 Upper and lower bounds for the GHZ entanglement cost 44 3.4.2 Comparison with preparation of states from singlets 45 3.4.3 Application of LD to general mixed states 46 3.4.4 Discussion 49 Chapter 4 Additivity relations of quantum correlations 50 4.1 Introduction 50 4.2 General concepts 51 4.2.1 Additivity of the total correlation 52 4.2.2 Additivity of the relative entropy of entanglement 55 4.2.3 Additivity of the relative entropy of discord 57 4.3 Examples 63 4.3.1 Mixed states case 69 4.3.2 Conclusion 71 Chapter 5 Conclusion 72 Bibliography 74 ๊ตญ๋ฌธ์ดˆ๋ก 84Docto

    Effects of Income Segregation in Urban Residential Areas on Residents' Sense of Social Cohesion

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2014. 8. ๊ถŒ์˜์ƒ.ํ•œ๊ตญ ์‚ฌํšŒ๋Š” ๊ณ ๋„์˜ ์••์ถ•์„ฑ์žฅ๊ณผ ๊ฒฝ์ œ์œ„๊ธฐ, ์„ธ๊ณ„ํ™”์˜ ํ๋ฆ„ ์†์—์„œ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๋Š” ์‚ฌํšŒ๊ฐˆ๋“ฑ์„ ๊ฒฝํ—˜ํ•˜๊ณ  ์žˆ๋‹ค. ๊ฐˆ๋“ฑ์˜ ์ •๋„๊ฐ€ ์‚ฌํšŒ์˜ ์ง€์†๊ฐ€๋Šฅ์„ฑ์„ ์œ„ํ˜‘ํ•  ์ •๋„๋กœ ์‹ฌ๊ฐํ•ด์กŒ๋‹ค๋Š” ์œ„๊ธฐ์˜์‹ ์†์—์„œ ํ•œ๊ตญ ์‚ฌํšŒ์—์„œ๋„ ์‚ฌํšŒํ†ตํ•ฉ์˜ ํ•„์š”์„ฑ์ด ์ œ๊ธฐ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๋‹ค์–‘ํ•œ ๊ฐˆ๋“ฑ ์–‘์ƒ ์ค‘์—์„œ๋„ ๊ณ„์ธต๊ฐˆ๋“ฑ์ด ๊ฐ€์žฅ ์‹ฌ๊ฐํ•œ ๋ฌธ์ œ ์ค‘ ํ•˜๋‚˜๋กœ ๊ผฝํžˆ๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ๊ฒฝ๊ฐํ•˜๊ธฐ ์œ„ํ•œ ๋Œ€์ฑ… ๋งˆ๋ จ์ด ์‹œ๊ธ‰ํ•œ ์ƒํ™ฉ์ด๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณ„์ธต๊ฐˆ๋“ฑ ๋ฐ ์‚ฌํšŒํ†ตํ•ฉ๊ณผ ๊ด€๋ จํ•˜์—ฌ ๋„์‹œ ์ฐจ์›์˜ ๊ด€์ ์—์„œ ์ ‘๊ทผ์„ ์‹œ๋„ํ•˜์˜€๋‹ค. ์ฃผํƒ์ด๋‚˜ ๊ฑฐ์ฃผ์ง€์—ญ ์ž์ฒด๊ฐ€ ๊ทธ๊ณณ์— ๊ฑฐ์ฃผํ•˜๋Š” ์‚ฌ๋žŒ์˜ ์†Œ๋“์ˆ˜์ค€์ด๋‚˜ ๊ณ„์ธต์„ ์ƒ์ง•์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ธ๋‹ค๋Š” ์ „์ œํ•˜์—์„œ, ์‹ฌํ™”๋˜๊ณ  ์žˆ๋Š” ๋„์‹œ์—์„œ์˜ ๊ฑฐ์ฃผ์ง€ ๋ถ„๋ฆฌ๊ฐ€ ๋„์‹œ๋ฏผ์˜ ๊ณ„์ธต๊ฐˆ๋“ฑ์„ ๊ฐ€์†ํ™”์‹œํ‚ค๋Š”์—ญํ• ์„ ํ•  ๊ฒƒ์œผ๋กœ ๊ฐ€์ •ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฐ๊ฒฝ์—์„œ ์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋„์‹œ์—์„œ์˜ ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ฑฐ์ฃผ์ง€ ๋ถ„๋ฆฌ๊ฐ€ ๋„์‹œ๋ฏผ์˜ ๊ณ„์ธต๊ฐˆ๋“ฑ๊ณผ ์‚ฌํšŒํ†ตํ•ฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ฐํ˜€๋‚ด๋Š” ๊ฒƒ์ด๋‹ค. ์ด์™€ ๊ด€๋ จ๋œ ์ด๋ก ์  ๊ณ ์ฐฐ ๋ฐ ํ˜„ํ™ฉ ๋ถ„์„ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋„์‹œ์—์„œ์˜ ๊ฑฐ์ฃผ์ง€ ๋ถ„๋ฆฌ๋Š” ์‚ฌํšŒ์  ๋ฐฐ์ œ๋ฅผ ์•ผ๊ธฐํ•˜์—ฌ ๊ฑฐ์ฃผ๋ฏผ์˜ ์‚ฌํšŒํ†ตํ•ฉ์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ์—ฌ๊ฒจ์ง€๊ณ  ์žˆ๋‹ค. ์ด์— ๋Œ€์‘ํ•˜๊ธฐ ์œ„ํ•œ ๊ธฐ์กด์˜ ๋„์‹œ์ •์ฑ…์œผ๋กœ๋Š” ๋Œ€ํ‘œ์ ์œผ๋กœ ์‚ฌํšŒ์  ํ˜ผํ•ฉ ์ •์ฑ…์„ ๋“ค ์ˆ˜ ์žˆ๋Š”๋ฐ, ์‚ฌํšŒ์  ํ˜ผํ•ฉ ์ •์ฑ…์ด ์‚ฌํšŒํ†ตํ•ฉ์ด๋ผ๋Š” ๊ถ๊ทน์ ์ธ ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ณต๊ฐ„์˜ ๋ถ„๋ฆฌ๊ฐ€ ๊ฐ€์ ธ์˜ค๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ์ดํ•ด๊ฐ€ ์„ ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ ์„œ์šธ์‹œ์˜ ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„ ๋ถ„๋ฆฌ ํ˜„ํ™ฉ์„ ๊ต์œก์ˆ˜์ค€์„ ๋Œ€๋ฆฌ๋ณ€์ˆ˜๋กœ ์‹ค์ฆ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์„œ์šธ์‹œ์—๋Š” ๊ฑฐ์‹œ์ , ๋ฏธ์‹œ์  ์ฐจ์› ๋ชจ๋‘์—์„œ ์†Œ๋“์ˆ˜์ค€์˜ ๊ณต๊ฐ„ ์ง‘์ ์ด ์กด์žฌํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฑฐ์‹œ์  ์ฐจ์›์—์„œ ๊ณต๊ฐ„ ์ง‘์ ์˜ ๋ณ€ํ™” ์–‘์ƒ๊ณผ ๋ฏธ์‹œ์  ์ฐจ์›์—์„œ ๊ณต๊ฐ„ ์ง‘์ ์˜ ๋ณ€ํ™” ์–‘์ƒ์€ ์„œ๋กœ ๋…๋ฆฝ์ ์ธ ๊ฒƒ์ž„์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ์„œ์šธ์‹œ์˜ ๊ฒฝ์šฐ ๊ฑฐ์‹œ์  ์ฐจ์›์—์„œ์˜ ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„ ์ง‘์  ์ •๋„๋Š” ๊ฐ์†Œํ•˜๊ณ  ์žˆ์ง€๋งŒ, ๋ฏธ์‹œ์  ์ฐจ์›์—์„œ์˜ ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„ ์ง‘์  ์ •๋„๋Š” ๋ณ€ํ™”๊ฐ€ ์—†๊ฑฐ๋‚˜ ์กฐ๊ธˆ ์ƒ์Šนํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๋„์‹œ์—์„œ์˜ ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ฑฐ์ฃผ์ง€ ๋ถ„๋ฆฌ๊ฐ€ ๊ณ„์ธต๊ฐˆ๋“ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‹ค์ฆ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ด๋ฅผ ์ ์ ˆํ•˜๊ฒŒ ๋ฐ˜์˜ํ•œ ๋…๋ฆฝ๋ณ€์ˆ˜์™€ ์ข…์†๋ณ€์ˆ˜ ์„ค์ •์ด ์ค‘์š”ํ•˜๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์†Œ๋“์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„์˜ ๋ถ„๋ฆฌ๋ฅผ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋น„๊ณต๊ฐ„ ์ง€ํ‘œ์™€ ๊ณต๊ฐ„ ์ง€ํ‘œ๋ฅผ ๋ชจ๋‘ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๋น„๊ณต๊ฐ„ ์ง€ํ‘œ๋Š” ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„์˜ ์ž…์ง€ ํŒจํ„ด์— ๋Œ€ํ•œ ๊ณ ๋ ค ์—†์ด ํŠน์ • ๊ณต๊ฐ„์˜ ๋‚ด๋ถ€ ์†Œ๋“์ˆ˜์ค€ ๋ถ„ํฌ๋ฅผ ์ธก์ •ํ•œ ๊ฒƒ์œผ๋กœ, ์†Œ๋“์ˆ˜์ค€์„ ๋ฒ”์ฃผํ™”์‹œํ‚ค๋Š” ๋ฐฉ์‹๊ณผ ์†Œ๋“์ˆ˜์ค€์˜ ๋ถ„์‚ฐ์„ ์ธก์ •ํ•˜๋Š” ๋ฐฉ์‹์„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ณต๊ฐ„ ์ง€ํ‘œ๋Š” ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„์˜ ์ž…์ง€ ํŒจํ„ด์„ ๊ณ ๋ คํ•˜์—ฌ ํŠน์ • ์ง€์—ญ์—์„œ ์†Œ๋“์ˆ˜์ค€์„ ๊ธฐ์ค€์œผ๋กœ ๊ณต๊ฐ„์  ์ž๊ธฐ์ƒ๊ด€, ์ฆ‰ ๊ณต๊ฐ„์˜ ์ง‘์  ์ •๋„๋ฅผ ์ธก์ •ํ•˜๋Š” ๋ฐฉ์‹์ด๋‹ค. ์—ฌ๊ธฐ์„œ ๊ณต๊ฐ„ ์ง€ํ‘œ์˜ ๊ฒฝ์šฐ ๋ถ„์„์˜ ๊ธฐ๋ณธ๋‹จ์œ„๋กœ ์„ค์ •ํ•œ ๊ณต๊ฐ„ ๋ฒ”์œ„์ธ ๊ทผ๋ฆฐ ๋‚ด๋ถ€์—์„œ์˜ ์ง‘์  ์ •๋„์™€ ์™ธ๋ถ€ ๊ทผ๋ฆฐ๋ผ๋ฆฌ์˜ ์ง‘์  ์ •๋„๋ฅผ ๋ชจ๋‘ ์ธก์ •ํ•˜์˜€๋‹ค. ์ข…์†๋ณ€์ˆ˜์˜ ๊ฒฝ์šฐ ๊ณ„์ธต๊ฐˆ๋“ฑ๊ณผ ์‚ฌํšŒํ†ตํ•ฉ ์ด๋ก  ๊ฒ€ํ† ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ๋„์‹œ ๊ฑฐ์ฃผ๋ฏผ์˜ ๊ณ„์ธต๊ฐˆ๋“ฑ๊ณผ ๊ด€๋ จํ•˜์—ฌ ๊ฐœ์ธ์ด ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๊ณ„์ธต์˜์‹๊ณผ ํƒ€ ๊ณ„์ธต ์‚ฌ๋žŒ์— ๋Œ€ํ•œ ํƒœ๋„๋ฅผ ๋ณ€์ˆ˜๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์ด๋ฅผ ๊ณ„์ธต๊ฐˆ๋“ฑ๊ณผ ๊ด€๋ จ๋œ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹์œผ๋กœ ์ •์˜ํ•˜์˜€์œผ๋ฉฐ, ์ž์‹ ์˜ ๊ณ„์ธต์œ„์น˜์— ๋Œ€ํ•œ ์ฃผ๊ด€์  ํ‰๊ฐ€, ์‚ฌํšŒ์˜ ๊ณ„์ธต๊ตฌ์กฐ์— ๋Œ€ํ•œ ์ฃผ๊ด€์  ํ‰๊ฐ€, ํƒ€ ๊ณ„์ธต ์‚ฌ๋žŒ์— ๋Œ€ํ•œ ํƒœ๋„์˜ 3๊ฐ€์ง€๋กœ ๋ฒ”์ฃผํ™”์‹œ์ผฐ๋‹ค. ๋ณ€์ˆ˜ ์ธก์ •์„ ์œ„ํ•ด์„œ ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณต๊ณต์—์„œ ์ œ๊ณต๋˜๋Š” ํ†ต๊ณ„์ž๋ฃŒ์™€ ์„ค๋ฌธ์กฐ์‚ฌ ์ž๋ฃŒ๋ฅผ ๋ชจ๋‘ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ง€์—ญ๋ณ„ ์†Œ๋“์ˆ˜์ค€์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด์„œ 2010๋…„ ๊ฐ€๊ตฌํ†ตํ–‰์‹คํƒœ์กฐ์‚ฌ ์ž๋ฃŒ์™€ 2010๋…„ ์ธ๊ตฌ์ฃผํƒ์ด์กฐ์‚ฌ ์ง‘๊ณ„๊ตฌ ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๊ณ , ๊ฑฐ์ฃผ๋ฏผ์˜ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด์„œ ์„ค๋ฌธ์กฐ์‚ฌ ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์„ค๋ฌธ์กฐ์‚ฌ๋Š” ์„œ์šธ์‹œ 94๊ฐœ ํ–‰์ •๋™์˜ ๊ฑฐ์ฃผ๋ฏผ์„ ๋Œ€์ƒ์œผ๋กœ ์ง„ํ–‰ํ•˜์˜€๊ณ  ์ด 583๊ฐœ์˜ ์œ ํšจ์ƒ˜ํ”Œ ์ค‘์—์„œ ์ƒ์„ธํ•œ ๊ฑฐ์ฃผ์ง€ ์ฃผ์†Œ ์ •๋ณด๊ฐ€ ๋ถ€์กฑํ•˜๊ฑฐ๋‚˜ ํ˜„์žฌ ์‚ด๊ณ  ์žˆ๋Š” ํ–‰์ •๋™์—์„œ์˜ ๊ฑฐ์ฃผ๊ธฐ๊ฐ„์ด ์งง์€ ์ƒ˜ํ”Œ์„ ์ œ์™ธํ•˜์—ฌ, ์ตœ์ข…์ ์œผ๋กœ 357๋ช…์˜ ์ƒ˜ํ”Œ์„ ๋ถ„์„์— ํ™œ์šฉํ•˜์˜€๋‹ค. ๋ถ„์„๋ชจํ˜•์˜ ๊ฒฝ์šฐ ์ข…์†๋ณ€์ˆ˜์ธ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹ ๋ณ€์ˆ˜๊ฐ€ ๋ชจ๋‘ Likert ์ฒ™๋„๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธก์ •๋œ ์„ค๋ฌธ๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๊ธฐ ๋•Œ๋ฌธ์— ์ˆœ์„œํ˜• ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ธฐ๋ถ„์„ ๋ชจํ˜•์„ ์ ์šฉํ•˜์˜€๊ณ , ์ฃผ๊ด€์  ๊ณ„์ธต๋ณ„ ๊ธฐ์ค€์œผ๋กœ ์ธตํ™”๋ถ„์„๊ณผ t-test๋ฅผ ๋™์‹œ์— ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ฐœ์ธ์˜ ๊ณ„์ธต๊ฐˆ๋“ฑ ๊ด€๋ จ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹์— ๋Œ€ํ•œ ๋ถ„์„ ๊ฒฐ๊ณผ, ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ฑฐ์ฃผ์ง€์˜ ๋ถ„๋ฆฌ๊ฐ€ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์šฐ์„  ๊ฑฐ์ฃผ์ง€ ๊ทผ๋ฆฐ ๋‚ด๋ถ€์—์„œ์˜ ์†Œ๋“์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„์˜ ์ง‘์  ์ •๋„๊ฐ€ ์‹ฌํ• ์ˆ˜๋ก ๊ฑฐ์ฃผ์ž๋Š” ์ž์‹ ์˜ ์ฃผ๊ด€์  ๊ณ„์ธต์œ„์น˜๋ฅผ ๋‚ฎ๊ฒŒ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฐœ์ธ์˜ ์ฃผ๊ด€์  ๊ณ„์ธต์œ„์น˜ ํ‰๊ฐ€๊ฐ€ ๊ณ„์ธต์˜์‹์—์„œ ๊ฐ€์žฅ ๊ธฐ๋ณธ์ ์ด๊ณ  ์ค‘์š”ํ•œ ์š”์†Œ์ž„์„ ๊ฐ์•ˆํ•  ๋•Œ, ์ด ๊ฒฐ๊ณผ๋Š” ์†Œ๋“์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„์˜ ๋ถ„๋ฆฌ๊ฐ€ ๊ฑฐ์ฃผ๋ฏผ์˜ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ์ค€๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ์‚ฌํšŒ๊ณ„์ธต๊ตฌ์กฐ์— ๋Œ€ํ•œ ๊ฐœ์ธ์˜ ํ‰๊ฐ€ ์ธก๋ฉด์—์„œ๋Š” ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ฑฐ์ฃผ์ง€์˜ ๋ถ„๋ฆฌ ์ •๋„๊ฐ€ ์‹ฌํ• ์ˆ˜๋ก ๊ฑฐ์ฃผ๋ฏผ์ด ์šฐ๋ฆฌ ์‚ฌํšŒ์˜ ๊ณ„์ธต๊ตฌ์กฐ๋ฅผ ๋ถ€์ •์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ๋Š”, ๊ฑฐ์ฃผํ•˜๊ณ  ์žˆ๋Š” ๊ทผ๋ฆฐ ๋‚ด๋ถ€์—์„œ์˜ ์†Œ๋“์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„ ์ง‘์  ์ •๋„๊ฐ€ ์‹ฌํ• ์ˆ˜๋ก ๊ฐœ์ธ์ด ์šฐ๋ฆฌ ์‚ฌํšŒ์˜ ๋นˆ๋ถ€๊ฒฉ์ฐจ ์ •๋„๋‚˜ ๊ณ„์ธต ๋Œ€๋ฌผ๋ฆผ ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด ๋ถ€์ •์ ์œผ๋กœ ์ƒ๊ฐํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ฃผ๊ด€์  ๊ณ„์ธต์„ ๊ธฐ์ค€์œผ๋กœ ์ˆ˜ํ–‰ํ•œ ์ธตํ™”๋ถ„์„ ๊ฒฐ๊ณผ, ์ฃผ๊ด€์  ํ•˜์ธต๋ฏผ์ด ์ฃผ๊ด€์  ์ค‘ยท์ƒ์ธต๋ฏผ๋ณด๋‹ค ๊ฑฐ์ฃผ์ง€ ๋ถ„๋ฆฌ์— ์˜ํ•ด ๋” ์‹ฌํ•˜๊ฒŒ ์˜ํ–ฅ์„ ๋ฐ›๋Š”๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฐœ์ธ์˜ ํƒ€ ๊ณ„์ธต ์‚ฌ๋žŒ์— ๋Œ€ํ•œ ์‹ ๋ขฐ ์ธก๋ฉด์—์„œ๋„ ์†Œ๋“์— ๋”ฐ๋ฅธ ๊ฑฐ์ฃผ์ง€ ๋ถ„๋ฆฌ ์ •๋„๊ฐ€ ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฑฐ์ฃผํ•˜๋Š” ๊ทผ๋ฆฐ ๋‚ด๋ถ€ ์ฃผ๋ฏผ๋“ค์˜ ์†Œ๋“์ˆ˜์ค€ ๋ถ„ํฌ๊ฐ€ ๊ท ๋“ฑํ• ์ˆ˜๋ก, ์ฆ‰ ๋‹ค์–‘ํ•œ ์†Œ๋“์ˆ˜์ค€์˜ ์‚ฌ๋žŒ๋“ค์ด ๊ฑฐ์ฃผํ•˜๋Š” ๊ทผ๋ฆฐ์— ์‚ฌ๋Š” ์‚ฌ๋žŒ์ผ์ˆ˜๋ก ํƒ€ ๊ณ„์ธต ์‚ฌ๋žŒ์— ๋Œ€ํ•œ ์‹ ๋ขฐ๊ฐ€ ๋†’์€ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ์ฃผ๊ด€์  ๊ณ„์ธต์„ ๊ธฐ์ค€์œผ๋กœ ํ•œ ์ธตํ™”๋ถ„์„ ๊ฒฐ๊ณผ ๊ทธ๋Ÿฌํ•œ ์˜ํ–ฅ์€ ์ฃผ๊ด€์  ํ•˜์ธต๋ฏผ์—๊ฒŒ ํŠนํžˆ ๊ฐ•ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฐœ์ธ์˜ ํƒ€ ๊ณ„์ธต ์‚ฌ๋žŒ์— ๋Œ€ํ•œ ๊ด€์šฉ ์ธก๋ฉด์—์„œ๋Š” ์†Œ๋“์— ๋”ฐ๋ฅธ ๊ฑฐ์ฃผ์ง€ ๋ถ„๋ฆฌ ์ •๋„๊ฐ€ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ธตํ™”๋ถ„์„ ๊ฒฐ๊ณผ, ์ฃผ๊ด€์  ํ•˜์ธต๋ฏผ์—๊ฒŒ๋Š” ์˜ํ–ฅ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ฆ‰, ์†Œ๋“์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„์˜ ์ง‘์  ์ •๋„๊ฐ€ ์‹ฌํ•˜๊ฑฐ๋‚˜ ๊ฑฐ์ฃผ๋ฏผ ์†Œ๋“์ˆ˜์ค€์˜ ๋ถ„ํฌ๊ฐ€ ๊ท ๋“ฑํ•˜์ง€ ์•Š์€ ๊ทผ๋ฆฐ์— ์‚ฌ๋Š” ์ฃผ๊ด€์  ํ•˜์ธต๋ฏผ์€ ํƒ€ ๊ณ„์ธต ์‚ฌ๋žŒ์— ๋Œ€ํ•œ ๊ด€์šฉ ์ •๋„๊ฐ€ ๋‚ฎ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ํ•œํŽธ ์ฃผ๊ด€์  ๊ณ„์ธต์œ„์น˜๋ฅผ ๊ธฐ์ค€์œผ๋กœ ํ•˜์—ฌ ์ˆ˜ํ–‰ํ•œ t-test ๊ฒฐ๊ณผ, ์ฃผ๊ด€์  ๊ณ„์ธต์œ„์น˜๊ฐ€ ๋†’์„์ˆ˜๋ก ๊ณ„์ธต๊ฐˆ๋“ฑ ๊ด€๋ จ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹ ์ˆ˜์ค€์ด ๋†’์•„์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ์ฆ‰, ๊ธฐ๋ณธ์ ์œผ๋กœ ๊ฐœ์ธ์˜ ์ฃผ๊ด€์  ๊ณ„์ธต์œ„์น˜๋ฅผ ์ƒ์Šน์‹œํ‚ค๋Š” ๊ฒƒ์ด ๊ณ„์ธต๊ฐˆ๋“ฑ ๊ด€๋ จ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹์„ ๋†’์ด๋Š” ๋ฐ ํ•„์š”ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ฑฐ์ฃผ์ง€ ๋ถ„๋ฆฌ๋ฅผ ์˜ˆ๋ฐฉํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•จ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ์ด์ƒ์˜ ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•ด๋ณด๋ฉด, ๋„์‹œ์—์„œ์˜ ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ฑฐ์ฃผ์ง€ ๋ถ„๋ฆฌ๋Š” ๊ฑฐ์ฃผ๋ฏผ์˜ ๊ณ„์ธต๊ฐˆ๋“ฑ ๊ด€๋ จ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ , ๊ทธ๋Ÿฌํ•œ ์˜ํ–ฅ์ด ํŠนํžˆ ์ฃผ๊ด€์  ํ•˜์ธต๋ฏผ์—๊ฒŒ ํฌ๊ฒŒ ์ ์šฉ๋œ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ์ฃผ๋กœ ์ด๋ก ์  ๋…ผ์˜์— ๊ทธ์น˜๊ณ  ์žˆ์—ˆ๋˜, ๋„์‹œ์—์„œ์˜ ์†Œ๋“์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„์˜ ๋ถ„๋ฆฌ๊ฐ€ ์šฐ๋ฆฌ ์‚ฌํšŒ์˜ ๊ฐˆ๋“ฑ์„ ์œ ๋ฐœํ•˜๊ณ  ์‚ฌํšŒํ†ตํ•ฉ์„ ์ €ํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒฝ๊ณ ์™€ ์ฃผ์žฅ์„ ์‹ค์ฆ์ ์œผ๋กœ ๊ฒ€์ฆํ–ˆ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ๊ฐ–๋Š”๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ˜„์žฌ ์‹œํ–‰๋˜๊ณ  ์žˆ๋Š” ๋ฐฉ์‹์˜ ์‚ฌํšŒ์  ํ˜ผํ•ฉ ์ •์ฑ…์˜ ๋‹น์œ„์„ฑ์„ ํ™•์ธํ•˜๊ณ , ์ถ”๊ฐ€์ ์œผ๋กœ ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„์˜ ์ง‘์  ๋ฌธ์ œ์—๋„ ๊ด€์‹ฌ์„ ๊ธฐ์šธ์—ฌ์•ผ ํ•จ์„ ๋ฐํ˜€๋ƒˆ๋‹ค๋Š” ์ ์—์„œ ๋„์‹œ ๊ณต๊ฐ„ ์ธก๋ฉด์˜ ์ •์ฑ…์  ํ•จ์˜๋„ ์ด๋Œ์–ด๋ƒˆ๋‹ค๊ณ  ๋ณธ๋‹ค.I. ์„œ๋ก  1 1.1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1.1.1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.1.2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  5 1.2. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 6 1.2.1. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 6 1.2.2. ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ๊ณผ ๋ฐฉ๋ฒ• 7 II. ์ด๋ก ์  ๊ณ ์ฐฐ ๋ฐ ํ˜„ํ™ฉ 9 2.1. ๋„์‹œ๊ณต๊ฐ„์˜ ๋ถ„๋ฆฌ์™€ ๋Œ€์ฑ… 9 2.1.1. ๋„์‹œ๊ณต๊ฐ„์˜ ๋ถ„๋ฆฌ 9 2.1.2. ๋„์‹œ๊ณต๊ฐ„์˜ ๋ถ„๋ฆฌ ํ˜„ํ™ฉ ๋ฐ ๋Œ€์ฑ… 27 2.2. ๊ณ„์ธต๊ฐˆ๋“ฑ๊ณผ ์‚ฌํšŒํ†ตํ•ฉ์˜์‹ 38 2.2.1. ๊ณ„์ธต๊ฐˆ๋“ฑ๊ณผ ๊ณ„์ธต์˜์‹ 38 2.2.2. ์‚ฌํšŒํ†ตํ•ฉ์˜์‹ 40 2.3. ์†Œ๊ฒฐ 46 III. ๋ถ„์„์˜ ํ‹€ 49 3.1. ์—ฐ๊ตฌ๋ฌธ์ œ ์„ค์ • 49 3.1.1. ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  49 3.1.2. ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 54 3.1.3. ์—ฐ๊ตฌ๋ฌธ์ œ ์„ค์ • 56 3.2. ์—ฐ๊ตฌ ๋Œ€์ƒ์ง€ ๋ฐ ๋ถ„์„์ž๋ฃŒ 60 3.2.1. ์—ฐ๊ตฌ ๋Œ€์ƒ์ง€ ์„ ์ • 60 3.2.2. ๋ถ„์„์ž๋ฃŒ 61 3.3. ์ฃผ์š”๋ณ€์ˆ˜ ์ธก์ • ๋ฐฉ์‹ 67 3.3.1. ์†Œ๋“์ˆ˜์ค€์— ๋”ฐ๋ฅธ ๋ถ„๋ฆฌ ์ธก์ • ๋ฐฉ์‹ ์„ ์ • 67 3.3.2. ์†Œ๋“์ˆ˜์ค€ ์ถ”์ •์‹ ๋„์ถœ 70 3.4. ๋ถ„์„๋ชจํ˜• ์„ค์ • 74 3.4.1. ๋ณ€์ˆ˜ ๊ตฌ์„ฑ 74 3.4.2. ๋ถ„์„๋ชจํ˜• 85 IV. ๋ถ„์„ ๊ฒฐ๊ณผ 86 4.1. ๋„์‹œ๊ณต๊ฐ„์—์„œ ์†Œ๋“์— ๋”ฐ๋ฅธ ๋ถ„๋ฆฌ๊ฐ€ ๊ฐœ์ธ์˜ ์ฃผ๊ด€์  ๊ณ„์ธต ํ‰๊ฐ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 86 4.2. ๋„์‹œ๊ณต๊ฐ„์—์„œ ์†Œ๋“์— ๋”ฐ๋ฅธ ๋ถ„๋ฆฌ๊ฐ€ ๊ฐœ์ธ์˜ ์‚ฌํšŒ๊ณ„์ธต๊ตฌ์กฐ ์ธ์‹์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 90 4.3. ๋„์‹œ๊ณต๊ฐ„์—์„œ ์†Œ๋“์— ๋”ฐ๋ฅธ ๋ถ„๋ฆฌ๊ฐ€ ๊ฐœ์ธ์˜ ํƒ€ ๊ณ„์ธต์— ๋Œ€ํ•œ ํƒœ๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 97 4.3.1. ํƒ€ ๊ณ„์ธต์— ๋Œ€ํ•œ ์‹ ๋ขฐ ๋ชจํ˜• 97 4.3.2. ํƒ€ ๊ณ„์ธต์— ๋Œ€ํ•œ ๊ด€์šฉ ๋ชจํ˜• 103 4.4. ์†Œ๊ฒฐ 109 V. ๊ฒฐ๋ก  112 5.1. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์š”์•ฝ 112 5.2. ์—ฐ๊ตฌ ์˜์˜์™€ ์ •์ฑ…์  ํ•จ์˜ 115 5.3. ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ๊ณผ์ œ 117 ์ฐธ๊ณ ๋ฌธํ—Œ 119 ๋ถ€๋ก 129Docto

    Optimal Calibration for Parametric Jump Processes

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    DoctorAccording to numerous empirical evidences observed in option markets, it is clear that the celebrating Black-Scholes-Merton option pricing model can not explain the intrinsic properties of option prices in real markets such as the implied volatility smile behavior. To capture the smile effect many option pricing models or methods have been developed in a