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.
Another Kind of Radical Market
The book as a whole tends to focus on centralized reforms that could be implemented on an economy from the top down, even if their intended long-term effect is to push more decision-making power to individuals. The proposals involve large-scale restructurings of how property rights work, how voting works, how immigration and antitrust law works, and how individuals see their relationship with property, money, prices and society. But there is also the potential to use economics and game theory to come up with decentralized economic institutions that could be adopted by smaller groups of people at a time.
Perhaps the most famous examples of decentralized institutions from game theory and economics land are (i) assurance contracts, and (ii) prediction markets. An assurance contract is a system where some public good is funded by giving anyone the opportunity to pledge money, and only collecting the pledges if the total amount pledged exceeds some threshold. This ensures that people can donate money knowing that either they will get their money back or there actually will be enough to achieve some objective. A possible extension of this concept is Alex Tabarrok’s dominant assurance contracts, where an entrepreneur offers to refund participants more than 100% of their deposits if a given assurance contract does not raise enough money.
Prediction markets allow people to bet on the probability that events will happen, potentially even conditional on some action being taken (“I bet $20 that unemployment will go down if candidate X wins the election”); there are techniques for people interested in the information to subsidize the markets. Any attempt to manipulate the probability that a prediction market shows simply creates an opportunity for people to earn free money (yes I know, risk aversion and capital efficiency etc etc; still close to free) by betting against the manipulator.
Posner and Weyl do give one example of what I would call a decentralized institution: a game for choosing who gets an asset in the event of a divorce or a company splitting in half, where both sides provide their own valuation, the person with the higher valuation gets the item, but they must then give an amount equal to half the average of the two valuations to the loser. There’s some economic reasoning by which this solution, while not perfect, is still close to mathematically optimal.
One particular category of decentralized institutions I’ve been interested in is improving incentivization for content posting and content curation in social media. Some ideas that I have had include:
- Proof of stake conditional hashcash(when you send someone an email, you give them the opportunity to burn $0.5 of your money if they think it’s spam)
- Prediction markets for content curation(use prediction markets to predict the results of a moderation vote on content, thereby encouraging a market of fast content pre-moderators while penalizing manipulative pre-moderation)
- Conditional payments for paywalled content (after you pay for a piece of downloadable content and view it, you can decide after the fact if payments should go to the author or to proportionately refund previous readers)
And ideas I have had in other contexts:
Twitter scammers: can prediction markets incentivize an autonomous swarm of human and AI-driven moderators to flag these posts and warn users not to send them ether within a few seconds of the post being made? And could such a system be generalized to the entire internet, where these is no single centralized moderator that can easily take posts down?Some ideas others have had for decentralized institutions in general include:
- TrustDavis (adding skin-in-the-game to e-commerce reputations by making e-commerce ratings be offers to insure others against the receiver of the rating committing fraud)
- Circles (decentralized basic income through locally fungible coin issuance)
- Markets for CAPTCHA services
- Digitized peer to peer rotating savings and credit associations
- Token curated registries
- Crowdsourced smart contract truth oracles
- Using blockchain-based smart contracts to coordinate unions
I would be interested in hearing Posner and Weyl’s opinion on these kinds of “radical markets”, that groups of people can spin up and start using by themselves without requiring potentially contentious society-wide changes to political and property rights. Could decentralized institutions like these be used to solve the key defining challenges of the twenty first century: promoting beneficial scientific progress, developing informational public goods, reducing global wealth inequality, and the big meta-problem behind fake news, government-driven and corporate-driven social media censorship, and regulation of cryptocurrency products: how do we do quality assurance in an open society?
All in all, I highly recommend Radical Markets(and by the way I also recommend Eliezer Yudkowsky’s Inadequate Equilibria) to anyone interested in these kinds of issues, and look forward to seeing the discussion that the book generates.
Decentralization vs Incoordination – Tadge Dryja (MIT DCI)BPASE ’17, January 26th 2017, Stanford UniversityStanford Cyber Initiative