Open Abstracts

Balázs Bodó, University of Amsterdam
Jaya Klara Brekke, Durham University
Jaap-Henk Hoepman, Radboud University

The rapidly evolving blockchain technology space has put decentralisation back into the focus of the design of techno-social systems, and the role of decentralised technological infrastructures in achieving particular social, economic, or political goals. In this entry we address how blockchains and distributed ledgers think about decentralisation.

Decentralised network topologies

A network is made of nodes, and edges, or interconnections between the members of the network. In a distributed network every node has roughly the same number of edges, and there are more than one routes in which nodes can connect with each other. This means that the topology of the network does not contain nodes in central or privileged positions, or if there are hierarchies built into the network, each node belongs to more than one hierarchy. This gives distributed networks a special property: the failure of a few nodes (even if they are chosen on purpose) still leaves the network connected, allowing all nodes to communicate with each other (albeit over a possibly much longer path than in the original network). In mathematics (and computer science) Graph Theory is devoted to the study of networks and their properties (Bondy and Murty, 2008).

Figure 1: Various network topologies (Baran, 1964).

Though often used as synonyms, decentralised and distributed networks are not the same. Decentralised networks are built from a hierarchy of nodes, and nodes at the bottom of the hierarchy have only a single connection to the network. Failure of a few nodes in a decentralised network still leaves several connected components of nodes that will be able to communicate with each other (but not with nodes in a different component).

The degree of decentralisation and distributedness varies from network by network. In general networks that are more distributed are more resilient to the failure of individual nodes or loss of connection between them. This resilience applies to both concrete and virtual networks i.e physical network infrastructures (such as the routers, cables, backbones, WIFI hotspots of the internet), and virtual networks running on the physical layer, such as blockchain networks, or file sharing networks.

Initially designed to be a cold-war resilient distributed network, the internet is in fact a decentralised network. Consequently, there are multiple stakeholders, and multiple physical as well as virtual bottlenecks where the network is controllable, or vulnerable to surveillance, and failure (Forte et al., 2016; Kaiser, 2019; Kastelein, 2016; Snowden, 2019). Likewise, while the TCP/IP protocol envisaged a network in which each node (user, machine) could be both an information sender and receiver, in practice, highly centralised virtual networks emerged in knowledge production, communication, or commerce. The recent wave of re-decentralisation (Redecentralize.org, 2020) tries to address the centralisation of the virtual layers - often assuming this will lead to decentralisation in other dimensions including power and political control (Buterin, 2017).

Advantages and disadvantages of decentralisation

Different network topologies come with particular advantages and disadvantages, that vary with the degree of centralisation, and the ways networks become more or less distributed over time. Distributed networks are more resilient to failure but incur a cost to maintain coordination. Centralised networks are much easier to maintain, but the central node can be a performance bottleneck and a single point of failure.

In the following table we summarised the main costs and benefits associated with distributed and centralised networks.

In distributed networks, each node has a wide range of responsibilities and associated costs. A distributed network is only operational if there is a coordination mechanism between the nodes.

In the absence of robust solutions to the problems of coordination and fault tolerance, Lamport et al (2019) have noted, a distributed system is only a network “in which the failure of a computer you didn't even know existed can render your own computer unusable''.

Coordination problems must address, for example, how nodes reach each other (as in the internet routing system); how to deal with competition and race conditions (when multiple nodes want to use the same limited resource, such as a network printer); or how the system’s operational and development processes are governed (Katzenbach and Ulbricht, 2019). While many of the more common technical coordination issues in distributed networks have been addressed and have robust solutions, governance, which might be equally distributed, remains experimental (Arruñada and Garicano, 2018; Atzori, 2017; De Filippi and Loveluck, 2016a). Most of the distributed applications and services have bare-bones, generic governance frameworks. Governance, however, entails more than, for example, an infrastructure of secure voting. Effective participation in the governance mechanisms of a distributed social, political, economic system also requires substantive investment from the individual in terms of knowledge, time, attention, engagement.

The problem of fault-tolerance has to do with failures and attacks[1], and ensures that the overall network remains functional and continues to work to achieve its overarching goal while some of its components fail. Attacks that are particular to distributed and decentralised systems include DDoS (Distributed Denial of Service)[2] and Sybil attacks[3]. Distributed architectures are designed to be tolerant of the failure of a relatively high number (typically 30-50%) of all nodes in a network[4]. But Troncoso et al. (2017) also showed that decentralisation, done naively, may multiply the ‘attack vectors’, and security risks, not least the breach of privacy. Distributed architectures might also be worse in terms of availability and information integrity, as the failure of nodes may have a fundamental impact on these properties.

In distributed networks, individual nodes must also take care of their own security, and availability. Distributed networks also have issues with efficiency, such as the transaction throughput of blockchain systems, or the bandwidth and latency in the TOR routing network.

In return, when done right, distributed networks offer higher resilience. There is also a lower risk of any central actors taking control, or exercising unilateral power over the network. For this reason, decentralised network topologies are also used to achieve privacy, censorship resistance, availability, and information integrity information security properties (Hoepman, 2014; Troncoso et al., 2017b).

In centralised systems coordination is taken care of by central actors who can specialise, and this leads to efficiency gains. There are costs to this, however, including making the network more vulnerable to the failure, or the abusive behaviour of that central node. Since network transactions run through a specific server, this grants those who control that server significant powers to observe, manipulate or cut off traffic (Troncoso et al., 2017), as well as to control, censor, tax, limit or boost particular social interactions, economic transactions, information exchange among network participants, and unilaterally set the conditions of interactions within the network.

To illustrate this cost-benefit calculus consider the privacy protecting TOR network. TOR is able to give reasonable levels of privacy at the cost of using a distributed network to route messages with lower speeds, and larger latency. These costs are seemingly too large for everyday users who are willing to settle for lower levels of privacy. On the other hand, for political dissidents who fear government retribution, journalists, whose integrity depends on their ability to protect their sources, and other groups for whom strong privacy is essential, the cost-benefit analysis justifies the higher costs of using this distributed network.

Both the costs and the benefits of using distributed network topologies are dynamic in nature, and are heavily dependent on factors both internal and external to the network. For example, the unresolved problem of distributed governance often creates a certain structurelessness in the social, political dimensions of distributed networks. As Freeman (1972) or De Filippi and Loveluck (2016) pointed out, seemingly unstructured social networks risk informal centralisation of their governance. In fact, blockchain networks have highly centralised forms of governance. (Azouvi, Maller, and Meiklejohn, 2018; De Filippi, 2019; De Filippi and Loveluck, 2016b; Musiani, Mallard, and Méadel, 2017; Reijers, O’Brolcháin, and Haynes, 2016). Blockchain networks may also suffer from centralisation in other dimensions of power. For instance, the proof-of-work (PoW) protocol randomly assigns a miner node to validate the latest batch of transactions for a relatively large reward to minimize the risk of a malicious miner hijacking the transaction ledger. The corresponding low chance of being rewarded for being honest forced miners to aggregate into a handful of coordinated mining pools, which control the vast share of this critical resource in an otherwise physically, geographically distributed network. The alternative approach, proof-of-stake (PoS) requires that those who wish to validate transactions stake their decisions with hard (crypto)cash: the larger the stake, the larger the validating power. PoS may remove mining pools, but creates another form of centralised power, namely that of capital. On the other hand, the increasing legal pressure on P2P file sharing networks, in particular on central nodes, pushed these projects towards increasingly distributed architectures, such as bittorrent networks, with distributed hash tables (Giblin, 2011).

Distributed systems in practice

While distributedness, as we have noted earlier, have been proposed as a general template for both the physical and the virtual digital networks, truly distributed networks only established themselves in particular niche applications, due to their particular cost-benefit balance.

P2P systems: P2P networks collectively make a resource (computation, storage) available among all nodes in the network. Examples of peer-to-peer computation networks are Seti@Home[5] and Folding@home[6]. Napster, Kazaa, or the bittorrent networks are peer-to-peer storage and file sharing networks, used to distribute copyrighted works under conditions of limited legal access (Johns, 2010; Patry, 2009). The peer-to-peer nature of these networks made it much harder to censor them and to take down material that infringed on copyrights (Buford, Yu, and Lua, 2009).

