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The cryptocurrency industry has been talking about 2018 as the year of rising decentralized blockchain platforms. These platforms broadly fall under two categories: 1) technologies that scale rapid, secure, and cheap asset transactions — for instance Lightning Network, Stellar, and 0x; and 2) technologies that enable tools and modules for people to easily write contracts — for instance REQ, LINK, and various technologies that facilitate Ethereum dApps.
To see these platforms live up to their promise, we’ll have to build structures on top of them. The future will have to see the design of new decentralized markets that can build upon these platforms to tap into existing allocative inefficiencies, and empower people to coordinate with each other with incentives aligned across time.
A subfield of economics, applied market design, can help! In the past, successfully designed markets — for instance, Hal Varian’s work with Google on ad auctions and Al Roth’s residency matching program — were key to making systems profitable. Here are some examples of ways we can use economics to solve problems through blockchain tech.
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Crypto-economics, Vitalik Buterin once proposed at a talk, is the use of cryptography to design systems with desirable properties; to align cryptographic guarantees with agent incentives.
While Buterin was talking about block reward systems in which consensus protocols are robust to attack, we can generalize this thinking to a mapping of on-chain incentives with desirable properties in the off-chain “real world.”
Buterin’s vision for blockchain technology, from a quote pulled in 2015, was “a ‘Lego Mindstorms’ for building economic and social institutions.” In this vein, we can improve the efficiency of processes by building new systems to incentivize “good” behavior.
The ultimate economic benefits from crypto may not necessarily be led by the marginal advantages that cryptographic guarantees provide, but how they transform the way we think about project funding:
Here are some mechanism design concepts that could become reality, aided by the use of blockchain-enabled smart contracts.
Tokenization schemes align incentives over time periods, for example:
Cryptographic platforms have made minting public tokens much easier. It costs now essentially less than $5 to mint a token that can be publicly audited, and traded on secondary markets. This leads us to an economy where even abstract goods can be as efficiently re-allocated as easily as commodities.
The most straightforward example for this is the art market. As artists only profit from their first point of sale, they are incentivized to spend a distracting amount of time negotiating relationships with dealers and gallerists to establish a favorable valuation before the sale. Artists who become renowned later in life, or after death, do not receive funding from future appreciation over their artwork.
While it would be infeasible to convince budding artists to hire lawyers and legally track every point of sale, a smart contract would be lightweight to both implement and enforce, given that existing blockchain platforms already integrate with secondary markets. Moreover, the metadata in a smart contract could be used to later verify the authenticity of the work in question.
This idea has already gained some traction in the art world. Artist-critic Hito Steyerl has analogized the creation of value in cryptocurrency through speculative adoption and networks with that among artists: “Art is a networked, decentralized, widespread system of value. It gains stability because it calibrates credit or disgrace across competing institutions or cliques.”
Another study publicized that if New York school artists Jasper Johns and Robert Rauschenberg had securitized their artwork and retained a 10% stake, they would have beat the S&P 500 by 3 to 900 times (Whitaker and Kraussl, 2018). Most hedge funds can’t even beat the S&P 500 with fees, period.
While the tokenization of art may run afoul of being filed as a security under the Howey test in the US, the smart contract could be defined to reflect specific rights, such as the right to sell prints, exhibitions, and so on. The smart contract could also require a service fee proportional to a new valuation for each resale.
There are also other tokenization schemes to consider. For instance, works could be governed by a “depreciating license” (Weyl Zhang 2017), where the owner of the work would periodically announce valuations for the work at which they commit to sell their licenses, and pay a percentage of these valuations to the artist as license fees to continue owning the good.
This could easily extend to any industry where current ownership and licensing schemes impede efficient reallocation of resources, including public resources which require consistent investment.
This could also have implications for the sharing economy, where companies could allocate assets by establishing marketplaces for depreciating licenses over the assets they own. For instance, Uber could use depreciating licenses with tokens to implement a long-term decentralized carsharing program among partnered drivers.
In the current model of short-term licensing, users have no incentive to maintain the common value of the asset. When Uber tried to conduct a subprime car-leasing program called Xchange to increase its service fleet, they overshot their loss predictions on inventory by 18 times. Part of this was due to a unusually generous “unlimited miles with routine maintenance” clause, which incentivized drivers to “work long days and return vehicles with way too many miles, killing the resale value.”
