Analyzing the Distribution of Voting Power in Blockchain

Table of Links Abstract/Zusammenfassung Publications Acknowledgements CHAPTER 1: INTRODUCTION Introduction 1.1 Overview of thesis contributions 1.2 Thesis outline CHAPTER 2: BACKGROUND 2.1 Blockchains & smart contracts 2.2 Transaction prioritization norms 2.3 Transaction prioritization and contention transparency 2.4 Decentralized governance 2.5 Blockchain Scalability with Layer 2.0 Solutions CHAPTER 3. TRANSACTION PRIORITIZATION NORMS Transaction Prioritization Norms 3.1 Methodology 3.2 Analyzing norm adherence 3.3 Investigating norm violations 3.4 Dark-fee transactions 3.5 Concluding remarks CHAPTER 4. TRANSACTION PRIORITIZATION AND CONTENTION TRANSPARENCY Transaction Prioritization and Contention Transparency 4.1 Methodology 4.2 On contention transparency 4.3 On prioritization transparency 4.4 Concluding remarks CHAPTER 5. DECENTRALIZED GOVERNANCE Decentralized Governance 5.1 Methodology 5.2 Attacks on governance 5.3 Compound’s governance 5.4 Concluding remarks CHAPTER 6. RELATED WORK 6.1 Transaction prioritization norms 6.2 Transaction prioritization and contention transparency 6.3 Decentralized governance CHAPTER 7. DISCUSSION, LIMITATIONS & FUTURE WORK 7.1 Transaction ordering 7.2 Transaction transparency 7.3 Voting power distribution to amend smart contracts Conclusion Appendices APPENDIX A: Additional Analysis of Transactions Prioritization Norms APPENDIX B: Additional analysis of transactions prioritization and contention transparency APPENDIX C: Additional Analysis of Distribution of Voting Power Bibliography APPENDIX C: Additional Analysis of Distribution of Voting Power C.1 Compound proposals categorization We gathered data from Messari (Messari, 2023) to determine the categories, subcategories, and the level of importance associated with each Compound proposal. Figure C.1 shows the distribution of 101 executed Compound proposals across different categories and subcategories. We show the degree of importance for each proposal according to Messari divided into “low”, “medium”, “high”, and “very high”. As a result, a few proposals categorized as “Parameter Change” and “Security” demonstrate a high level of importance. Furthermore, proposals with the highest level of importance are found within the “Security” category, specifically within the “Mining and Validation” subcategory. This refers to the proposal 64 that was created to fix a bug introduced by proposal #62 (Loewen, 2021a,b). The majority of the proposals (61 proposals, accounting for 60.4%) are related to “Parameter Change” followed by “Team and Operations” and “Token Supply” accounting for 10 (9.9%) each, and “Governance” with 7 (6.93%) proposals. According to the level of importance reported by Messari, out of the total of 101 executed proposals, 51 proposals (50.5%) are classified as low importance, 46 proposals (45.54%) as medium importance, 3 proposals as high importance, and 1 proposal as very high importance. C.2 Filtering events to construct our Compound data Set This section describes the details required to filter and collect transactions data that triggered events of interest from any smart contract on the Ethereum blockchain. Before creating a filter, we need the address of our target contract and its Application Binary Interface (ABI). The ABI is a JSON file that specifies the functions available in the contract, their signatures, and the available events. We can retrieve this information by calling the Etherscan API (Etherscan, 2023a). Once we have the contract address and ABI, we can create a filter to track the contract’s activity on the Ethereum blockchain using an important Python library for interacting with Ethereum nodes called Web3.py (web3.py team, 2022) to facilitate the communication with our node’s API. The Web3.py library provides a filtering function called createFilter. This function can be used to filter transactions that triggered events of interest from a specific contract within a range of block numbers. We use this function to efficiently collect all transactions that triggered these events from the Compound (Leshner and Hayes, 2019) smart contract. C.3 Inferring wallet addresses ownership We aim to identify the ownership of public wallet addresses on the Ethereum blockchain. Due to the inherent anonymity of blockchain addresses, this proves to be a challenging task as we can only know the owners of an address if the owner chooses to disclose it. However, popular blockchain explorers such as Etherscan (Etherscan, 2023b) often provide information on the top holders of specific cryptocurrencies, which allows us to partially overcome this obstacle. Then, we first obtained the lists of the top 10,000 Ether holders from which there are 290 (2.9%) identified addresses and the top 1000 COMP holders from which there are 82 (8.2%) identified addresses from Etherscan. By comparing these lists