Distributed ledgers are distributed data structures where a set of bookkeeping nodes (sometimes called miners), interconnected by a peer-to-peer network, collectively maintain a global state without centralised control (Narayanan et al., 2016). Bitcoin (Nakamoto, 2008) was the first distributed ledger, inventing blockchain as the data structure to store transaction histories of digital tokens capable of digitally representing units of value. Ethereum generalised the distributed ledger from recording transactions to instead process code and store the state of the network. Bookkeeping nodes maintain consensus on the list of executed transactions and their effect on the global state, as long as a specified fraction of the bookkeeping nodes is honest and active.

Secure multiparty computation allows several participants to collectively compute a common output, which is based on each of their private inputs. Instead of sending the private inputs to one central coordinator (that would therefore learn the values of all private inputs), the algorithm to compute the value is distributed and the computation is done on the devices of the participants themselves, thus ensuring that their inputs remain private (Cramer, Damgard, and Nielsen, 2015; Yao, 1982).

Decentralisation as a social template

Distributed networks have brought experimentation with new coordination mechanisms, new ways to manage risks, and failures, lowering transaction costs and removing central powerful positions in technical terms. Proponents of disintermediation hope that these same logics provide new tools for horizontal social coordination, and the removal of political, economic, or social intermediary institutions, previously fulfilling those tasks.

The centralisation/decentralisation dichotomy is often framed in terms of power asymmetries, where distributed architectures are proposed as an alternative to authoritarian, coercive forms of political power. This dichotomy rests on a number of assumptions about power, and often does not fully account for the ways that, in practice, decentralisation in one dimension might produce or be enabled by centralisation in another. In terms of economics, distributed digital networks often align with the concept of perfectly competitive markets, designed to prevent the emergence of entities in a monopoly position, whether information, resource, or other monopoly (Brekke, 2020). Yet in practice, markets tend to rely heavily on a regulatory body to ensure fair competition. Distributed ledger technologies (DLT) have also offered a possible technical solution to the loss of trust in institutional actors (Bodó, 2020), by setting up networks with little reliance on trusted third parties, and minimising the need to have trust in interpersonal relations (Werbach, 2018). Yet in practice, DLT brings along new kinds of intermediaries, from interface designers and wallet developers, to exchanges, miners, full nodes and core developers, therefore requiring new forms of accountability methods.

The recent popularity of distributed technical networks raised important questions about the preferred modes of social, political, or economic organisation. Digital innovation changes the costs and benefits of coordination and collaboration (Benkler, 2006). This highlights questions about the roles that intermediaries play in those relations (Sen and King, 2003). For example, cryptocurrency technology may have successfully demonstrated that there is no need for a centralised intermediary to keep accounts, or even run an asset exchange. However, that is not the only function of banks and exchanges. Trust generation, due diligence, risk assessment, conflict resolution, rules provision, accountability, insurance, protection, stability, continuity, and education are arguably also core functions of the banking system, offered in conjunction with the bookkeeping function. A second set of questions address the various layers which constitute a complex techno-social system, and the fact that a distributed topology at one layer, may not produce, require, or allow a distributed form of organisation at the other. In fact, often highly centralised governance is a precondition of a distributed system to function, as is currently the case in blockchain based systems. Another example would be the role of governments to ensure fair and open competition on various markets, such as anti-trust regulation, or in politics.

Conclusion

Decentralised and distributed modes of organisation are well defined in computer science discourses and denote a particular network topology. Even there, they can be understood either as an engineering principle, a design aim, or an aspirational claim. In the decentralisation discourse these three dimensions are often conflated without merit. A decentralised network design might not produce decentralising effects and might not either necessarily be decentralised in its actual deployment.

When the technical decentralisation discourse starts to include social, political, or economic dimensions, the risk of confusion may be even larger, and the potential harms of mistaking a distributed system for something it is not, even more dangerous. Individual autonomy, the reduction of power asymmetries, the elimination of market monopolies, direct involvement in decision making, solidarity among members of voluntary associations are eternal human ambitions. It is unclear whether such aims can now suddenly be achieved by particular engineering solutions. An uncritical view on decentralisation as an omnipotent organisational template may crowd out alternative approaches to creating resilient, trustworthy, equitable, fault resistant technical, social, political or economic modes of organisation.

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1. Failure can mean multiple things: the unavailability of a node; the unreliable, unexpected, or unaccounted for behaviour; and any malicious, manipulative or destructive behaviour. Failures can happen for a number of reasons: stochastic processes which may equally affect any node in a network due to their intrinsic properties; failures in some of the underlying layers: energy failures, environmental force majeure; as well as failures due to attacks by malicious actors. ↑

2. A DDoS attack is when the bandwidth of a network is overloaded by flooding it with traffic coming from a distributed set of nodes. ↑

3. A Sybil attack is when some actor/s create/s many nodes such that the network seems distributed, when in actual fact it might be controlled by a single or small set of actors. ↑

4. The so-called Byzantine Agreement protocols allow a system to agree on a common output even if at most one-third of the members are faulty (in the Byzantine sense, meaning that they are malicious) (Lamport, Shostak, and Pease, 2019). But this is only the case under certain conditions. In particular, fully asynchronous systems (where there is no bound on the time it can take for a message to arrive or the time a node may take to complete a step) defy solutions to the Byzantine Agreement problem (Fischer, Lynch, and Paterson, 1985). This highly theoretical line of research re-emerged with the birth of Bitcoin and the subsequent explosion of distributed ledger technologies that exactly needed what Byzantine Agreement offered: reaching agreement on the global order of transactions, when faced with potentially malicious adversaries. ↑

5. Started in 1999, it’s aim is detecting intelligent life outside Earth, see https://setiathome.berkeley.edu ↑

6. Started in 2020, it’s aim is to simulate protein dynamics, see https://foldingathome.org ↑

Primavera De Filippi, French National Centre for Scientific Research
Ori Shimony, dOrg
Antonio Tenorio-Fornés, Universidad Complutense de Madrid

Definition of the term

A. Origin

Technologies such as the internet, or blockchain, enable large scale interactions among total strangers. Reputation systems (Resnick et al., 2000) appeared as a solution to facilitate these interactions when some level of trust was required, such as for online shopping in peer to peer marketplaces like eBay, or online production communities (Benkler, 2006). Yet, these systems generally relied on a centralised operator, in charge of managing user reputation.

Many decentralised reputation systems have been developed before the advent of blockchain technology (Hendrikx, 2015), most relying either on maintaining a personal list of trusted and untrusted nodes; aggregating such reputation information from other trusted nodes (with a certain degree of transitivity such as in web-of-trust); or using Distributed Hash Tables to manage a global reputation system (Chawathe et al., 2003). These decentralised reputation systems have been used in particular for applications such as file-sharing (Napster) and bandwidth routing (Tor).

The advent of blockchain technology introduced new opportunities for the development of next-generation reputation systems that rely on persistent global state and immutable transaction histories. This allows for transparency and security guarantees that were unavailable in previous distributed systems. Furthermore, blockchain technology made it possible to achieve portability and interoperability between different reputation systems, serving as an open neutral shared data store that can be leveraged by multiple services.

B. Evolution

We delineate below the different types of reputation systems that emerged over time with the development of new blockchain-based networks and decentralised applications. As opposed to the early blockchain networks like Bitcoin, whose governance is inherently plutocratic, these new applications have developed new decentralised reputation systems in order to implement more sophisticated governance systems which are not exclusively based on market dynamics.

Bitcoin (Nakamoro, 2009) first used blockchain technology to create a distributed payment system operating on top of a peer-to-peer network. The governance of Bitcoin did not rely on trust or reputation. Instead, the influence of every network node is determined by the amount of resources engaged into the network: the greater the amount of resources, the more influence one has in the network. Many of the other blockchain-based networks that followed suit relied on similar protocols, also based on a plutocratic governance model (i.e., the amount of hashing power in the case of proof-of-work or the amount of tokens holding in the case of proof-of-stake).

The introduction of reputation in the context of these early blockchain-based networks was seemingly driven by a desire to move away from a model of plutocratic governance, towards a more meritocratic governance system. Early reputation systems have been implemented at the infrastructure layer, as trust-based alternatives to the proof-of-work or proof-of-stake consensus algorithm. For instance, delegated proof-of-stake (Larimer, 2014) allows for a more meritocratic system, based on merit or perceived trustworthiness. As a result, anyone holding a particular amount of reputation within a blockchain community will have influence in proportion to that amount of reputation.

