In the Ethereum mempool, these apex predators take the form of “arbitrage bots.” Arbitrage bots monitor pending transactions and attempt to exploit profitable opportunities created by them. No white hat knows more about these bots than Phil Daian, the smart contract researcher who, along with his colleagues, wrote the Flash Boys 2.0 paper and coined the term “miner extractable value” (MEV).Phil once told me about a cosmic horror that he called a “generalized frontrunner.” Arbitrage bots typically look for specific types of transactions in the mempool (such a DEX trade or an oracle update) and try to frontrun them according to a predetermined algorithm. Generalized frontrunners look for any transaction that they could profitably frontrun by copying it and replacing addresses with their own. They can even execute the transaction and copy profitable internal transactions generated by its execution trace.
Bitcoin and its users employ a variety of obfuscation techniques to increase their financial privacy. We visualize a representative selection of these techniques in Figure 1 based on their time of invention/creation and our assessment of their similarity to obfuscation vs. cryptography. We make several observations. First, techniques used in Bitcoin predominantly fall into obfuscation, with stronger techniques being used exclusively in alternative cryptocurrencies (altcoins). Second, there is a trend towards stronger techniques over time, perhaps due to a growing interest in privacy and to the greater difficulty of developing cryptographic techniques. Third, obfuscation techniques proposed at later points in time are seeing less adoption, arguably a result of their increased complexity and need for coordination among participants (Möser & Böhme 2017).
When Satoshi Nakamoto designed the Bitcoin protocol, he had the insight to include the notion of transaction fees. These fees incentivized miners to include transactions into blocks. But initially, Bitcoin did not have, in any meaningful sense, a fee market.A large portion of early Bitcoin transactions were completely free up until 2013 (blue in the above chart). Wallet developers eventually hard-coded tiny fixed fees into their clients, thought of as donations to miners. At first these fees defaulted to 0.1 BTC, but they were driven down as the Bitcoin price rose.
There has recently been a lot of interest in using cryptoeconomic or token-based techniques for fighting spam, maintaining registries, identifying fraudulent ICOs, reducing manipulability of upvoting, etc etc. However, this is an area where it is easy to create something very exploitable, or fail to achieve one’s goals, by building the application in the wrong way.Cryptoeconomics in social media has unique challenges; particularly: The inherent subjectiveness of judging the quality or suitability of a given message Rampant speaker/listener fault ambiguities 8 The public-good nature of internet content, making it difficult to incentivize The inability of a blockchain to know what happened “in the real world”, or make any measurements of the real world (with limited exceptions, eg. mining hashpower)However, there are ways to design primitives that sidestep these issues in different ways.This list is an ongoing work in progress.
At a fundamental level, blockchains are composed of multiple distinct layers, similar to other technology protocols like the internet paradigm (Link, Network, Internet, Transport, Application). Here, we present a framework of the layers that compose blockchains. The layers are defined such that each layer depends on the one(s) below it. Here, we discuss what each layer provides as opposed to how each layer is implemented.
In this brief contribution, I distinguish between code-driven and data-driven regulation as novel instantiations of legal regulation. Before moving deeper into data-driven regulation, I explain the difference between law and regulation, and the relevance of such a difference for the rule of law. I discuss artificial legal intelligence (ALI) as a means to enable quantified legal prediction and argumentation mining which are both based on machine learning. This raises the question of whether the implementation of such technologies should count as law or as regulation, and what this means for their further development. Finally, I propose the concept of ‘agonistic machine learning’ as a means to bring data-driven regulation under the rule of law. This entails obligating developers, lawyers and those subject to the decisions of ALI to re-introduce adversarial interrogation at the level of its computational architecture.
If a miner controls an economy of scale (i.e. PoW hardware manufacturing), they ultimately control the liquidity/velocity flow of the State/Federal level cryptos that are derived from those root chains, given a lack of market competition. Therefore, direct influence over said monopolistic entities are then tightly-coupled to future tokenized cities/states, which means that entire political-monetary interfaces, globally, if adopted and built upon, could be centralizing governance in ways many might not immediately realize — until it’s too late.
Cryptocurrency researchers from FECAP University in Brazil have shown that it would take only around $1.5 million to attack the ETC network and still pull a nice little profit. With $55 million dollars, you could effectively bankrupt the currency, netting nearly $1 billion in straight profit.If a party that controlled just 2.5% of the Ethereum hash rate switched to ETC, they’d instantly control over 51% of the total network hash rate. The attack wouldn’t even be absurdly expensive. It’d cost what you would earn mining on the ETH network with 2.5% of the hash rate, which equates to roughly 525 ETH, or $318,000.
Blockchain “freezes the future”, argues Hildebrandt, admitting of the two it’s the technology she’s more skeptical of in this context. “Once you’ve put it on a blockchain it’s very difficult to change your mind, and if these rules become self-reinforcing it would be a very costly affair both in terms of money but also in terms of effort, time, confusion and uncertainty if you would like to change that.
A few months ago, it was publicly exposed that ASICs had been developed in secret to mine Monero. My sources say that they had been mining on these secret ASICs since early 2017, and got almost a full year of secret mining in before discovery. The ROI on those secret ASICs was massive, and gave the group more than enough money to try again with other ASIC resistant coins.It’s estimated that Monero’s secret ASICs made up more than 50% of the hashrate for almost a full year before discovery, and during that time, nobody noticed. During that time, a huge fraction of the Monero issuance was centralizing into the hands of a small group, and a 51% attack could have been executed at any time.