to our data set, we were able to identify most of the top COMP holder addresses in our sample. However, this method did not work for the top delegated accounts, as most of them were not included in the list of top COMP holders on Etherscan. This means that most of the delegated accounts does not hold many tokens. Further, we also used the list of top 100 delegated Compound addresses by voting weight available on the Compound website (Compound Labs, Inc., 2022b) from which there are 66 identified addresses. Furthermore, to extend the available identified addresses in our analysis, we obtained the addresses of 2783 identified users from the Sybil-List (Sybil, 2023b), a project maintained by Uniswap that uses cryptographic proofs to verify wallet addresses ownership. By combining the identified addresses from both sources, we were able to obtain the ownership of 3191 inferred public wallet addresses to use in our analysis. We were able to infer 114 (3.41%) out of the 3341 unique addresses in our data set. Considering the top 10 most powerful voters for each proposal (refer to Figure C.3 in §C.5), we were able to infer 67 (50.37%) of the 133 unique addresses. Overall, our methodology allowed us to partially overcome the anonymity of public wallet addresses on the Ethereum blockchain and shed light on the ownership of these addresses in our data set. Finally, as an entity can control more than one address, we grouped the addresses we identified belonging to the same entity together to conduct our analysis. C.4 Types of existing governance protocols There are various smart contract applications that utilize decentralized governance protocols for decision-making, including those for lending, decentralized exchanges (DEXes), and stablecoins, among others. An example of such protocols can be found on the Ethereum blockchain, where a number of these applications are available. We have selected some of the most protocols that use decentralized governance for decisionmaking. Table C.1 presents 8 protocols, including Maker Executive and Maker Pooling, which are part of the MakerDAO (MakerDAO, 2023) stablecoin protocol responsible for the DAI token. These protocols use decentralized governance mechanisms, and we characterize them based on whether their votes are cast on- or off-chain, the delegation methods they use, how they aggregate the votes, and how the proposal outcome take effect. C.5 How voters cast their votes This section examines how each of the top-10 voters of Compound and Uniswap cast their votes. Some proposals may not have received any votes if they were cancelled before the voting period began. See §5.3.2 for details. Figure C.3 shows how each of the top-10 voters cast their votes in each of the 126 (94.74%) out of 133 Compound proposals. Figure C.4 shows the all votes cast in chronological order per proposal. On average, voters took 1.4 days (with a standard deviation of 0.95 and a median of 1.34 days) to cast their votes after the voting period began. C.6 Time until reaching the quorum in Compound For a proposal to pass, it must receive a majority of in favor votes and at least 400,000 (4%) votes in favor from the total supply of Compound tokens. This minimum number of in favor votes is referred to as the quorum and is defined by the Compound Governor Bravo contract. We analyzed how long it takes for these proposals to reach the required quorum. Figure C.2 shows the number of days it took each of the evaluated Compound proposals to reach the quorum. On average, it takes 1.64 days with a standard deviation of 0.72 days for the proposals to reach the quorum. The cumulative distribution function of our results, where 32% take more than 2 days to reach the quorum. The shortest and longest time it took was 0.11 and 3 days, respectively. Bibliography 0x Protocol (2022). 0x: Powering the decentralized exchange of tokens on Ethereum. https://www.0x.org. AAVE (2022). AAVE - Open Source Liquidity Pool. https://aave.com. AAVE (2023). 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Author: (1) Johnnatan Messias Peixoto Afonso This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract/Zusammenfassung Abstract/Zusammenfassung Publications Publications Acknowledgements Acknowledgements CHAPTER 1: INTRODUCTION CHAPTER 1: INTRODUCTION Introduction 1.1 Overview of thesis contributions 1.2 Thesis outline Introduction 1.1 Overview of thesis contributions 1.2 Thesis outline Introduction Introduction 1.1 Overview of thesis contributions 1.1 Overview of thesis contributions 1.2 Thesis outline 1.2 Thesis outline CHAPTER 2: BACKGROUND CHAPTER 2: BACKGROUND 2.1 Blockchains & smart contracts 2.1 Blockchains & smart contracts 2.2 Transaction prioritization norms 2.2 Transaction prioritization norms 2.3 Transaction prioritization and contention transparency 2.3 Transaction prioritization and contention transparency 2.4 Decentralized governance 2.4 