At the application layer, the introduction of reputation in the blockchain space was also an attempt to move away from the perception of blockchain technology as a purely trustless system, to enable the establishment of more sophisticated systems where some actors can be trusted. As argued by Hawlitschek & al. (2018), the introduction of reputation is necessary for the establishment of architecturally trustless systems (i.e., blockchains) that operationally rely on trust. In other words, while at the infrastructure level, the operations of a blockchain-based network do not depend on any trusted third party, at the application level, it might be beneficial to allow for certain interactions to be strengthened or facilitated by a particular amount of trust. The need for a reputation system thus ultimately depends on the types of applications at hand. On the one hand, trustless systems such as Bitcoin are based on the assumption that no one can or shall be trusted. Hence, these systems are designed to entirely eliminate the need for trust, relying on cryptographic primitives and proofs in order to ensure that people behave according to the rules (Ali & al., 2016). On the other hand, there are many human-sensitive services (e.g., peer-to-peer marketplaces like Uber, Airbnb, or eBay) based on the assumption that some actors can be trusted to behave honestly. These systems rely on reputation in order to help users assess the trustworthiness of the other users interacting on these platforms. In order to provide these types of human-sensitive mediation services, blockchain-based applications need to also rely on some kind of reputation system.

C. Coexisting uses/meanings

Existing blockchain reputation systems vary widely in how reputation is earned and utilised.

In many blockchain-based marketplaces, reputation does not have an explicit or software-defined role, but acts as a signal of trustworthiness. For instance, in service marketplaces (e.g., Gitcoin, Bounties Network), users can decide who to hire or work for based on transaction histories and summary statistics. Similarly, in digital goods marketplaces (e.g., Rarible, OpenSea), a buyer can review the seller’s transaction history to evaluate the quality of goods for sale before making a purchase.

In blockchain-based social media (e.g., Steemit, Hive, Sapien, Relevant) and work networks (e.g., Colony, Sourcecred), reputation represents a user’s evaluation weight on other users’ contributions. Reputation can be global in scope or limited to a specific community or domain. Evaluation-weighting alters reputation dynamically, as users continuously influence each other’s reputation scores in proportion to their own reputation. Some systems also incorporate time-based mechanisms to decay reputation with inactivity.

In blockchain-based governance frameworks (e.g., Aragon, DAOstack, Moloch), reputation often determines a user’s voting weight on proposals in a given organisation. Reputation can also entitle the user to a proportional claim of the organisation’s assets or ongoing revenues. Reputation is often modified through community voting, where the votes of community members are weighted by their reputation (e.g., a community can vote whether to give 50 reputation points to Alice or remove 100 reputation from Bob). Just as in social media cases, reputation can also be modified by dynamic criteria stipulated by the community, such as reputation rewards for voting with the majority, creating proposals that pass, or reputation penalties for the reverse.

Issues currently associated with the term

A. Different types of reputation

First of all, it is important to distinguish between two different types of reputation systems: “personal” and “global” reputation systems (Hendrikx 2015).

• Personal reputation systems are specific to an individual. They represent the standard mechanism of peer-to-peer reputation assignment. These systems are designed to assign a personal reputation score to each member of a particular network or community, although such a score will ultimately be relevant only to one specific individual. Hence, these systems necessarily rely on direct user input: users are expected to score each of their interactions with other community members, in order to help the system compute their corresponding reputation score. However, these systems often suffer from scalability issues. Indeed, the purpose of a reputation system is to provide information about the qualities of different users in a given domain, so that other users can make informed decisions about who they wish to interact with. Yet, a personal reputation system has limited capacity to do so, because it is not possible (or too costly) for a single user to evaluate the qualities of all the users in the system. In order to overcome these limitations, many of these reputation systems often implement a web of trust mechanism, leveraging the information submitted by other people (who are regarded as trustworthy by the user) in order to compute the personal reputation score of those with whom such user did not yet have a sufficient amount of interaction.
• Global reputation systems are not specific to any community member, but rather to the community as a whole. These systems assign a single and unique reputation score to the different actors in a particular community or network, which will be regarded by all community members as the sole and legitimate score. Besides, these reputation systems are rather easy to implement in a centralised platform; they are much more difficult to implement in a decentralised setting, since they require highly sophisticated mechanisms of reputation transfer that will not fall prey to Sybil attacks.

B. Sybil attacks and identity

Unlike popular online services, most blockchain-based systems have no central party to verify user identities, remove fake accounts, or patrol spam. While beneficial for privacy, this opens the door to Sybil attacks, where anyone can create multiple pseudonymous accounts to gain disproportionate influence over the system. The ongoing threat of Sybil attacks is a central challenge for building trust and authentic engagement in blockchain-based systems.

Well-designed reputation systems can mitigate the threat of Sybil attacks. First, systems can avoid Sybil attacks through the web of trust model, where a small set of known users slowly invites more users to be peer verified over time. Alternatively, blockchain reputation systems could rely on an external source of identity verification.

In cases where trust is needed but identity is not required, reputation scores can be used to avoid Sybil attacks. In these cases, the mechanism for earning reputation should measure difficult-to-forge value-added through peer verification. This way, an individual has to contribute just as much value regardless of how many accounts they spread the effort over, so there is no added incentive for Sybil attacks.

C. Privacy

In light of its attributes of transparency, censor-resistance, and immutability, blockchain technology can be instrumental to the operations of both personal and global reputation systems, enabling anyone to access and retrieve these scores, in order to compute both a personal and a global reputation score.

However, in order to protect the privacy of users, the reputation system should avoid permanently registering in a blockchain the association between real-world identities and the identities of the reputation system. In addition, users should be aware of the risks of linking real-world identities to their blockchain accounts. Maintaining this separation makes it possible for users to protect their privacy while allowing for anyone interacting within their blockchain-based identity to evaluate the risks of each user in that domain.

This is especially relevant in light of new European General Data Protection Regulation, which provides users with the possibility to request the erasure of specific information deemed inaccurate, inappropriate, or obsolete. Given the immutability of a blockchain, the recording of any type of data that can affect the reputation of a particular persona would potentially violate the provisions of the law, insofar as the persona can be linked back to a real-world identity.

D. Oligarchies and power distribution

The use of reputation systems also raises concerns about power concentration. The creation and consolidation of oligarchies are common in online communities. However, reputation systems might reinforce inequalities in such communities, as powerful actors are more likely to be trusted and increase their reputation while those with low reputation will have fewer opportunities to increase their reputation. Blockchain systems use reputation as a source of economic or political power: these options are explicitly made available in many governance frameworks. Thus, the accumulation of reputation in such blockchain systems might result in even stronger power inequalities than in other online communities.

E. Amplification of social inequalities

It is worth considering the potential biases reputation systems incorporate and reproduce. First, not all activities or contributions are a source of reputation in online communities (Rozas & Gilbert, 2015). Some activities, such as contributing source code to free software projects are explicitly valued in these systems, while others such as community organising, or affective labour, typically carried by women (Iosub et al., 2014) are often invisible to these reputation systems. These types of biases can trigger new forms of inequalities incorporated directly into the algorithms managing a platform, such as higher work time and lower average wage for women in the so-called gig-economy (Barzilay, 2016). We have briefly considered the reproduction of gender inequalities by reputation systems. However, other dimensions of social injustice such as race or class, and their interactions, should also be considered when studying how reputation systems reproduce them.

Conclusion

Coming up with a single definition of reputation is a difficult task, in light of the different ways in which the term has been used over time and in different disciplines. From an empirical standpoint, there are many different typologies of reputation systems (centralised vs. decentralised, personal vs. global), each with their own advantages and drawbacks. We provide here a definition of the term reputation that is specific to the blockchain space and hopefully generic enough to encompass the wide variety of decentralised reputation systems which have been developed so far.

In general terms, a decentralised reputation system is an online mechanism that enables participants to evaluate each other's trustworthiness. Reputation of a particular actor is usually calculated by aggregating the evaluations of multiple peers—each assigning a score that represents the standing, status, or reputation of such an actor within the system, based on past actions or behaviours. In a blockchain-based system, these evaluations are recorded in a blockchain and used by the system in order to calculate a final score. This score can be leveraged both explicitly through functions in the code (voting power, economic rights) or implicitly as a means of signalling an entity’s trustworthiness.

