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題名 礦工行為與區塊鏈網絡性能的動態模擬分析
Dynamic Simulation of Miner Behaviors and Blockchain System Performance
作者 黃文志
Huang, Wen-Zhi
貢獻者 莊皓鈞
Chuang, Hao-Chun
黃文志
Huang, Wen-Zhi
關鍵詞 比特幣
區塊鏈
礦工行為
系統動力學
Bitcoins
Blockchain
Miner behaviors
System Dynamics
日期 2023
上傳時間 3-Oct-2023 10:45:01 (UTC+8)
摘要 隨著區塊鏈技術應用範疇逐漸擴大，其技術原理與應用概念日益受到全球政 府與企業的重視。特別是比特幣，作為區塊鏈最早的應用之一，已經吸引了大量公 眾關注。本研究旨在以比特幣這一虛擬貨幣為研究對象，深入探討具有不同風險態 度的礦工對比特幣網絡的性能所產生的影響。為此，我們將採用系統動力學方法對 相關問題進行建模與分析。
根據比特幣網絡的特性，我們可以知道比特幣價格短期與中長期波動會影響 礦工在網路停留的時間與礦工對報酬率的敏感度，而這兩者會改變比特幣網絡中 礦工的數量，最終對比特幣網絡的哈希值產生影響。依照上述脈絡，本研究首先建 構了一個基於比特幣網絡真實情境的系統動力學模型，並將比特幣礦工依照風險 態度的不同而區分成三大種類 (1.) 風險趨避礦工 (2.) 風險中立礦工 (3.) 風險偏 好礦工，探討這三種不同風險態度礦工會如何被比特幣價格波動所影響，進而對比 特幣網絡中的哈希值產生變動。
通過模擬實驗，本研究發現三種不同風險態度礦工對比特幣網絡的運作有顯 著的影響。風險趨避礦工可能在比特幣價格大幅下跌時而選擇降低挖礦時間，因而 影響比特幣網絡中礦工數量，導致算力降低;而風險偏好礦工則較不受到比特幣價 格下跌的影響，反而在比特幣價格上漲時，還會增加待在網絡中挖礦的時間，進而 增加比特幣網絡中礦工數量，提升算力;而風險中立礦工在比特幣網絡中礦工數量 中則是相對穩定。此外，本研究還發現即便是抱有相同風險態度礦工，在面臨短期 和中長期價格波動的情況下，礦工數量及哈希值的影響程度也會有差異。
綜合來看，本研究對於理解比特幣等區塊鏈網絡中礦工行為的影響機制以及 制定相應的政策措施具有重要意義，有助於幫助人們理解礦工行為在比特幣網絡 扮演的角色，甚至幫助推動區塊鏈技術的健康發展和應用。未來研究可進一步考慮 其他區塊鏈系統以及不同的礦工激勵機制，以擴大本研究的普適性和實用性。
As the application of blockchain continue to expand, its technology and concepts are increasingly receiving attention from governments and enterprises worldwide. Particularly, Bitcoin, as one of the earliest and successful blockchain applications, has gained significant public interests. This paper will discuss the impact of miners’ different risk attitudes on the Bitcoin network performance, using system dynamics for modeling and analyzing.
Based on the characteristics of the Bitcoin network, it is clear that short-term and medium-long term fluctuations in Bitcoin prices will affect miners’ residence time and their sensitivity to ROI. These two factors will change the number of miners in the Bitcoin network, ultimately affecting the hash rate of the Bitcoin network. With this concept, this study first constructs a system dynamics model based on the actual scenario of the Bitcoin network and classifies Bitcoin miners into three major categories according to their different risk attitudes: (1) risk-averse miners, (2) risk-neutral miners, and (3) risk-seeking miners. The study explores how these three types of miners with different risk attitudes are affected by fluctuations in Bitcoin prices, thereby causing changes in the hash rate in the Bitcoin network.