Decentralized governance 2.5 Blockchain Scalability with Layer 2.0 Solutions 2.5 Blockchain Scalability with Layer 2.0 Solutions CHAPTER 3. TRANSACTION PRIORITIZATION NORMS CHAPTER 3. TRANSACTION PRIORITIZATION NORMS Transaction Prioritization Norms 3.1 Methodology 3.2 Analyzing norm adherence 3.3 Investigating norm violations 3.4 Dark-fee transactions 3.5 Concluding remarks Transaction Prioritization Norms 3.1 Methodology 3.2 Analyzing norm adherence 3.3 Investigating norm violations 3.4 Dark-fee transactions 3.5 Concluding remarks Transaction Prioritization Norms Transaction Prioritization Norms 3.1 Methodology 3.1 Methodology 3.2 Analyzing norm adherence 3.2 Analyzing norm adherence 3.3 Investigating norm violations 3.3 Investigating norm violations 3.4 Dark-fee transactions 3.4 Dark-fee transactions 3.5 Concluding remarks 3.5 Concluding remarks CHAPTER 4. TRANSACTION PRIORITIZATION AND CONTENTION TRANSPARENCY CHAPTER 4. TRANSACTION PRIORITIZATION AND CONTENTION TRANSPARENCY Transaction Prioritization and Contention Transparency 4.1 Methodology 4.2 On contention transparency 4.3 On prioritization transparency 4.4 Concluding remarks Transaction Prioritization and Contention Transparency 4.1 Methodology 4.2 On contention transparency 4.3 On prioritization transparency 4.4 Concluding remarks Transaction Prioritization and Contention Transparency Transaction Prioritization and Contention Transparency 4.1 Methodology 4.1 Methodology 4.2 On contention transparency 4.2 On contention transparency 4.3 On prioritization transparency 4.3 On prioritization transparency 4.4 Concluding remarks 4.4 Concluding remarks CHAPTER 5. DECENTRALIZED GOVERNANCE CHAPTER 5. DECENTRALIZED GOVERNANCE Decentralized Governance 5.1 Methodology 5.2 Attacks on governance 5.3 Compound’s governance 5.4 Concluding remarks Decentralized Governance 5.1 Methodology 5.2 Attacks on governance 5.3 Compound’s governance 5.4 Concluding remarks Decentralized Governance Decentralized Governance 5.1 Methodology 5.1 Methodology 5.2 Attacks on governance 5.2 Attacks on governance 5.3 Compound’s governance 5.3 Compound’s governance 5.4 Concluding remarks 5.4 Concluding remarks CHAPTER 6. RELATED WORK CHAPTER 6. RELATED WORK 6.1 Transaction prioritization norms 6.1 Transaction prioritization norms 6.2 Transaction prioritization and contention transparency 6.2 Transaction prioritization and contention transparency 6.3 Decentralized governance 6.3 Decentralized governance CHAPTER 7. DISCUSSION, LIMITATIONS & FUTURE WORK CHAPTER 7. DISCUSSION, LIMITATIONS & FUTURE WORK 7.1 Transaction ordering 7.1 Transaction ordering 7.2 Transaction transparency 7.2 Transaction transparency 7.3 Voting power distribution to amend smart contracts 7.3 Voting power distribution to amend smart contracts Conclusion Conclusion Appendices Appendices APPENDIX A: Additional Analysis of Transactions Prioritization Norms APPENDIX A: Additional Analysis of Transactions Prioritization Norms APPENDIX B: Additional analysis of transactions prioritization and contention transparency APPENDIX B: Additional analysis of transactions prioritization and contention transparency APPENDIX C: Additional Analysis of Distribution of Voting Power APPENDIX C: Additional Analysis of Distribution of Voting Power Bibliography Bibliography APPENDIX C: Additional Analysis of Distribution of Voting Power C.1 Compound proposals categorization We gathered data from Messari (Messari, 2023) to determine the categories, subcategories, and the level of importance associated with each Compound proposal. Figure C.1 shows the distribution of 101 executed Compound proposals across different categories and subcategories. We show the degree of importance for each proposal according to Messari divided into “low”, “medium”, “high”, and “very high”. As a result, a few proposals categorized as “Parameter Change” and “Security” demonstrate a high level of importance. Furthermore, proposals with the highest level of importance are found within the “Security” category, specifically within the “Mining and Validation” subcategory. This refers to the proposal 64 that was created to fix a bug introduced by proposal #62 (Loewen, 2021a,b). The majority of the proposals (61 proposals, accounting for 60.4%) are related to “Parameter Change” followed by “Team and Operations” and “Token Supply” accounting for 10 (9.9%) each, and “Governance” with 7 (6.93%) proposals. According to the level of importance reported by Messari, out of the total of 101 executed proposals, 51 proposals (50.5%) are classified as low importance, 46 proposals (45.54%) as medium importance, 3 proposals as high importance, and 1 proposal as very high importance. C.2 Filtering events to construct our Compound data Set This section describes the details required to filter and collect transactions data that triggered events of interest from any smart contract on the Ethereum blockchain. Before creating