In a decentralized token system, drivers can buy a depreciating license for the car (analogous to a “deposit/down payment”), pay a proportional amount of this self-evaluation (analogous to a “rental fee”), then resell the license when they no longer utilize it enough to pay this fee. In contrast to subprime loans, drivers would not only have an incentive to keep the car in good conditions for a higher resale value, but also get a sense of communal ownership.
If necessary, Uber could still moderate bad actors by voiding tokens claimed by fraud, and also incorporate other incentives on top of this program, including gamification, such as token rewards for drivers who complete some milestone number of rides.
The power of markets is that they allow us to allocate assets efficiently between parties. One source of inefficiency is externalities, a cost or benefit incurred by a party that did not choose to receive it.
A famous economic result, the Coase Theorem, proves that if parties that can conduct trade on an externality with perfect competition, we will get an efficient allocation of assets regardless of who holds the property rights to start with. Brave’s BAT Token, for instance, cites the Coase Theorem in the Appendix of its whitepaper, explaining its product as a market for attention between users and advertisers, with a negative externality being the “social cost” of intrusive ads.
A problem is that the theoretical result holds only under two rather strong assumptions: 1) that there are zero transaction costs, 2) that both parties have perfect, symmetric information about the good.
Current venture funding models prevent the rise of marketplaces. Startups that are trying to start marketplaces not only have to prove they can grow, but that they have a profit model. In more traditional financing, this forces them to add a layer of transaction fees or costly centralized verification.
Moreover, the value of marketplaces relies heavily on network effects, and new entrants require a lot of capital and following right from the start. This is partly why incumbent marketplace services, like Craigslist, have rarely been challenged even when they don’t actively innovate. Even current innovators in the space, such as Facebook Marketplaces, are loss-leaders bankrolled by other verticals.
These issues are partly resolved by the I.C.O. funding model. With cryptographic tokens, the scarcity of tokens are externally verified by default; as a result, founders can be transparent about allocating a reasonable portion of minted tokens as a founder’s reward. With developments in blockchain platforms, there’s a lot of developer money being thrown into lowering transaction fees for payments. Moreover, funding through token offerings is, for now, very front-loaded: with the capital that token offerings raise, these new startups will be much more equipped to take on wealthy incumbents.
And many types of markets are possible! For instance, arbitration markets. Smart contracts can and do go wrong. Arbitration markets can let contract owners freeze contracts that run in a disputed manner, and stake tokens to pay a market of arbiters to decide on what action the smart contract should perform: an analogue would be “settling out of court.”
Prediction markets like Augur produce markets where users can stake bets on how likely events are going to happen, and produce predictions of the future through the wisdom of crowds.
All of these markets have precedents, but the crypto ecosystem is making them easier to implement and scale.
Smart contract products can align incentives to ensure coordination:
It’s arguably true that a trusted central authority can already implement these without the use of cryptographic guarantees. Most mechanism design experiments are carried out by national and state governments, on university campuses, or large tech companies like Google and Facebook.
But token offerings can spread applied market design research into more industries, aligning incentives among more market participants. Smart contracts, unlike existing mechanisms, carry the promise of auditable code and distributed ownership. As platforms mature and templates for contracts emerge, these products will only become easier to build.
It’s not that anyone expects all, or even most, of these markets to succeed. All I know is if I had 1 ETH for every time an economics nerd talked about prediction markets to me before 2016, I’d be pretty rich. Augur, the prediction market, raised several million dollars and is now valued at over half a billion.
If you’re a nerd who ever wanted several million dollars to design cooperative mechanisms, now is as good a time as any to get stuck in.
Brian Ng is a writer and economist who has worked for Lightyear.io / Stellar Foundation and TGG Group, where he worked with economists as Steven Levitt and Daniel Kahneman on economics and data consulting projects. He graduated with a degree in Economics from the University of Chicago.
Disclaimer: the author holds small amounts of tokens in some of the publicly available assets that are mentioned. Opinions here do not represent those of any employer or affiliated organization.
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