Blockchain exists for ~10 years and still there are no mainstream use cases where it replaced the incumbent tech, other than illegal activity. There is a fundamental reason for that.BCh offers a single unique feature: distributed trusted transaction (DTT). DTT competes with a centralized transaction == transaction with a trusted third party (T3P). DTT is by definition distributed and as such is *always* more expensive than a T3P all other things being equal: reaching consensus with multiple parties is harder than with a single party. In order for DTT to be competitive with the old tech T3P, the distributed nature of DTT must offer some advantage for people to be willing to pay the required premium. So far the only use case where people or willing to pay this premium is circumvention of regulation, when the trusted third party does not exist. This brings us to this list of use cases:1. Circumvention of regulation.This is the only meaningful use of DTT.China has capital flow controls which effectively bar companies and individuals from moving money out of China. To get around these regulations people buy video cards and electricity in China for CNY, mine cryptocoins, sell them in the States for USD. That’s the largest market right now, much bigger than buying drugs on the likes of Silk Road. This use case also includes ICOs and other pump and dump schemes.2. Selling picks and shovels.Derivative of (1). If 1 goes away, 2 will go away too.https://finance.yahoo.com/quot… [yahoo.com]3. Marketing & FMOAdd blockchain to the company name and see your valuation pop.”We must work on blockchain because it’s the future”.All kinds of blockchain projects in banks, etc which are going mainstream “any time now”. All of them can be done easier/cheaper/more reliably with a T3P, no exceptions.Reply to This Share
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.
Bitcoin’s mining hardware (hashrate) has tripled since December, as can be seen above, even while price has fallen by 3x since December.It is now therefore a lot more expensive to mine a bitcoin than in December, while at the same time one mined bitcoin is worth a lot less.At some point miners are unable to afford energy costs or to keep up with adding more and more hardware as their old one becomes useless due to the constant increase of hashrate difficulty. So they close shop.Some miners, however, like Bitman, have lower costs, presumably because they manufacture themselves the mining hardware.So as other miners struggle, like Bitfury which has now dropped to 2%, Bitmain starts gaining more and more hashrate to the point they are now nearing 51%.The above bitcoin hashrate chart, however, even in a common sense way, looks quite unusual because it rarely goes down, if ever.Rather than responding to the price action, the hashrate appears completely detached. A situation that can not go for much longer because that increased new hardware itself puts pressure on price as the new barely profitable miners need to sell everything to cover costs.
In the last few months Balazs was participating in the creation of the Dutch Blockchain Research Agenda for NWO, the Dutch Science Agency.
The Agenda spells out the research priorities, and topics where more interdisciplinary research is needed. To quote the Agenda: “Given the complex fabric of technological and societal questions around blockchain, future research seems to require at least the awareness of this multi-disciplinarity, or even seek collaboration across the boundaries of disciplines. Blockchain research carries many challenges on the level of research design and methodology. As is the case with systems focused research, the proper demarcation of scope of future research projects and programmes is essential. This scope also sets the disciplinary mix that needs to be involved. At the same time, it should be ensured that the required disciplinary progress can happen, especially since different disciplines require research at different time scales.
Since blockchain technology is a moving target, in terms of research methodology one must also consider more exploratory, theory generating,
high risk and open-ended approaches, including tools such as mathematical modelling and analysis, business modelling, techno-economic analysis, functional and non-functional design and testing, action research, simulations and experiments in research labs and living labs, horizon scanning, etc. As this research agenda includes both fundamental and applied research, it requires active involvement from non-academic stakeholders from public bodies, industry, market sectors and the general public.
Another methodological challenge is the futureproofing of research. In such a volatile field, it is often difficult to distinguish issues relevant only in the short term, versus long term blockchain specific problems, versus fundamental research questions that cut across multiple digital technologies and have been and will be with us for decades.
There are several streams of investment that fuel research in the blockchain technology domain. Private investment through venture capital and
ICOs (crowdsourcing) as well as public investment by governments, universities, and research funding bodies should be aligned in a smart way.
In that context it seems inevitable to identify the fields that Dutch academia, research institutes and research departments of Dutch organisations are
best positioned to answer, either because they already excel in certain domains, or because they want to build skills and research capacity through
The Agenda is now public And can be downloaded from here:
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.
In this article, we’ll assess the foundational business functionalities for the main enterprise facing platforms including Ethereum, Hyperledger Fabric and R3 Corda in terms of where the software acquires its influence and how the system is overall optimized, whether through traditional distributed systems or a contemporary blockchain basis.
What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesise that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large data sets: price on on-line exchanges, volume of word-of-mouth communication in on-line social media, volume of information search, and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena which leave digital footprints, such as on-line social network usage.
Cryptocurrencies have become increasingly popular since the introduction of bitcoin in 2009. In this paper, we identify factors associated with variations in cryptocurrencies’ market values. In the past, researchers argued that the “buzz” surrounding cryptocurrencies in online media explained their price variations. But this observation obfuscates the notion that cryptocurrencies, unlike fiat currencies, are technologies entailing a true innovation potential. By using, for the first time, a unique measure of innovation potential, we find that the latter is in fact the most important factor associated with increases in cryptocurrency returns. By contrast, we find that the buzz surrounding cryptocurrencies is negatively associated with returns after controlling for a variety of factors, such as supply growth and liquidity. Also interesting is our finding that a cryptocurrency’s association with fraudulent activity is not negatively associated with weekly returns—a result that further qualifies the media’s influence on cryptocurrencies. Finally, we find that an increase in supply is positively associated with weekly returns. Taken together, our findings show that cryptocurrencies do not behave like traditional currencies or commodities—unlike what most prior research has assumed—and depict an industry that is much more mature, and much less speculative, than has been implied by previous accounts.
What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesize that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large datasets: price on online exchanges, volume of word-of-mouth communication in online social media, volume of information search and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena that leave digital footprints, such as online social network usage.