References

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Rozas, D., & Gilbert, N. (2015). Talk is silver, code is gold? Contribution beyond source code in Free/Libre Open Source Software communities. CRESS Working Papers.

Jaya Klara Brekke, Durham University
Aron Fischer, Colony

Digital scarcity is a credibly maintained limitation, imposed through software, of digital information, goods or services that may be accessed and used entirely digitally.” (Conclusion below)

The history of digital scarcity

Some of the earliest uses of the term digital scarcity stem from the early 2000s and describe the scarcity of access to IT resources and the underlying physical resources that computers and networks rely on - i.e. “the scarcity of the digital” (Weinberger, 2003; Hammersley, 2003; Chaudhry & Shipp, 2005). In one prominent example, legal restrictions on access to radio frequencies stymied the growth of communications networks at the beginning of this century - and activists and network operators bemoaned the resulting digital scarcity (Weinberger, 2003; Hammersley, 2003). The explosive growth of mobile telephony and the ever growing demand for digital services on mobile devices led to public auctions of radio spectrum usage rights in order to alleviate this particular form of legally imposed scarcity (Wikipedia, 2020b). Digital scarcity, as referring to the availability of IT resources, has also described issues of accessibility due to forms of marginalisation (the digital divide), including lack of access because of disabilities. In this specific context, the term digital scarcity is used to describe “the dearth of accessible technological and other resources, lack of political will to address the problem, and general ignorance about digital access” (Chaudhry & Shipp, 2005, sect. 6, para. 10).

As internet access has become more widespread, and as an increasing amount of content is consumed digitally (text, news, music), the usage of the phrase has shifted. Digital scarcity now refers to the imposition of limits and conditions on availability and access to digital content. Digital information can be easily copied and is by nature not scarce or rivalrous; it can be shared at next to no cost, with no reduction in availability or quality: ‘Digital reproduction frustrates notions of originality’ (O’Dwyer, 2018, p. 874). As assets that were previously scarce or rivalrous (e.g., number of copies of a book or record) became increasingly digital, this led to no end of difficulties in the realm of copyright enforcement (Perzanowski & Schultz, 2016). In this context, digital scarcity describes limitations set on the access to data, in order to protect business models that depend on scarcity, as well as in the development of new forms of digital markets and economies.

Copyright-based industries for example, seek to impose digital scarcity to prevent copying of data. In the case of music, the value to the customer is clear, but scarcity needs to be maintained in order to protect industry profits. In the words of Warner Music Group chairman Edgar Bronfman: “Music is ubiquitous to a degree that also I think is probably not helpful for the industry” (Resnikoff, 2007). On the other hand, manipulating (perceptions of) scarcity is used a priori as a marketing tool in order to create demand for digital goods and services (Fortin, 2007). This (pre-bitcoin) notion of scarcity is perhaps best summed up in the following: “’textitOne key to the success of digital goods business models is to maintain the scarcity of the digital goods. Since digital goods are digital, they cost nothing to copy. Free copies of digital goods would reduce demand for paying for the same item. In a closed system, it is easier to maintain scarcity. The company controls the supply of all digital goods completely.” (Partners, 2008).

The rise of the internet, and the ease with which data could be copied, led to movements of digital activists seeking to open the access to information entirely (‘information wants to be free’). These movements often clashed with intellectual property and copyright-based industries (Dahlstrom et al., 2006; Swartz & Lessig, 2016), and these clashes in turn informed much of the development of peer-to-peer systems that would enable circumvention of the copyright industry, and free access to information (Oram et al., 2001; Andersson, 2011). Arguments were put forward that nothing digital is genuinely scarce and that any imposed scarcity is not just artificial, but also objectionable. “In digital goods, scarcity doesn’t exist” and “the economics of scarcity doesn’t apply digitally” (Masnik, 2006a). Virtual goods are only scarce by design and, as such are scarce by choice, and “that’s a recipe for trouble” (Masnik, 2006b), see also (Picard & Wildman, 2015, p. 242).

If we look beyond mere data, there are digital resources that are inherently limited, such as bandwidth or short domain names. Short domain names for instance (using the English alphabet) are scarce due to the limitations of the alphabet, and may sell for millions of dollars (Wikipedia, 2020a) but scarcity of top-level-domains (such as .com, .org, .luxe, or .io) is artificially created and maintained by the Internet Assigned Numbers Authority (IANA) and the Internet Corporation for Assigned Names and Numbers (ICANN). Sometimes the creation of digital scarcity is accidental and its maintenance is due to a failure in governance. A prominent example is the dearth of IPv4 (Internet Protocol version 4) addresses (Rodriguez, 2012). The looming shortage has been apparent since the 1990s, and yet the coordinated migration to the newer IPv6 has yet to be achieved on a large scale.

Bandwidth meanwhile is a hotly contested commodity and the ability to impose artificial scarcity on specific customers or types of content is subject to intense legal wrangling (Smith, 2010). Indeed, the imposition of limitations on bandwidth has been used as a means to clamp down on peer-to-peer file sharing or to degrade performance of a competitor’s services, known as throttling. In opposition to such practices, the campaign to protect net neutrality aims to protect the free flow of data and ensure that internet providers are not legally allowed to discriminate based on content or source or usage (Wu, 2003); for an overview, see (Finley, 2020).

Digital scarcity in the age of blockchains

In the context of the Bitcoin blockchain, digital scarcity refers to the limitation on the total supply of bitcoin. In contrast to the previous meaning, access to data is not restricted, and indeed the network relies on the blockchain data being freely available for anyone to copy in order to function securely. What determines a specific bitcoin is thus not its uniqueness as a piece of data, but rather its function as a verified entry in a distributed ledger.

It should also be noted that while both the cryptocurrency ether (on the Ethereum blockchain) and bitcoin (on the Bitcoin blockchain) are finite. The total amount of ether rises linearly whereas the total amount of bitcoin rises at a linear rate that is then halved every four years, so that the total number of bitcoins asymptotically approaches 21 million. As both blockchains enable finite transfers of a finite amount of digital currency, and the total amount in question grows only (sub-)linearly, they both exhibit digital scarcity and are not subject to uncontrolled inflation brought about by an out-of-control increase in money supply.

There is a further aspect of scarcity inherent in Bitcoin (and indeed any blockchain). Just as the bitcoin supply is limited to 21 million, so too is the Bitcoin network limited to seven transactions per second. These are both examples of digital scarcity, but while the former limit is entirely arbitrary and determined in protocol designs, the latter cannot be arbitrarily raised as it is bounded by bandwidth and processing constraints.

In the digital realm, data can be copied, databases re-indexed and values of variables can be changed - at least in principle. As the copyright battles of the 1990s and early 2000s made clear, maintaining digital data scarcity by preventing copies is nearly impossible. Copying data is just too easy and ubiquitous. However, establishing referential scarcity, where references are ledger / database entries (and the referents are anything from cryptocurrencies to cryptokitties), is possible as long as it can be credibly established that the scarcity will be maintained and the rules adhered to. The crucial aspect of referential scarcity is not control over data availability, but control over manipulation of the data in question. This was the innovation of Bitcoin and the invention of the blockchain as a decentralised ledger technology.

With the invention of Bitcoin, digital scarcity could be established without the need for a central entity to enforce it. Instead, the network uses cryptographic hash cycles (mining) in order to agree on, maintain and enforce a record of valid transaction data. Cryptocurrencies are not the first databases with finite number entries, but they are the first in which changes to the entries cannot be forced by the entities providing the computing infrastructure. The notion that centralised control over a database is necessary to ensure digital scarcity was thus overturned[1].

As more advanced and general-purpose blockchain networks such as Ethereum appeared, the scope for scarce ledger entries grew. Aside from scarcity of cryptocurrencies and currency-like ‘tokens,’ a new class of ‘unique digital items’ known as non fungible tokens, or NFTs have appeared. These range from formal claims of ownership over a real-world (offline) asset, to purely digital collectibles (see, for example, Serada et al., 2020).

The rise of NFTs has led to experiments with new types of digital property where ‘the broader intention does not appear to be to reduce the circulation and reproduction of the work, but instead to create titles and derivatives from its use and circulation’ (O’Dwyer, 2018, p. 876). This for example implies producing a digital ‘original’ where its source and provenance is considered important enough to be able to acquire value as a ‘unique’ digital object, but where ‘copies’ can nevertheless circulate freely. However, there are considerable doubts about whether the possibilities afforded by distributed ledgers for new forms of digital scarcity will challenge much of the economic dynamics of property rights, or financial speculation and benefit producers of digital goods (Zeilinger, 2018; Lotti, 2016).

Issues currently associated with the term

At the time of writing, the culture around blockchains is still young, and it remains highly politicised and polarised. This polarisation contributes to the confusion surrounding digital scarcity as it relates to ideas of value. Proponents of Bitcoin in particular argue that it is the limited supply of bitcoins (and that alone) that gives them ‘intrinsic’ value whereas supporters of other blockchains (such as Ethereum, Cardano, Polkadot) argue that utility of the network, its ‘extrinsic’ value, is far more important. From their perspective, the limited performance of blockchain networks, which to the Bitcoin network is a feature, in fact inhibits the usability of the network and therefore growth of value.

In the context of the Bitcoin blockchain, digital scarcity tends to refer to the limitation on the total supply of bitcoin. The single-minded focus on Bitcoin’s supply is not without precedent. New bitcoin are created in a staggered process, intended to replicate the dynamics of gold - it is increasingly hard to find, and the total supply is limited. Media theorist Golumbia (Golumbia, 2016) traces these ideas in Bitcoin via the Austrian school of economics to right-of-centre US monetary ideas (and hard right conspiracy theories) that view the governance of money supply with deep suspicion. This line of thought views the government’s very purpose as being the theft of ordinary people’s wealth by printing money and causing hyperinflation. The broader consensus however is that good monetary governance, rather than no governance, is key to addressing not only hyperinflation but also other economic concerns. The correlation of hyperinflation with money supply draws on the Quantity Theory of Money (QTM). There are several real world examples of hyperinflation, from Zimbabwe (Ncube, 2019) to video games (Earle, 2013); (Picard & Wildman, 2015 p. 248). But where proponents draw on QTM as a reason for absolute monetary and digital currency scarcity, critics – most notably of the school of Modern Monetary Theory, argue that money supply is not the main issue of concern, but rather how the supply is governed and what it is directed towards (Kelton, 2020).

Conclusion

Digital scarcity describes a credibly maintained limitation, imposed through software, of digital information, goods or services that may be accessed and used entirely digitally. This includes limitations to entries in a ledger or database (including cryptocurrency entries in a blockchain or top-level domains in the Domain Name System), as well as limitations in access to computing resources such as network addresses, bandwidth, or (again in the context of blockchains) transactions-per-second, wherever these limits go beyond the physical limits imposed by hardware. The motivations for engineering digital scarcity tend to be in order to support business models that profit from scarcity or uniqueness in the digital realm.

Older usage of the term includes physical limitations in processing power and bandwidth, and limitations in physical access to computing devices and computing services. Since in such cases, scarcity is not imposed through software, it is included in the history of the term but not in the current definition.

References

Andersson, J. (2011). The origins and impacts of the swedish file-sharing movement - a case study. Critical Studies in Peer Production (CSPP), 1(1), 1–18.

Chaudhry, V., & Shipp, T. (2005). Rethinking the digital divide in relation to visual disability in india and the united states: Towards a paradigm of “information inequity.” Disability Studies Quarterly, 25(2). https://dsq-sds.org/article/view/553/730

Dahlstrom, D., Farrington, N., Gobera, D., Roemer, R., & Schear, N. (2006). Piracy in the digital age. University of California, San DiegoCSE 291 (D00) - History of Computing. https://courses.cs.washington.edu/courses/csep590a/06au/projects/digital-piracy.pdf

Earle, P. C. (2013). A virtual weimar: Hyperinflation in a video game world. Mises Institute [website]. https://mises.org/library/virtual-weimar-hyperinflation-video-game-world

Finley, K. (2020). The WIRED guide to net neutrality. Wired Magazine. https://www.wired.com/story/guide-net-neutrality/

Fortin, M. (2007). Digital scarcity: Does it still convert? Personal Blog. https://michelfortin.com/blog/digital-scarcity-does-it-still-convert/

Golumbia, D. (2016). The politics of bitcoin, software as right-wing extremism. University of Minnesota Press.

Hammersley, B. (2003). Radio active revolution. The Guardian. https://www.theguardian.com/technology/2003/jul/10/onlinesupplement2

Kelton, S. (2020). The deficit myth, modern monetary theory and the birth of the people’s economy. PublicAffairs.

Lotti, L. (2016). Contemporary art, capitalization and the blockchain: On the autonomy and automation of art’s value. Finance and Society, 2(2). https://doi.org/10.2218/finsoc.v2i2.1724

Masnik, M. (2006a). Economics of abundance getting some well deserved attention. Tech Dirt [website]. https://www.techdirt.com/articles/20061026/102329.shtml

Masnik, M. (2006b). In a world where everything is digital, economics gets screwy fast. Tech Dirt [website]. https://www.techdirt.com/articles/20061114/181724.shtml

Ncube, M. (2019). Introducing a new currency was zimbabwe’s only viable option. Financial Times, London. https://www.ft.com/content/f3e298c2-c8e7-11e9-a1f4-3669401ba76f

O’Dwyer, R. (2018). Limited edition: Producing artificial scarcity for digital art on the blockchain and its implications for the cultural industries. Convergence, 26(4), 874–894. https://doi.org/10.1177/1354856518795097

Oram, A., Lessig, L., Kan, G., Miller, J., O’Reilly, & Associates, Inc. S. (2001). Peer-to-peer: Harnessing the benefits of a disruptive technology. O’Reilly.

Partners, L. V. C. (2008). Three use cases for virtual goods. blog posting. https://lsvp.wordpress.com/2008/01/28/three-use-cases-for-virtual-goods/

Perzanowski, A., & Schultz, J. (2016). The end of ownership. Personal property in the digital economy. MIT Press. https://mitpress.mit.edu/books/end-ownership

Picard, R. G., & Wildman, S. S. (2015). Handbook on the economics of the media. Elgar. https://doi.org/10.4337/9780857938893.00018

Resnikoff, P. (2007). Bronfman ponders digital scarcity, retreads strategy. Digital Music News [website]. https://www.digitalmusicnews.com/2007/08/08/warner-2/

Rodriguez, R. (2012). IPv4 scarcity. Internet Society [website]. https://www.internetsociety.org/blog/2012/11/ipv4-scarcity/

Serada, A., Sihvonen, T., & Harviainen, J. T. (2020). CryptoKitties and the new ludic economy: How blockchain introduces value, ownership, and scarcity in digital gaming. Games and Culture. https://doi.org/10.1177/1555412019898305

Smith, C. E. (2010). Net neutrality, full throttle: Regulation of broadband internet service following the comcast/bittorrent dispute. Santa Clara Law Review, 50(2). https://digitalcommons.law.scu.edu/lawreview/vol50/iss2/7

Swartz, A., & Lessig, L. (2016). The boy who could change the world : The writings of aaron swartz. The New Press. https://thenewpress.com/books/boy-who-could-change-world

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1. Meanwhile, in some cases centralised control does not guarantee the maintenance of digital scarcity either. This is evident not least from the video game ‘Diablo 3’: although the publisher - Blizzard Entertainment - nominally had complete control over all aspects of the game, they could not forestall runaway hyperinflation in the in-game economy (Earle, 2013). ↑

Ingolf G.A. Pernice, Weizenbaum-Institute for the Networked Society
Brett Scott, Independent researcher

Introduction

Many scientific publications simply assume the meaning of the term cryptocurrency to be common knowledge or, at most, sketch it roughly.[1] We follow the evolution of the term starting with Bitcoin to define what cryptocurrency is understood as today. Reasoning that its imprecise nature and the diversity of included phenomenons renders the concept impractical, we suggest using the term cryptocurrency in conjunction with specifying classifications.