Through simulation experiments, this study finds that the three types of miners with different risk attitudes have a significant impact on the Bitcoin network. Risk-averse miners may choose to reduce their mining time if Bitcoin price plummet, thus affecting the number of miners in the network and leading to a decrease in hash rate. On the other hand, risk-seeking miners are less affected by the decline in Bitcoin price; instead, if Bitcoin price rise, they will increase their time spent on mining significantly, resulting in a higher number of miners and an increased hash rate. Meanwhile, the number of risk- neutral miners in the bitcoin network remain relatively stable. On top of that, we also find that even for miners with the same risk attitudes, the extent of the impact on the number of miners and the hash rate varies when facing short-term and medium-long term price fluctuations.
This study is of great magnitude for understanding the impact mechanisms of miner behaviors on Bitcoin network. It helps people understand the role of miner behaviors in the Bitcoin network and even promotes the healthy development and application of blockchain technology. Future research can further consider other blockchain systems and different miner incentive mechanisms to increase the universality and practicality of this study.
參考文獻 Bissias, G., Levine, B.N., & Thibodeau, D. (2018). Using Economic Risk to Model Miner Hash Rate Allocation in Cryptocurrencies, Lecture Notes in Computer Science, 11025, 155–172.
Fantazzini, D. & Kolodin, N. (2020). Does the Hashrate Affect the Bitcoin Price? Journal of Risk and Financial Management. 2020, 13(11), 263.
Forrester, J.W. (1993). System Dynamics and the Lessons of 35 Years. In: De Greene, K.B. (eds) A Systems-Based Approach to Policymaking. Springer, Boston, MA.
Gopalakrishnan, V. (2022). Modeling the Trajectory of Bitcoin using System Dynamics, (Master’s thesis). System Design and Management Program, Massachusetts Institute of Technology.
Härdle, W.K., Harvey, C.R. and Reule, R.C.G. (2020). Understanding Cryptocurrencies, Journal of Financial Econometrics. 18(2), 181-208.
Kroll, J.A., Davey, I.C., & Felten, E.W. (2013). The Economics of Bitcoin Mining, or Bitcoin in the Presence of Adversaries. The 12th Workshop on the Economics of Information Security, Washington DC, 11-12 June 2013.
Kubal, J. & Kristoufek, L. (2022). Exploring the relationship between Bitcoin price and network’s hashrate within endogenous system, International Review of Financial Analysis. Elsevier, 84, 102375.
Lasi, D. & Saul, L. (2020). A System Dynamics Model of Bitcoin: Mining as an Efficient Market and the Possibility of ‘Peak Hash’, Applied Economics and Finance, 7(4), 78-88.
Li, K., Liu, Y., Wan, H. & Huang, Y. (2021). A discrete-event simulation model for the
Bitcoin blockchain network with strategic miners and mining pool managers,
Computers & Operations Research. 134, 105365.
Mahmoodi, S., Jalaee, S.A. & Sadeghi, Z. (2022). Investigation of Environmental Effects of Bitcoin Mining Using System Dynamics Method, (Doctoral thesis). Shahid Bahonar University. Majakivi, A. (2019). Modeling the Bitcoin Ecosystem, (Master’s thesis). Aalto University. School of Electrical Engineering.
Meadows, D.H., Meadows, D.L., Randers, J., Behrens, W.W. (1972). The limits of growth.:Potomac Associates.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
110363074
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110363074
資料類型 thesis
dc.contributor.advisor 莊皓鈞zh_TW
dc.contributor.advisor Chuang, Hao-Chunen_US
dc.contributor.author (Authors) 黃文志zh_TW
dc.contributor.author (Authors) Huang, Wen-Zhien_US
dc.creator (作者) 黃文志zh_TW
dc.creator (作者) Huang, Wen-Zhien_US
dc.date (日期) 2023en_US
dc.date.accessioned 3-Oct-2023 10:45:01 (UTC+8)-
dc.date.available 3-Oct-2023 10:45:01 (UTC+8)-
dc.date.issued (上傳時間) 3-Oct-2023 10:45:01 (UTC+8)-
dc.identifier (Other Identifiers) G0110363074en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/147735-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所(MBA學位學程)zh_TW
dc.description (描述) 110363074zh_TW
dc.description.abstract (摘要) 隨著區塊鏈技術應用範疇逐漸擴大，其技術原理與應用概念日益受到全球政 府與企業的重視。特別是比特幣，作為區塊鏈最早的應用之一，已經吸引了大量公 眾關注。本研究旨在以比特幣這一虛擬貨幣為研究對象，深入探討具有不同風險態 度的礦工對比特幣網絡的性能所產生的影響。為此，我們將採用系統動力學方法對 相關問題進行建模與分析。
根據比特幣網絡的特性，我們可以知道比特幣價格短期與中長期波動會影響 礦工在網路停留的時間與礦工對報酬率的敏感度，而這兩者會改變比特幣網絡中 礦工的數量，最終對比特幣網絡的哈希值產生影響。依照上述脈絡，本研究首先建 構了一個基於比特幣網絡真實情境的系統動力學模型，並將比特幣礦工依照風險 態度的不同而區分成三大種類 (1.) 風險趨避礦工 (2.) 風險中立礦工 (3.) 風險偏 好礦工，探討這三種不同風險態度礦工會如何被比特幣價格波動所影響，進而對比 特幣網絡中的哈希值產生變動。
通過模擬實驗，本研究發現三種不同風險態度礦工對比特幣網絡的運作有顯 著的影響。風險趨避礦工可能在比特幣價格大幅下跌時而選擇降低挖礦時間，因而 影響比特幣網絡中礦工數量，導致算力降低;而風險偏好礦工則較不受到比特幣價 格下跌的影響，反而在比特幣價格上漲時，還會增加待在網絡中挖礦的時間，進而 增加比特幣網絡中礦工數量，提升算力;而風險中立礦工在比特幣網絡中礦工數量 中則是相對穩定。此外，本研究還發現即便是抱有相同風險態度礦工，在面臨短期 和中長期價格波動的情況下，礦工數量及哈希值的影響程度也會有差異。
綜合來看，本研究對於理解比特幣等區塊鏈網絡中礦工行為的影響機制以及 制定相應的政策措施具有重要意義，有助於幫助人們理解礦工行為在比特幣網絡 扮演的角色，甚至幫助推動區塊鏈技術的健康發展和應用。未來研究可進一步考慮 其他區塊鏈系統以及不同的礦工激勵機制，以擴大本研究的普適性和實用性。		zh_TW
dc.description.abstract (摘要) As the application of blockchain continue to expand, its technology and concepts are increasingly receiving attention from governments and enterprises worldwide. Particularly, Bitcoin, as one of the earliest and successful blockchain applications, has gained significant public interests. This paper will discuss the impact of miners’ different risk attitudes on the Bitcoin network performance, using system dynamics for modeling and analyzing.
Based on the characteristics of the Bitcoin network, it is clear that short-term and medium-long term fluctuations in Bitcoin prices will affect miners’ residence time and their sensitivity to ROI. These two factors will change the number of miners in the Bitcoin network, ultimately affecting the hash rate of the Bitcoin network. With this concept, this study first constructs a system dynamics model based on the actual scenario of the Bitcoin network and classifies Bitcoin miners into three major categories according to their different risk attitudes: (1) risk-averse miners, (2) risk-neutral miners, and (3) risk-seeking miners. The study explores how these three types of miners with different risk attitudes are affected by fluctuations in Bitcoin prices, thereby causing changes in the hash rate in the Bitcoin network.
Through simulation experiments, this study finds that the three types of miners with different risk attitudes have a significant impact on the Bitcoin network. Risk-averse miners may choose to reduce their mining time if Bitcoin price plummet, thus affecting the number of miners in the network and leading to a decrease in hash rate. On the other hand, risk-seeking miners are less affected by the decline in Bitcoin price; instead, if Bitcoin price rise, they will increase their time spent on mining significantly, resulting in a higher number of miners and an increased hash rate. Meanwhile, the number of risk- neutral miners in the bitcoin network remain relatively stable. On top of that, we also find that even for miners with the same risk attitudes, the extent of the impact on the number of miners and the hash rate varies when facing short-term and medium-long term price fluctuations.
This study is of great magnitude for understanding the impact mechanisms of miner behaviors on Bitcoin network. It helps people understand the role of miner behaviors in the Bitcoin network and even promotes the healthy development and application of blockchain technology. Future research can further consider other blockchain systems and different miner incentive mechanisms to increase the universality and practicality of this study.		en_US
dc.description.tableofcontents 第一章 緒論 1
第二章 比特幣網絡與系統動力學 7
第三章 模型建置 11
第四章 模型分析與結果 24
第五章 結論與未來展望 32
參考書目 35		zh_TW
dc.format.extent 1150389 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110363074en_US
dc.subject (關鍵詞) 比特幣zh_TW
dc.subject (關鍵詞) 區塊鏈zh_TW
dc.subject (關鍵詞) 礦工行為zh_TW
dc.subject (關鍵詞) 系統動力學zh_TW
dc.subject (關鍵詞) Bitcoinsen_US
dc.subject (關鍵詞) Blockchainen_US
dc.subject (關鍵詞) Miner behaviorsen_US
dc.subject (關鍵詞) System Dynamicsen_US
dc.title (題名) 礦工行為與區塊鏈網絡性能的動態模擬分析zh_TW
dc.title (題名) Dynamic Simulation of Miner Behaviors and Blockchain System Performanceen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Bissias, G., Levine, B.N., & Thibodeau, D. (2018). Using Economic Risk to Model Miner Hash Rate Allocation in Cryptocurrencies, Lecture Notes in Computer Science, 11025, 155–172.
Fantazzini, D. & Kolodin, N. (2020). Does the Hashrate Affect the Bitcoin Price? Journal of Risk and Financial Management. 2020, 13(11), 263.
Forrester, J.W. (1993). System Dynamics and the Lessons of 35 Years. In: De Greene, K.B. (eds) A Systems-Based Approach to Policymaking. Springer, Boston, MA.
Gopalakrishnan, V. (2022). Modeling the Trajectory of Bitcoin using System Dynamics, (Master’s thesis). System Design and Management Program, Massachusetts Institute of Technology.
Härdle, W.K., Harvey, C.R. and Reule, R.C.G. (2020). Understanding Cryptocurrencies, Journal of Financial Econometrics. 18(2), 181-208.
Kroll, J.A., Davey, I.C., & Felten, E.W. (2013). The Economics of Bitcoin Mining, or Bitcoin in the Presence of Adversaries. The 12th Workshop on the Economics of Information Security, Washington DC, 11-12 June 2013.
Kubal, J. & Kristoufek, L. (2022). Exploring the relationship between Bitcoin price and network’s hashrate within endogenous system, International Review of Financial Analysis. Elsevier, 84, 102375.
Lasi, D. & Saul, L. (2020). A System Dynamics Model of Bitcoin: Mining as an Efficient Market and the Possibility of ‘Peak Hash’, Applied Economics and Finance, 7(4), 78-88.
Li, K., Liu, Y., Wan, H. & Huang, Y. (2021). A discrete-event simulation model for the
Bitcoin blockchain network with strategic miners and mining pool managers,
Computers & Operations Research. 134, 105365.
Mahmoodi, S., Jalaee, S.A. & Sadeghi, Z. (2022). Investigation of Environmental Effects of Bitcoin Mining Using System Dynamics Method, (Doctoral thesis). Shahid Bahonar University. Majakivi, A. (2019). Modeling the Bitcoin Ecosystem, (Master’s thesis). Aalto University. School of Electrical Engineering.
Meadows, D.H., Meadows, D.L., Randers, J., Behrens, W.W. (1972). The limits of growth.:Potomac Associates.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.		zh_TW