non-parametric and parametric way. In non-parametric approaches they do not rely on pre-assumed models but instead try to uncover/induce the model. There is a weak point with non-parametric approaches which it cannot applied to pricing path-dependent exotic options due to its lack of underlying dynamics.Recently in financial literature parametric methods, such as exponential Lยดevy models and affine jump-diffusion models, have been widely adopted as alternative models that explain stylized facts of asset returns and volatility smile effects of traded option prices. Hence if the parameters are calibrated reasonably the parametric models can be very powerful. Unfortunately the number of parameters is a lot and itโ€™s hard to estimate parameters from the information in financial market. To calibrate them we use cross-sectional data of option prices. Least-squaresense is usually employed to calibrate them in finance, although it is well-known ill-posed inverse problem. To conquer the ill-posed inverse problem we propose a derivative-free calibration method constrained by four observable statistical moments (mean, variance, skewness and kurtosis) from underlying time series and so-called multi-basin system which consists of three sequential phases to expedite the search for agood parameter set.To verify the performance of the proposed methods, we conduct simulations on some model-generated option prices data and real-world option market data. The simulation results show that the proposed methods fit the option ranges well and calibrate the parameter set ofexponential Lยดevy models and affine jump-diffusion models reasonably and robustly.In this thesis we also give a modularized summary of all the detailed equations relevant to all exponential Lยดevy models and affine jumpdiffusion models in a consistent way by using the unified notations
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