a filter, we need the address of our target contract and its Application Binary Interface (ABI). The ABI is a JSON file that specifies the functions available in the contract, their signatures, and the available events. We can retrieve this information by calling the Etherscan API (Etherscan, 2023a). Once we have the contract address and ABI, we can create a filter to track the contract’s activity on the Ethereum blockchain using an important Python library for interacting with Ethereum nodes called Web3.py (web3.py team, 2022) to facilitate the communication with our node’s API. The Web3.py library provides a filtering function called createFilter . This function can be used to filter transactions that triggered events of interest from a specific contract within a range of block numbers. We use this function to efficiently collect all transactions that triggered these events from the Compound (Leshner and Hayes, 2019) smart contract. createFilter C.3 Inferring wallet addresses ownership We aim to identify the ownership of public wallet addresses on the Ethereum blockchain. Due to the inherent anonymity of blockchain addresses, this proves to be a challenging task as we can only know the owners of an address if the owner chooses to disclose it. However, popular blockchain explorers such as Etherscan (Etherscan, 2023b) often provide information on the top holders of specific cryptocurrencies, which allows us to partially overcome this obstacle. Then, we first obtained the lists of the top 10,000 Ether holders from which there are 290 (2.9%) identified addresses and the top 1000 COMP holders from which there are 82 (8.2%) identified addresses from Etherscan. By comparing these lists to our data set, we were able to identify most of the top COMP holder addresses in our sample. However, this method did not work for the top delegated accounts, as most of them were not included in the list of top COMP holders on Etherscan. This means that most of the delegated accounts does not hold many tokens. Further, we also used the list of top 100 delegated Compound addresses by voting weight available on the Compound website (Compound Labs, Inc., 2022b) from which there are 66 identified addresses. Furthermore, to extend the available identified addresses in our analysis, we obtained the addresses of 2783 identified users from the Sybil-List (Sybil, 2023b), a project maintained by Uniswap that uses cryptographic proofs to verify wallet addresses ownership. By combining the identified addresses from both sources, we were able to obtain the ownership of 3191 inferred public wallet addresses to use in our analysis. We were able to infer 114 (3.41%) out of the 3341 unique addresses in our data set. Considering the top 10 most powerful voters for each proposal (refer to Figure C.3 in §C.5), we were able to infer 67 (50.37%) of the 133 unique addresses. Overall, our methodology allowed us to partially overcome the anonymity of public wallet addresses on the Ethereum blockchain and shed light on the ownership of these addresses in our data set. Finally, as an entity can control more than one address, we grouped the addresses we identified belonging to the same entity together to conduct our analysis. C.4 Types of existing governance protocols There are various smart contract applications that utilize decentralized governance protocols for decision-making, including those for lending, decentralized exchanges (DEXes), and stablecoins, among others. An example of such protocols can be found on the Ethereum blockchain, where a number of these applications are available. We have selected some of the most protocols that use decentralized governance for decisionmaking. Table C.1 presents 8 protocols, including Maker Executive and Maker Pooling, which are part of the MakerDAO (MakerDAO, 2023) stablecoin protocol responsible for the DAI token. These protocols use decentralized governance mechanisms, and we characterize them based on whether their votes are cast on- or off-chain, the delegation methods they use, how they aggregate the votes, and how the proposal outcome take effect. C.5 How voters cast their votes This section examines how each of the top-10 voters of Compound and Uniswap cast their votes. Some proposals may not have received any votes if they were cancelled before the voting period began. See §5.3.2 for details. Figure C.3 shows how each of the top-10 voters cast their votes in each of the 126 (94.74%) out of 133 Compound proposals. Figure C.4 shows the all votes cast in chronological order per proposal. On average, voters took 1.4 days (with a standard deviation of 0.95 and a median of 1.34 days) to cast their votes after the voting period began. 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Author: (1) Johnnatan Messias Peixoto Afonso Author : Author (1) Johnnatan Messias Peixoto Afonso This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv