A Taxonomy for Digital Assets
Going Down the Cryptoeconomics Rabbit Hole
Investor views on cryptocurrencies are strongly divided, ranging from ‘rat poison squared’ and a ‘new bubble’, to ‘rat poison for cash’ and ‘Web 3.0’. Despite this divergence in opinions, there seems to have been a shift in sentiment amongst institutional investors. Some surveys are suggesting that 7 out of 10 institutional investors are expecting to invest in digital assets in the future. Whether blockchain technology transforms society as we know it or fails to gain mainstream adoption, it appears likely that digital assets are here to stay. This presents investors in traditional assets with new return sources.
This is the first post in a series on digital assets. The target audience is investors who are looking to explore this nascent asset class and allocate capital to it. That includes institutional investors, professional retail traders, academics and researchers in traditional financial markets, as well as computer scientists, data scientists and software engineers with little background in traditional finance. We will focus on the most liquid digital assets, i.e. those which exceed a minimum threshold of daily dollar volume with derivatives traded across major centralized and decentralized exchanges. The series aims to develop and share useful analytical tools, algorithms, data sets and research notebooks via an open source project.
Note that some posts will go into technical detail and expand on important concepts. When doing so, we will highlight the key takeaways in bold, provide a summary in the introduction and distill important information in data visualizations and tables. This allows us to be both comprehensive and concise, yet allows readers the choice of higher vs. lower level coverage of the topic, depending on their interest level and time constraints.
In this introductory post, we’ll explore where we are in the adoption life cycle of this new asset class, a taxonomy for digital assets, cryptoeconomics as an emerging field, an interdisciplinary concept map and the most salient features of cryptoeconomic systems. We end by providing a list of learning resources (online courses, videos, books, papers and journals) which can serve as a starting point for new investors who wish to learn more about digital assets.
Key takeaways:
- The global technology adoption rate is currently at most 5%, using the number of active addresses across major cryptoassets as a proxy.
- Cryptoeconomics brings together the fields of economics and computer science to study the decentralized marketplaces and applications that can be built by combining cryptography with economic incentives.
- Cryptoeconomic systems are a socio-economic coordination and resource allocation mechanism. Those resources can be either 1) computational, 2) financial or 3) social.
- The key features of cryptoeconomic systems are: 1) decentralization, 2) complexity, 3) interdisciplinarity and 4) unorthodoxy. By definition, they are difficult to understand and beyond the scope of traditional academia.
- Digital assets are a crucial element of cryptoeconomic systems. They provide the economic incentive which allows the coordination and allocation of resources in a decentralized digital ecosystem.
- There are 3 broad categories of digital assets: 1) cryptocurrencies (digital money), 2) utility tokens (digital resources or finished goods/services) and 3) security tokens (digital representations of more traditional assets).
- Digital asset valuation is largely dependent on the correct classification of the asset.
Technology Adoption Life Cycle
Still early days
Though rapidly evolving, blockchain technology is still quite young. The global adoption rate is currently at most 5%, using the active address count (sum count of unique addresses that were active in the network) across major cryptoassets as a proxy. Although this figure may bias the adoption rate upwards since active addresses may double count users with many addresses, it points to a stage of technology adoption which is comparable to the internet in the late 1990s and what technologists would refer to as the ‘early adoption phase’ of the technology adoption life cycle. In other words, we are still quite far from the technology reaching the mainstream, a process which took roughly 20 years for the internet.
The Birth of a New Technology…
…and a New Asset Class
“There are three eras of currency: commodity-based, politically based, and now math based” — Chris Dixon [1]
The arrival of Bitcoin brought with it an entirely new asset class. But what exactly do we mean by ‘asset class’? Assets are often assigned into groups such as equities, bonds, commodities, currencies, real estate, etc. However, how and why they are classified that way rarely gets addressed.
An asset class can be defined as an investment vehicle which shares common exposure to economic risks and has distinct empirical properties relative to assets in other classes.
Greer [2] identifies 3 asset superclasses:
- Capital assets: assets which produce an ongoing source of value, generally in the form of a cash flow or stream of income. Capital assets include stocks (producing a stream of earnings or dividends), bonds (producing a stream of interest payments) and real estate (producing a stream of rents). These assets can be valued by the net present value of an expected stream of income, which is largely a function of growth and discount rates.
- Consumable/Transformable assets: assets which have utility, can serve as inputs into producing a good or service, or can be consumed. This superclass is most often associated with physical commodities such as wheat, energy products, metals, etc. These assets do not generate any cash flows and therefore cannot be valued by their net present value. Rather, they are more often valued using supply/demand analysis.
- Store of value assets: assets which have no utility, nor produce a cash flow. These assets preserve value mostly due to their scarcity properties. Examples include precious metals, currency, collectibles, gemstones, land, livestock, etc.
These asset superclasses can sometimes overlap, blurring the lines between asset classes. For example, gold can be both a store of value, and a consumable/transformable asset. Land can be both a store of value and an income-producing capital asset if developed. Asset classes can be further subdivided into subclasses based on characteristics like geography (countries and regions), economic development (emerging vs. developed markets), or credit risk (sovereign debt issued in local or foreign currency). The use of financial derivatives can further complicate the definition of an asset class, and lead to the emergence of new ones (e.g. volatility).
Despite these overlaps, grouping assets into the ‘correct class’ can be quite important given its relevance for asset valuation. Unlike legacy assets, which have an extensive history and body of work on asset pricing and valuation, the classification and valuation of digital assets remains a work in progress. With over 10,000 digital assets now in existence, a more comprehensive taxonomy of the digital asset universe is required.
A Taxonomy for Digital Assets
Defining & Categorizing Key Concepts
“The media is refining the message, saying ‘The currency is a joke, but the technology — I don’t know, maybe there’s something there … Bitcoin is a currency, bitcoin is a network, bitcoin is a technology and you can’t separate these things. A consensus network that bases its value on the currency does not work without the currency.” — Andreas Antonopoulos [3]
The digital asset space can be confusing to newcomers, especially its nomenclature. For example, when talking about bitcoin, the largest digital asset by market cap, it’s often unclear whether one is referring to the protocol or the cryptocurrency. Moreover, some terms like cryptocurrencies and blockchain technology are often used interchangeably, but in practice they may not always overlap. Lastly, many terms can have more than one meaning; blockchain can mean a data structure (i.e. a chain of blocks), or it can be used to mean the broader blockchain technology ecosystem.
Let’s start by clarifying some important concepts:
- Digital assets refer to any asset that can be represented digitally. This includes: digital documents, audible content, motion pictures, financial transactions, personal information and any other relevant digital data and metadata.
- Cryptoassets are a subclass of digital assets. They use cryptography to validate, record and secure the ownership and exchange of digital assets using a peer-to-peer network without a trusted third party. They can be further subdivided into Fungible and Non-fungible tokens (NFTs).
- Fungible tokens (FTs) are interchangeable and indistinguishable data units stored on a distributed ledger. They include Cryptocurrencies, Utility Tokens and Security Tokens. Fungibility is one of the key properties of sound money.
- Non-Fungible tokens (NFTs) are unique and non-interchangeable data units stored on a distributed ledger. They include tradable assets, which can be either digital or physical like IP and art, and non-tradable assets like personal information, identity and credentials.
- Cryptocurrencies, sometimes also referred to as Coins, include any fungible token which can be used as a medium of exchange, store of value and/or unit of account. Unlike fiat money where these functions typically come bundled, distributed ledgers facilitate the unbundling of the functions of money and have the potential to address the Money Trilemma¹ and lead to more optimal economic outcomes [4] [5]. Bitcoin is the first and largest cryptocurrency.
- Utility tokens represent the right to use a service or product. They can be further separated into Platform tokens and App Coins. Platform tokens can be thought of as digital resources much like commodities are in the physical world. In a digital world, digital commodities are things like computational power, storage capacity and network bandwidth. Examples of Platform tokens include Ether (gas) and Filecoin (storage). App Coins, on the other hand, represent finished digital consumer goods or services like social networks, financial services, media, games, etc. Since dApps are usually built on top of a platform, App Coins are highly tied to the platforms’ infrastructure . Examples of App Coins include Compound (DeFi) and Decentraland (virtual reality), both of which are built on top of Ethereum.
- Security tokens refer to a digital and cryptographic representation of ownership stake in more traditional assets like stocks (companies), bonds, commodities, real estate, etc. An example of a security token is AspenCoin, which represents equity ownership in the St. Regis Aspen Colorado resort.
- Blockchain refers to an underlying data structure used by some distributed ledgers, which combines two popular data structures: linked lists and stacks. A blockchain is a chain of blocks, each of which contains a timestamp and a set of transactions. Every block contains a hash of the previous block producing a chain of linked blocks in chronological order. Once a sufficient amount of blocks are added to the chain, the blockchain becomes very much like a shared append-only database where every transaction is recorded in a transparent, decentralized and immutable way.
- Distributed ledger technology (DLT) is a broader term which captures various types of data structures that are used to create shared and decentralized databases. Not all distributed ledgers use blockchain data structures. For example, IOTA uses a directed acyclic graph (DAG). Furthermore, private/permissioned distributed ledgers may not need a cryptoasset in order to function since they are usually more centralized and may not require economic incentives for coordination. Examples of DLTs include Blockchain, Tangle and Hashgraph [6].
- Distributed ledgers can be either public, private, or a mix often referred to as a consortium. Public ledgers are fully decentralized networks where access is open to anyone who wants to join the network. There is no need for a trusted third party, and anyone can read, send or validate transactions. The degree to which someone can influence the ledger is a function of the consensus mechanism and the economic resources under their control (computational, energy, tokens, etc). Private ledgers, on the other hand, are closed and more centralized networks where access is restricted and may require permission from a trusted third party, often a single organization, to read from and/or modify the ledger. A consortium typically lies somewhere in between public and private ledgers with the network comprised of pre-selected nodes (participants). Unlike private ledgers, public ledgers generally require an economic incentive, typically in the form of a cryptoasset, for miners/validators to ensure the proper functioning of the protocol.
- Cryptoeconomic systems (CES) refer to systems which combine important elements from public key cryptography, peer-to-peer networks, consensus mechanisms, data structures and economic incentives to produce decentralized, self-governing digital market economies.
- Web 3.0, aka Web3, refers to a newer generation of decentralized applications (dApps) on the World Wide Web which are built on top of blockchains, or other distributed ledgers, and utilize cryptoeconomic systems to function.
One way to improve our understanding of digital assets is to create a taxonomy for them. Taxonomies help researchers classify and systematize domain knowledge into hierarchical and/or categorical relationships using visual representations like concept maps or dendrograms. Their main benefits are that they can facilitate understanding, improve communication and collaboration between stakeholders in the ecosystem, and ultimately lead to better interoperability and regulatory frameworks. The most well known taxonomy for digital assets is the Token Taxonomy Framework (TTF).
A taxonomy can help investors understand how various digital assets differ on key characteristics and how that may impact their portfolio risk exposures. Below, we provide a simplified taxonomy of digital assets. It’s important to keep in mind that this taxonomy is likely to expand and change as the universe of digital assets grows.
Going Down the Cryptoeconomics Rabbit Hole
The Emerging Field of Cryptoeconomics
“This is your last chance. After this, there is no turning back. You take the blue pill — the story ends, you wake up in your bed and believe whatever you want to believe. You take the red pill — you stay in Wonderland and I show you how deep the rabbit-hole goes.” — Morpheus [7]
What is Cryptoeconomics?
The term Cryptoeconomics emerged from the Ethereum developer community in 2014/2015. Zamfir [8] describes it as “a formal discipline that studies protocols that govern the production, distribution, and consumption of goods and services in a decentralized digital economy. Cryptoeconomics is a practical science that focuses on the design and characterization of these protocols”. MIT’s Cryptoeconomics Lab founded in 2017 defines it as a discipline that “brings together the fields of economics and computer science to study the decentralized marketplaces and applications that can be built by combining cryptography with economic incentives. It focuses on individual decision-making and strategic interaction between different participants in a digital ecosystem (e.g. users, providers of key resources, application developers etc.), and uses methodologies from the field of economics — such as game theory, mechanism design and causal inference — to understand how to fund, design, develop, facilitate the operations and encourage the adoption of decentralized marketplaces and related services and digital assets.” The newly established Cryptoeconomic Systems (CES) journal, published by The MIT Press, attempts to fill many of the discipline’s “epistemic gaps” with an interdisciplinary approach.
Learning the Domain
A relevant question for a new investor in digital assets is what one should learn to adequately analyze the fundamentals of protocols and their respective cryptoassets. Given the complex and interdisciplinary nature of cryptoeconomic systems, Repko and Szostak [9] suggest that the research process should follow a specific set of steps in order to be effective:
- Identification of relevant disciplines (concept map)
- Mapping research questions to identify the disciplinary parts (concept map & research)
- Reducing the number of potentially relevant disciplines (concept map)
- Literature review in relevant disciplines and for relevant research questions (research & learning resources)
- Developing adequacy in relevant disciplines (learning resources)
- Analyzing problems and evaluating insights (research)
- Integrating insights and creating common ground for insights (research & open-source collaboration)
One way to to better understand the breadth and depth of a new domain is to use a concept map. Tools like taxonomies, concept maps, ontologies, and knowledge graphs are becoming increasing important in a Big Data world as a means of organizing data and concepts into ‘knowledge’.
The concept map below illustrates the interconnectedness and range of disciplines spanned by cryptoeconomics.
To better see and navigate the concept map, we suggest following this link.
The map allows us to explore various concepts and their relations. Although it is reasonably comprehensive, it is by no means exhaustive. Rather, the exercise should be an ongoing and collaborative process which allows us to develop a sense of the scope of the domain, relations between various concepts, and unifying themes. In this case, a central theme does emerge: allocation of resources. Essentially, cryptoeconomic systems are a coordination mechanism for resource allocation decisions between various stakeholders with distinct preferences and information [10]. Those resources can be 1) computational (hardware, energy, etc), 2) financial (tokens, fiat money, etc) and 3) social (governance, code contribution, etc).
Knowledge modeling can help researchers develop more intuition about a domain, especially a new one, and ultimately increase ‘domain knowledge’. One can go further and formalize a concept map into an ontology, a topic for future research.
Key Features of Cryptoeconomic Systems
Decentralization, Complexity, Unorthodoxy and Interdisciplinarity
“You put an open, decentralized ecosystem: open source, open standards, open networking and the intelligence and innovation pushed all the way to the edge — put that against a closed system, controlled by a central provider, whose permission you need in order to innovate and who will only innovate at the exclusion and competition of all of the other companies — and we will crush them.” — Andreas Antonopoulos [3]
“There is a ‘decentralisation illusion’ in DeFi since the need for governance makes some level of centralisation inevitable and structural aspects of the system lead to a concentration of power. If DeFi were to become widespread, its vulnerabilities might undermine financial stability. These can be severe because of high leverage, liquidity mismatches, built-in interconnectedness and the lack of shock absorbers such as banks.” — Aramonte et al. [11]
To better understand cryptoeconomic systems, it’s useful to characterize some of their most salient features: 1) decentralization, 2) complexity, 3) unorthodoxy and 4) interdisciplinarity.
Decentralization
One of the key elements of cryptoeconomic systems is decentralization. In systems theory, a decentralized system is one where decisions are made by various agents without centralized control or processing. Rather, control is distributed to the agents in the system who act on local information, leading to self-organizing order and coordination in the pursuit of global goals.
The degree to which a system is decentralized can have important implications. A decentralized system generally implies lower security risks since it minimizes the risk of a single point of failure. On the other hand, a centralized system implies faster speed since it minimizes the need for coordination between agents. For example, valuable jewelry can be made more secure from theft if spread across many safety deposit boxes rather than left in a single box. This would, however, come at the cost of time since getting the jewelry out of the boxes would be slower. This inherent trade-off between the degree of decentralization and speed is often referred to as the Blockchain Trilemma. The concept allows us to understand the trade-offs inherent to blockchain design, and DTLs more broadly. Essentially, the trilemma states that it is difficult to have all three of the following at the same time: 1) decentralization, 2) security and 3) scalability.
The advantages/disadvantages of decentralized systems also depend on the use case and type of decentralization. Buterin [12] argues that there are 3 types of software decentralization: 1) architectural, 2) political and 3) logical.
Blockchains are politically decentralized (no single individual or organization), architecturally decentralized (multiple nodes) and logically centralized (one central database/agreed state).
The centralization vs. decentralization spectrum is implicit in almost every important socio-economic system which involves decision making and resource allocation. For example, economic systems can be either centrally planned, where a central authority makes resource allocation decisions, or market-based where the price signal is relied upon for resource allocation. In political systems, we have autocracies, where absolute power over a state is concentrated in the hands of one person, and direct democracies where the people decide on policies without any intermediary or representative. This centralization/decentralization spectrum allows us to think more explicitly about the design trade-offs of various systems and select them more optimally depending on the use case. The table below summarizes the key differences between centralized and decentralized systems across features and examples.
Complexity
A common starting point for new investors in the digital asset universe is to begin with a literature review. This usually involves reading the bitcoin whitepaper [13], among others (see learning resources), which makes it quite clear that this wasn’t the first attempt at a digital currency. Equally important was the work of Chaum [14], Dai [15], Back [16] and Szabo [17], all of which laid a foundation for Nakamoto to build on. This body of work allows us to delve deeper into some of the technical aspects of blockchain technology. The newcomer will soon realize that the technology involves quite a bit of complexity. Voshmgir and Zargham [10] describe the Bitcoin network as a complex socio-economic system, the general features of which include [18]:
- Nonlinearity: a change in input can lead to a nonproportional change in output.
- Feedback loops: a system where the outputs are fed back as inputs as part of a causal chain forming a circuit or loop. The feedback can be positive (amplification) or negative (damping).
- Spontaneous order: the spontaneous emergence of order out of seeming chaos, or self-organization, in a system comprised of self-interested agents.
- Emergence: the existence of collective properties of a system that its parts do not have alone.
- Adaptation: a system where the individual agents adapt to the changing environment, making the collective behavior difficult to predict.
It’s not difficult to see the parallels between complex systems and Bitcoin. For instance, the difficulty adjustment is an example of a negative feedback loop. Spontaneous order arises in a network comprised of self-interested nodes (aka miners/validators). Emergent properties appear as smart contracts get deployed on layer 1 smart contract platforms (e.g. Ethereum) leading to decentralized applications (e.g. DeFi).
This inherent complexity generally means that in order to achieve true mastery of the subject matter, one is likely to have to make a significant investment in time and effort. Crypto enthusiasts often refer to this as ‘going down the crypto rabbit hole’. The likelihood of making such an investment of resources depends on a number of factors like age (indirectly affecting brain plasticity and wealth), relevant domain knowledge (e.g. computer science or economics), vested interest in the status quo (e.g. executives at large banks or centralized tech firms), free-time and pre-disposition for learning, etc. For example, an ambitious young developer who enjoys studying computer science and economics, has an open-mind and free time to explore new ideas and projects, and has no vested interests in the status quo, is much more likely to ‘go down the crypto rabbit hole’ than an older banking executive who runs a large financial institution, has become wealthy in the established financial system, has a family, is largely unfamiliar with many of the ideas in this new complex and interdisciplinary field, and has limited time and incentive to learn them.
Unorthodoxy
The emergence of cryptocurrencies has been met with tremendous skepticism and disdain, especially by mainstream economists. Mainstream economics can be defined as a “body of knowledge, theories, and models of economics, as taught by universities worldwide, that are generally accepted by economists as a basis for discussion”. It is currently associated with New Keynesian economics and its workhorse model, the dynamic stochastic general equilibrium (DSGE) model. DSGE models are built on microfoundations, i.e. the theoretical foundations of equilibrium (supply/demand), rationality (consumers maximizing utility and firms maximizing profit) and self-interest (aka the invisible hand). Essentially, these micro level models of individual behavior are aggregated to derive macro level models [19].
Over the past decades, advances in newer branches of economics, such as behavioral economics, game theory, information economics, economics of networks, and complexity economics have raised some serious questions about the basic assumptions of mainstream economics. The seminal work of Daniel Kahneman and Amos Tversky, which earned them a Nobel Prize and is detailed in Michael Lewis’ book [20], revealed that human judgement and decision making under uncertainty deviate significantly from rationality. Using game theory, Pangallo et al. [21] show that in complicated and competitive games, similar to those in economic systems, convergence to equilibrium is unlikely. In addition, research in complexity economics conducted by the Santa Fe Institute and the Institute of New Economic Thinking (INET) points to a high likelihood of far from equilibrium economic phenomena in competitive games like the stock market [22]. As legendary fund manager Bill Miller explains [23], “real markets are a long way from the dynamic stochastic general equilibrium (DSGE) models that dominate academic economics. Being complex adaptive systems, they are nonlinear and constantly changing as circumstances and conditions and information warrant, and those changes can be abrupt, violent, and frightening.” Lastly, Grund et al. [24] present a model for both self-interest and ‘other-regarding’ preferences, which lays the foundations for more cooperative social networked behaviors. In fact, cooperative governance is an important feature of cryptonetworks [25].
The failures of mainstream economics are not new. Classical economics failed to predict the Great Depression, after which it was largely replaced by Keynesian economics. Keynesianism, however, failed to explain the stagflation of the 1970s. This led to the merging of both schools — aka New Keynesianism — looking to rebuild macroeconomics from a microeconomic basis. This new synthesis didn’t seem to improve the state of mainstream economics as it once again failed to predict the global financial crisis (GFC) of 2007–2009. This string of failures is not unique to the field of economics. In his seminal work The Structure of Scientific Revolutions, philosopher and historian of science Thomas Kuhn describes the process of scientific progress as more akin to revolutions — a non-linear, cyclical and often disruptive process — rather than one where knowledge is accumulated following a smooth linear path. These revolutions, which he called paradigm shifts, tend to occur when new phenomena cannot be explained with the prevailing framework, and a new theory emerges which can explain both the older data as well as the dominant paradigm’s anomalies [27].
The post-financial-crisis policy landscape has increasingly been characterized by experimental policies, starting with unconventional monetary policies such as quantitative easing (QE) and forward guidance. That was followed by even more extreme policies after the COVID-19 pandemic-induced recession, as monetary/fiscal cooperation, aka helicopter money, was stepped up. These policies have led to the polarization of economic factions. On one side, there is Modern Monetary Theory (MMT), aka Neo-Chartalism, of which Prof. Stephanie Kelton is a major proponent. On the other, there is a school of thought characterized by decentralized [28] and layered [29] digital money, aka Neo-Metalism, of which Prof. Saifedean Ammous and Prof. Nik Bhatia are proponents. While the former camp advocates for state-issued money and a public monopoly, where credit and debt levels are limited only by inflation, the later camp advocates for privately-issued money and currency competition, where money soundness determined in the free-market is the decisive factor for acceptance. These competing factions are now on a collision course with one another. This is most obvious in the financial system where centralized finance (CeFi), dominated by fiat-money issuing central banks and traditional financial institutions, is facing off with decentralized finance (DeFi), dominated by competing cryptocurrencies, fintech start ups and new decentralized financial protocols and applications.
Cryptoeconomic systems provide a unique opportunity to approach economic research from an entirely different perspective, and with the use of more powerful tools. While traditional economics is restricted to empirical evidence to tests its theories, cryptoeconomics can make use of experiments, i.e. small, controlled digital economies where policy prescriptions, rules and regulations, aka Regtech, can be programmed into protocols through a voting or rating system, rather than left to the whim of a small group of technocrats. Some of these features of crypto economies make them an ideal laboratory for economists: a small scale and contained environment, clearly established programmatic rules, data transparency and availability, etc [30]. In search of a new paradigm, cryptoeconomic systems will allow us to explore new approaches grounded in market-based allocation systems.
Interdisciplinarity
With Bitcoin, Nakamoto’s major innovation wasn’t so much any individual technical breakthrough, but rather the integration of many of the existing building blocks [31]. In particular, he included elements of linked timestamping, cryptography, distributed and decentralized computing, consensus mechanisms and economic incentives.
This interdisciplinarity can be observed in more detail by navigating through our concept map (shown above). Cryptoeconomics ties together ideas from a broad range of disciplines: mathematics, computer science, economics, law, politics, systems theory, etc. For example, graph theory, a branch of mathematics, is used in computer networks to assess performance, security and decentralization. It is also used in network economics for the analysis of businesses that benefit from network effects, aka multi-sided platforms, the native business model of cryptoeconomic systems. Game theory, another branch of mathematics, is applied to economics in mechanism and market design, which in turn guide protocol and token design. Hence, developing a sound analytical framework for the fundamentals of digital assets requires tying together ideas from various disciplines.
The interdisciplinary nature of the technology explains why many of the technology’s major contributors came from outside of academia. Olnes and Kutsen [32] argue that most universities are not set up for interdisciplinary studies, as collaboration between departments is often limited. Furthermore, curriculums take time to be revised following important technological breakthroughs, leaving a wide knowledge gap when they occur. Brekke and Alsindi [33] call this the ‘epistemic gap’, and point to things like legacy training, selection mechanisms, institutional structure, and career incentives, all of which produce researchers less likely to be open to interdisciplinary collaboration.
As a result, most crypto pioneers are considered polymaths. Satoshi Nakamoto was able to connect the dots across disciplines to solve the double spending problem, something neither a Nobel prize nor Turin award winner was able to do. Nick Szabo, with degrees both in computer science and law, merged key concepts from both disciplines to provide the foundation for smart contracts. In 2014, Vitalik Buterin was awarded a $100k grant from the Thiel Fellowship, a scholarship often given to promising young polymaths, allowing him to work full-time on Ethereum. This new breed of crypto-polymaths are creating an entirely new field of research that spans many academic disciplines.
Use Cases
Early Applications and Potential
The application of cryptoeconomics appears elusive or even far fetched to most newbies. They argue that decentralization is overhyped, and that cryptoecononomic systems have few interesting or practical use cases. This is often the dominant narrative at the early stages of adoption of general purpose technologies (GPT). Not many were able to foresee that the internet would become ubiquitous in its early days. It took not only deep understanding of how the technology works, but imagination in terms of how it could be applied to various sectors of the economy. In Blockchain Revolution [34], Alex Tapscott provides a useful overview of how cryptoeconomic systems can be used to both improve and disrupt current industries. We provide a summary table of potential applications below.
In sum, cryptoeconomic systems provide a new way of leveraging market-based resource allocation systems, with use cases that we are only beginning to grasp. With this general purpose technology comes the birth of digital assets, a key component for coordination within these systems. Investors ignoring this asset class are likely to fall behind as the asset universe grows and use cases continue to evolve at a rapid pace to impact different sectors of the global economy.
Nick Latinovic is the founder of Systamental, an open-source community dedicated to providing the highest quality data, algorithms and analytical tools to digital asset investors.
We welcome questions and feedback on this post in the comments section below. If you would like to contribute to our open-source community or discuss specific projects, reach out to us at info@systamental.com.
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Learning Resources
White Papers
Chaum, David (1983). Blind Signatures for Untraceable Payments. Advances in Cryptology, pp 199–203
Dai, Wei (1998). B-Money.
Back, Adam (2002). Hashcash — A Denial of Service Counter-Measure.
Szabo, Nick (2005). Bit Gold.
Nakamoto, Satoshi (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
Poon, Joseph; Dryja, Thaddeus (2016). The Bitcoin Lightning Network: Scalable Off-Chain Instant Payments.