Origin and evolution

The term cryptocurrency entered public usage with the surge of Bitcoin in 2009 - a protocol aimed at enabling a network of people connected together via peer-to-peer digital communications infrastructure to issue digital tokens and transfer them between themselves whilst securing the process through cryptography (Nakamoto, 2009). While the original proposition did not use the term cryptocurrency, Nakamoto presented the project as a peer-to-peer "currency" in a network and cryptography mailing list.[2] The term “cryptocurrency”, however, soon gained traction in online-chatter[3][4] and print media (e.g., Davis, 2011).[5]

An early distinction was made between the protocol - using the capitalised term Bitcoin - and the tokens, which used the lower-case term bitcoin. New bitcoins are ‘written into existence’ by a network participant (a so-called miner) who has succeeded in transforming the format of a bundle of proposed transactions (of previously issued bitcoins, along with a single request to issue new ones as a reward) in such a way that the bundle can be hitched to a chain of previously hitched bundles.

The origin of crypto in archetypal cryptocurrencies

Crypto might be seen as surrogate for cryptography, but could also have emerged from the cypherpunk movement, whose “dream was anonymous cash and other untraceable payment systems” (De Filippi, 2018, p. 19) in order to promote a system of crypto-anarchy (Ludlow, 2001, p. 4). Bitcoin's mission of leveraging “cryptographic proof instead of trust” (Nakamoto, 2009, p.1) fitted that goal.

The exact protocol specifications of Bitcoin and its descendants are summarised in Scheuermann and Tschorsch (2016). Cryptography enters its architecture in various ways. A few examples are the integrity of, and consensus on a joint transaction history as well as the authorisation setup for sending tokens. However, the use of the surrogate crypto for Bitcoin is slightly arbitrary in the sense that earlier attempts at creating digital currencies (compare e.g. Chaum, 1988) relied heavily on cryptographic techniques as well. Nevertheless, it might seem justified by the fact that cryptography plays a far more central role for Bitcoin than it does for fiat currencies.

The origin of currency in archetypal cryptocurrencies

The modern fiat monetary system consists of physical and digital credits – issued by state central banks, state treasuries, and private commercial banks – which circulate under a legal system that guarantees their redemption. The number of credits expands through issuance, after which they can be transferred in the course of exchange among those who use them, before being retired when they are returned to the issuers. This composite system of expandable-contractable credits is what we refer to as ‘money’ in everyday parlance.

In this context, the term cryptocurrency is controversial, because – from its inception – the name has simply assumed that the tokens are money tokens. The controversy is amplified by the fact that enthusiasts sometimes use the term performatively to make the normative point that crypto tokens ‘should be money’, or – alternatively – to deny that what we currently call ‘money’ is in fact money.

One strategy to negotiate these language politics is to initially strip the money assumption from the tokens by giving them the generic name crypto-tokens, and then listing their uncontroversial characteristics to compare them with fiat credits.

Tokens of first-wave cryptocurrencies are data objects created through accounting, much like the act of typing out the number ‘1’ creates the mental image of a ‘thing’. This is what is referred to as a ‘token’, but they are ‘blank tokens’. An example of a blank token in the physical world might be a clear plastic token with no inscription or rights attached to it. Bitcoin tokens, similarly, are empty signifiers (or floating signifiers), somewhat like the digital equivalent of blank physical tokens, but with strict supply limits[6]. These blank digital tokens however, are promoted with a name and branded logo that serves as a mental image for them, without which they would be almost entirely featureless.

The tokens can be said to be digital bearer instruments, in the sense that transfers can only be initiated by the possessor of a private key that can unlock an ‘unspent transaction output’. The ‘bearer-instrument-like’ nature is one reason why cryptocurrency sometimes gets referred to as ‘digital cash’ (physical cash being the bearer-instrument form of fiat currency).

The tokens move around – Bitcoin and some of its descendants are processing hundreds of thousands of transfers of tokens every day (compare Hileman and Rauchs, 2017). Furthermore, they have a price measured in fiat currency and their tokens can be split into smaller pieces, or combined into larger ones. The fact that split-able and lump-able tokens with a fiat currency price can be moved gives the system a ‘moneylike’ feeling, and – under a shallow definition of money as something that is issued and moved around in association with commerce – the term cryptocurrency feels loosely plausible in everyday conversation.

Most ‘purchases’ conducted with bitcoin tokens, however, take the form of countertrade. The token, priced in fiat currency, is compared to a good or service, priced in fiat currency, and from this comparison of two fiat currency prices emerges an exchange ratio between the token and the good or service. This is the conceptual equivalent of superimposing two two-way fiat currency transactions over each other and cancelling out the money flows, giving the residual appearance of the crypto-token being used as ‘money’ to ‘pay’ for a good or service.

Nevertheless, Bitcoin is used primarily for speculation (Baur, 2018) - buying the token with fiat currency with an intention to resell it for fiat currency – rather than using it to countertrade (‘pay’) for goods and services. This speculation (compare, among others, Yermack, 2015; Glaser et al., 2014; or Cheah, 2015) drives volatility in the fiat currency price of tokens, which - when analysed through the lens of the conventional ‘functions of money’ paradigm favoured by economic textbooks (money as a medium-of-exchange, a store-of-value and a unit-of-account), poses problems for the ‘moneyness’ of the tokens. Not only are they not widely accepted in exchange for goods and services, but they are not widely used to price things, and attempts to provide prices are unintuitive[7] (compare Yermack, 2016). They also struggle to consistently ‘store value’, if we interpret that to mean ‘maintain stable purchasing power’ (which in the case of Bitcoin means ‘maintain fiat price and countertrade ratios’). Put simply, while a person can generally predict how many bags of sugar $100 will command in a month, they will be very uncertain as to how much sugar they can obtain through Bitcoin countertrade in a month.

From archetype to modern manifestations

Beyond these debates about the validity of the original use of the term cryptocurrency, the term has been destabilised by the proliferation of alterations to traditional cryptocurrency systems. Thus, the term nowadays incorporates initiatives with very different technology, governance and economic token design approaches.

We will touch on four important developments that exemplify this change: privacy-preserving cryptocurrencies, stablecoins, first-layer tokens underlying smart contract platforms[8] and payment systems carried out by corporations using blockchain-based-technologies.[9]

The crypto in today’s cryptocurrencies

So-called privacy-preserving cryptocurrencies or ‘privacy coins’ (e.g., Zcash[10] or Monero[11]) are “alternative cryptocurrencies designed with the goal of providing stronger privacy guarantees than Bitcoin” (Genkin et al., 2018) and are leading to rising concerns with respect to anti-money-laundering and law enforcement (compare Tziakouris, 2020; or Ferrari, 2020). They hold true to the aim of replacing trust by cryptographic proof found in archetypal cryptocurrencies (compare Nakamoto, 2009; and Genkin et al., 2019) but increase the use of cryptography to ensure anonymity.

The broad trajectory in recent years, however, has been to decrease the crypto. Eyal (2017) concludes that “if attendees at recent blockchain events are any indication, cryptocurrencies have caught the attention of the mainstream financial technology (FinTech) sector” (Eyal, 2017, p. 39). Payment systems run by corporations but still called cryptocurrencies entered the stage (compare e.g. Libra[12] [13] [14]), and with traditional business starting to experiment with the technology inspired by Bitcoin, system requirements - and with it the respective security setups and use of cryptography - changed. The economic design for these more centralised payment systems, as well as for the majority of ‘stablecoin’ designs, led to reestablishing trusted third parties or intermediaries for token creation to a certain degree.[15]

A useful classification of projects from a technical standpoint involves rights for writing and reading transaction records. Peters et al. (2016) introduced a popular categorisation that can be used to classify the underlying infrastructure of cryptocurrency systems along the dimension “public” vs. “private” and “permissioned” vs. “permissionless”. In public-permissionless systems every participant in the network (node) can read transactions and write others to the ledger. For public-permissioned systems, only authorised nodes can write. In private permissioned systems, finally, even reading is restricted to authorised nodes. The more “private” and “permissioned” in its underlying infrastructure a system is, the further it is from the cypherpunk vision.

While many novel cryptocurrencies are far from the crypto-anarchist roots of archetypal token designs, the general idea of the replacement of trust in institutions by cryptography still plays a role in all cryptocurrency designs. However, given that even fiat bank payments use cryptography for security means that mere reliance on cryptography for security should not enter our definition of cryptocurrencies.[16]

The currency in today’s cryptocurrencies

Early cryptocurrencies had the declared intent of creating ‘digital cash’ or currency (see section 1.1.), but the proliferation of crypto token forms have destabilised how this is conceptualised. For example, the first-layer tokens that underlie smart contract platforms are called cryptocurrencies, but they exist first and foremost to activate smart-contracts rather than aiming to provide a payment solution for goods and services more generally (see Bartoletti, 2017).

Nevertheless, this more ‘limited purpose’ focus can be a strength, insofar as smart contract activation can be seen as a real service accessible via possession of the token, thereby ‘anchoring’ the tokens into a ‘real economy’, albeit one in cyberspace. By contrast, cryptocurrencies with blank-token designs and no native smart-contract platform offer little to no indication of their fundamental utility, which, according to Caginalp and Caginalp (2018) might be one of the primary reasons for the instability in their fiat currency prices. One response to archetypal cryptocurrency instability was the advent of ‘stablecoins’, which try to solve the issue of high volatility in purchasing power of Bitcoin and its descendants (Pernice, 2019). Stablecoins are tethered or pegged to fiat currencies, or ‘backed’ in some way with assets that have fiat currency prices. They are thus no longer ‘blank’ empty signifiers, and contain some reference point that is easier to estimate and communicate.

There are very different types of stablecoins, and recently several frameworks have tried to unify and abstract existing stabilisation techniques (e.g., Bullmann et al., 2019; Pernice et al., 2019; Moin et al., 2020; Sidorenko, 2019; or Clark et al., 2019). A national currency can be ‘tokenized’ by issuing a digital promise for it on a blockchain system, and such tokenised funds might indeed be categorised as a “new form of electronic money” (Blandine et al., 2019) and thus fall under the respective regulations for E-money, anti money laundering and counter terrorist financing regulations. This might ensure “moneyness” at least from a legal standpoint. With more complex stablecoin designs the legal case is not always clear, but from an economic standpoint their stability in purchasing power might contribute to an increase in their adoption as money in the future. Stablecoins, for now however, haven’t seen mainstream adoption in retail markets yet (Bullmann et al., 2019).

Conclusion

The neologism cryptocurrency is unstable in its meaning, and is applied to systems with diverse technical architectures and governance systems. Nevertheless, one way to unify the diverse uses of the term is to define it by some common intent among those who claim it, rather than by the diverse means via which that intent is enacted, and regardless of whether the intent is achieved in practice.

Thus a cryptocurrency system is:

A system intended for the issuance of tokens which are intended to be used as a general or limited-purpose medium-of-exchange, and which are accounted for using an often collectively-maintained digital ledger making use of cryptography to replace trust in institutions to varying extents.

Against such a backdrop, the singular term cryptocurrency can mean

A token, intended to be used as a general or limited-purpose medium-of-exchange, issued via a cryptocurrency system.

Nevertheless, to make the term more useful in public discourse, cryptocurrency should be coupled with specifying classifications from an economic (e.g., Bullmann et al., 2019; Pernice et al., 2019; Moin et al., 2020; or Clark et al., 2019), governance (e.g., Ziolkowski et al., 2020; or Beck et al., 2018; Hacker, 2019) or technological (e.g., Cachin and Vukoli, 2017; or Peters et al., 2016) point of view.

References

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Alharby, Maher, and Aad Van Moorsel. 2017. “Blockchain Based Smart Contracts : A Systematic Mapping Study,” Computer Science &amp; Information Technology (CS &amp; IT) <http://dx.doi.org/10.5121/csit.2017.71011>

Ammous, Saifedean. 2018. The Bitcoin Standard: the Decentralized Alternative to Central Banking (John Wiley &amp; Sons)

Barone, Raffaella, and Donato Masciandaro. 2019. “Cryptocurrency or Usury? Crime and Alternative Money Laundering Techniques,” European Journal of Law and Economics, 47.2: 233–54 <http://dx.doi.org/10.1007/s10657-019-09609-6>

Bartoletti, Massimo, and Livio Pompianu. 2017. “An Empirical Analysis of Smart Contracts: Platforms, Applications, and Design Patterns,” Financial Cryptography and Data Security Lecture Notes in Computer Science: 494–509 <http://dx.doi.org/10.1007/978-3-319-70278-0_31>

Baur, Dirk G., Kihoon Hong, and Adrian D. Lee. 2018. “Bitcoin: Medium of Exchange or Speculative Assets?,” Journal of International Financial Markets, Institutions and Money, 54: 177–89 <http://dx.doi.org/10.1016/j.intfin.2017.12.004>

Beck, Roman and Müller-Bloch, Christoph and King, John Leslie. 2018. “Governance in the blockchain economy: A framework and research agenda,” Journal of the Association for Information Systems, 19:10: 1 <https://arxiv.org/pdf/1707.01873>

Bullmann, Dirk and Klemm, Jonas and Pinna, Andrea. 2019. “Opinion: Valuation, Liquidity Price, and Stability of Cryptocurrencies,” ECB Occasional Paper, 230

Blandin, Apolline, Ann Sofie Cloots, Hatim Hussain, Michel Rauchs, Rasheed

Saleuddin, and others. 2019. “Global Cryptoasset Regulatory Landscape Study,” SSRN Electronic Journal <http://dx.doi.org/10.2139/ssrn.3379219>

Cachin, Christian, and Vukolić. 2017. “Blockchain consensus protocols in the wild,” arXiv preprint arXiv:1707.01873 <https://arxiv.org/pdf/1707.01873>

Caginalp, Carey, and Gunduz Caginalp. 2018. “Opinion: Valuation, Liquidity Price, and Stability of Cryptocurrencies,” Proceedings of the National Academy of Sciences, 115.6: 1131–34 <http://dx.doi.org/10.1073/pnas.1722031115>

Cheah, Eng-Tuck, and John Fry. 2015. “Speculative Bubbles in Bitcoin Markets? An Empirical Investigation into the Fundamental Value of Bitcoin,” Economics Letters, 130: 32–36 <http://dx.doi.org/10.1016/j.econlet.2015.02.029>

Chaum, David and Fiat, Amos and Naor, Moni. 2018. “Untraceable electronic cash,” Conference on the Theory and Application of Cryptography, 319--327

Chu, Jeffrey, Stephen Chan, Saralees Nadarajah, and Joerg Osterrieder. 2017. “GARCH Modelling of Cryptocurrencies,” Journal of Risk and Financial Management, 10.4: 17 <http://dx.doi.org/10.3390/jrfm10040017>

Clark, Jeremy, Didem Demirag, and Seyedehmahsa Moosavi. 2020. “Demystifying Stablecoins,” Queue, 18.1: 39–60 <http://dx.doi.org/10.1145/3387945.3388781>

Davis, Joshua. 2011. “The crypto-currency,” The New Yorker, Vol. 87, Condé Nast New York

Farell, Ryan. 2015. “An analysis of the cryptocurrency industry”. Wharton Research Scholars. 130.

Ferrari, Valeria. 2020. “The Regulation of Crypto-Assets in the EU – Investment and Payment Tokens under the Radar,” Maastricht Journal of European and Comparative Law <http://dx.doi.org/10.1177/1023263x20911538>

Genkin, Daniel, Dimitrios Papadopoulos, and Charalampos Papamanthou. 2018. “Privacy in Decentralized Cryptocurrencies,” Communications of the ACM, 61.6: 78–88 <http://dx.doi.org/10.1145/3132696>

Glaser, Florian and Zimmermann, Kai and Haferkorn, Martin and Weber, Moritz Christian and Siering, Michael. 2014. “Bitcoin-asset or currency? revealing users' hidden intentions,” Revealing Users' Hidden Intentions. ECIS

Hacker, Philipp. 2019. “Corporate Governance for Complex Cryptocurrencies?,” Regulating Blockchain: 140–66 <http://dx.doi.org/10.1093/oso/9780198842187.003.0008>

Härdle, Wolfgang Karl, Harvey, Campbell R, Reule, Raphael C G. Journal of Financial Econometrics, Volume 18, Issue 2, Spring 2020, Pages 181–208, <https://doi.org/10.1093/jjfinec/nbz033>

Hileman, Garrick, and Michel Rauchs. "Global cryptocurrency benchmarking study." Cambridge Centre for Alternative Finance 33 (2017): 33-113.