Buterin, Vitalik (2017). Ethereum White Paper: A Next-Generation Smart Contract and Decentralized Application Platform.
Protocol Labs (2017). Filecoin: A Decentralized Storage Network.
MakerDAO (2017). The Dai Stablecoin System.
Richard Craib, Richard; Bradway, Geoffrey; Dunn, Xander; Krug, Joey (2017). Numeraire: A Cryptographic Token for Coordinating Machine Intelligence and Preventing Overfitting.
Peterson, Jack; Krug, Joey; Zoltu, Micah; Williams, Austin K.; Alexander, Stephanie (2018). Augur: a Decentralized Oracle and Prediction Market Platform. Forecast Foundation, July
Brave Software (2021). Blockchain Based Digital Advertising.
General
Lee, David (2015). Handbook of Digital Currency: Bitcoin, Innovation, Financial Instruments, and Big Data. Academic Press, 1st edition, May 13
Buterin, Vitalik (2015). On Public and Private Blockchains. Ethereum Foundation, Blog
Brownworth, Anders (2016). Blockchain 101 — A Visual Demo. MIT, Nov 5
Narayanan, Arvind; Bonneau, Joseph; Felten, Edward; Miller, Andrew; Goldfeder, Steven (2016). Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton University Press, Illustrated edition, Jul
Pilkington, Marc. (2016). Bitcoin through the Lenses of Complexity Theory: Some Non-Orthodox Implications for Economic Theorizing. Jan
Narayanan, Arvind; Clark, Jeremy (2017). Bitcoin’s Academic Pedigree. Communications of the ACM, Volume 60, Issue 12, December, pp 36–45
Antonopoulos, Andreas (2017). Mastering Bitcoin: Programming the Open Blockchain. O’Reilly Media, 2nd edition, Jul
Buterin, Vitalik (2017). The Meaning of Decentralization. Medium
Dixon, Chris (2018). Why Decentralization Matters. OneZero
Sultan, Karim; Ruhi, Umar; Lakhani, Rubina (2018). Conceptualizing Blockchains: Characteristics & Applications.
Labouseur, Alan G.; Johnson, Matthew; Magnusson, Thomas (2019). Demystifying blockchain by teaching it in computer science: adventures in essence, accidents, and data structures. Journal of Computing Sciences in Colleges, Volume 34, Issue 6, April, pp 43–56
Filippova, E. (2019). Empirical Evidence and Economic Implications of Blockchain as a General Purpose Technology. IEEE Technology & Engineering Management Conference (TEMSCON), pp. 1–8
Dixon, Chris (2020). Crypto Networks and Why They Matter. YouTube, a16z, Crypto Startup School
Cryptoeconomics
Böhme, Rainer; Christin, Nicolas; Edelman, Benjamin; Moore, Tyler (2015). Bitcoin: Economics, Technology, and Governance. Journal of Economic Perspectives, 29 (2): 213–38
Davidson, Sinclair; De Filippi, Primavera; Potts, Jason. (2016). Economics of Blockchain. Available at SSRN, March 8
Buterin, Vitalik (2017). Introduction to Cryptoeconomics. YouTube, Ethereum Foundation. Feb 23
Dixon, Chris (2017). Crypto Tokens: A Breakthrough in Open Network Design. Medium
Stark, John (2017). Making Sense of Cryptoeconomics. Coindesk, Opinion
Voshmgir, Shermin; Zargham, Michael (2020). Foundations of Cryptoeconomic Systems. Interdisciplinary Research Institute for Cryptoeconomics, Working Paper Series
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Brekke, Jaya Klara; Alsindi, Wassim Zuhair (2021). Cryptoeconomics. Internet Policy Review, Journal of Internet Regulation, Volume 10, Issue 2
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Computer Networks
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Engineering
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Data Science
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Token Economics
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Mathematics
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Governance
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Law & Regulation
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De Fillipi, Primavera; Wright, Aaron (2020). Blockchain and the Law: The Rule of Code. Harvard University Press
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Footnotes
1. The Money Trilemma states that money cannot be all three of the following at the same time: a store of value, medium of exchange and unit of account.
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