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Peters, Gareth W., and Efstathios Panayi. 2016. “Understanding Modern Banking Ledgers Through Blockchain Technologies: Future of Transaction Processing and Smart Contracts on the Internet of Money,” Banking Beyond Banks and Money New Economic Windows: 239–78 <http://dx.doi.org/10.1007/978-3-319-42448-4_13>

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1. The meaning of cryptocurrency is outlined briefly in White (2014), Farell (2015), Lansky (2018), Aggarwal (2018), Chu et al. (2017), Sovbetov (2018) and Härdle et al. (2020). ↑

2. http://p2pfoundation.ning.com/forum/topics/bitcoin-open-source (Last accessed 09-07-2020.) ↑

3. https://bitcointalk.org/index.php?topic=128.msg1078#msg1078 (Last accessed 09-07-2020.) ↑

4. http://ctrlq.org/first/7631-cryptocurrency/ (Last accessed 09-07-2020.) ↑

5. An online search on Google Trends and Google Ngrams indicated that the term cryptocurrency was not used before the inception of Bitcoin. ↑

6. Note, that the notion of a “blank token” refers here to economic intuition rather than technical implementation. In Bitcoin and its descendents no “coins” exist, but only transaction outputs that are transferable and arbitrarily divisible. ↑

7. Usual consumer goods priced in Bitcoin, for example, are represented by tiny decimal numbers (Yermack, 2015). ↑

8. For a survey of such platforms refer, among others, to Bartoletti et al. (2017) or Alharby et al. (2017). ↑

9. Compare, for example, the discussion around the payment system Libra initiated by Facebook as e.g. in
   https://www.nytimes.com/2020/04/16/technology/facebook-libra-cryptocurre... (Last accessed 09-07-2020.). ↑

10. https://z.cash/technology/ (Last accessed 09-07-2020.) ↑

11. https://web.getmonero.org/resources/research-lab/ (Last accessed 09-07-2020.) ↑

12. https://www.nytimes.com/2020/04/16/technology/facebook-libra-cryptocurre... (Last accessed 09-07-2020.) ↑

13. https://www.cnbc.com/2020/08/20/central-bank-digital-currencies-bitcoin-... (Last accessed 09-07-2020.) ↑

14. https://www.bloomberg.com/news/articles/2020-01-20/facebook-s-failed-lib...
    fresh-swiss-hurdles (Last accessed 09-07-2020.). ↑

15. Compare Pernice et al. (2019) for a survey on stablecoin designs. ↑

16. We would have liked to rely on the unifying element of blockchain-based technology (which supposedly amalgamates all the cryptographic tools of a cryptocurrency) here. However, noting that this term is similarly unclear and vague as the term to define, we abstained from that step. ↑

Primavera De Filippi, French National Centre for Scientific Research
Chris Wray, Legal Graph Company Limited
Giovanni Sileno, University of Amsterdam

Definition

Nick Szabo first described smart contracts in the late 1990s. He envisioned placing contracts into code that could be both trustless and self-enforcing, enhancing efficiency and removing ambiguity from contractual relationships (Szabo, 1996). The idea was to eliminate the need for trust amongst the parties, by increasing the confidence that the contract will be performed exactly as designed (typically making breaches prohibitively expensive). To illustrate his concept, Szabo compared a smart contract to a vending machine. Individuals insert coins into the machine and—assuming the inserted amount is correct—the machine delivers the goods they requested. This predictable interaction requires little to no trust amongst the contracting parties: the vending machine has no choice but to deliver the goods upon receiving the money. The technological infrastructure of the machine is a guarantee that the contract will be fulfilled as intended.

Later, Szabo envisioned that smart contracts could be embedded into all sorts of property that is valuable and controlled by digital technologies to ensure that the associated contractual provisions are automatically executed by technological means (Szabo, 1997). From a historical perspective, the concept of using machines for the application of normative directives can be dated back to Leibniz, with his famous Calculemus! (De Arte Combinatoria, 1666), and returned to more concretely with the advent of legal expert systems in artificial intelligence (AI) and attempts at formalisation of law (e.g. Sergot et al., 1984). Szabo’s proposal can thus be seen as a simplification of the higher-level goal set (with mixed results) by research on normative systems.

Today, the term smart contract has been adopted by the blockchain community to refer to code deployed and run in a blockchain environment (Buterin, 2013). In this sense, smart contracts are software programmes executed in a distributed manner by the miners of a blockchain-based network. Smart contracts take parameters (as an input) via incoming blockchain transactions, process these parameters according to some deterministic algorithm, and generate (as an output) either a state change in the smart contract memory or a new blockchain transaction.

Although they can be programmed in any language that can be compiled into a particular blockchain environment or virtual machine, the most prominent platform today for the deployment of smart contract code is Ethereum. Indeed, the Ethereum blockchain implements a Turing-complete[1] programming language called Solidity, combined with a shared virtual machine (the Ethereum Virtual Machine or EVM), which has become the de facto standard for developing and deploying smart contracts.[2] As a programming language, Solidity is object-oriented, with a strong procedural flavour; its core components are imperative instructions defining positive actions, like e.g. storing the result of a numeric expression in a variable, or logging certain events on the EVM.

Once deployed, the code of a smart contract is stored—in a pre-compiled form—on the Ethereum blockchain and is assigned an address. In order to interact with the smart contract, parties send a transaction to the relevant address, thereby triggering the execution of the underlying code. As such, Ethereum can be regarded as a global and distributed computing layer, which constitutes the backbone for decentralised systems and applications (Buterin, 2013). While Ethereum was the first of its kind, similar functionalities have since been implemented in other blockchain-based platforms, the most popular of which are Cardano, EOS, NEO, Tezos and TRON.[3]

Regardless of the blockchain on which they run, smart contracts fundamentally differ from standard software programmes because they can be executed independently from any centralised operator or trusted third party (De Filippi & Mauro, 2014). Indeed, to the extent that they rely on a decentralised network that is not controlled by any single operator (Chen & al., 2017), smart contracts are guaranteed to run in a predefined and deterministic manner, free from intervention by any particular third party (Voshmgir, 2017). Hence, just like a vending machine, smart contracts can be said to be self-executing, with a guarantee of execution (Buterin, 2013).

Smart contracts generally only implement basic functionalities, such as:

• token issuance for the purpose of fund-raising (as in the case of a token sale or initial coin offering (ICO));
• issuance and management of tokens as digital collectibles (e.g. cryptokitties);
• decentralised marketplaces for the trading of digital tokens (e.g. OpenSea);
• conditional or recurrent payments based on a set of predefined conditions;
• joint savings accounts, allowing parties to withdraw only a particular amount every day;
• escrow systems programmed to execute a transaction whenever specific conditions are met;
• simple lottery systems[4] collecting funds and redistributing them to the selected winner(s);
• gambling systems (such as prediction markets) the operations of which are inherently transparent, permitting users to verify how much money the house has on hand for payouts (e.g., Augur).

Yet, by aggregating multiple smart contracts together, it is possible to create applications with more advanced functionalities. These include decentralised finance applications, such as lending platforms (e.g. MakerDAO) and liquidity pools (e.g. Uniswap, Aave); social media platforms (e.g. Akasha, Karma, Peepeth); or even distributed governance systems for blockchain-based assets, often referred to as Decentralised Autonomous Organisations (e.g., TheDAO, MolochDAO, DxDAO, etc.).

Perhaps one of the greatest potentials of smart contracts lies in the extent to which they can be used to complement or supplement existing legal contracts. They could be used, for instance, to increase the security of identification phases, to facilitate the subscription for shares in a company, the management of an insurance policy, or even the execution of an employment contract (Alhabry & Van Moorsel, 2017). However, most implementations of smart contracts in the legal field are still far from being widely adopted, or even useful. Indeed, for the majority of legal applications (beyond pure financial applications), much of the computation cannot be done by the smart contracts alone, because the smart contract does not have access to information that is not recorded on a blockchain. This is why many smart contracts rely on so-called "oracles": blockchain addresses controlled by some trusted third parties through which the relevant inputs to the contract are provided. Oracles make it possible for smart contracts to react to external data for the implementation of more sophisticated applications—such as a parametric crop insurance service, which receives information from a national weather service and automatically disburses funds based on predefined conditions (Cohn & al., 2017). Relevant extensions enabled by oracles concern ex-post enforcement mechanisms and dispute resolution by means of witnesses, juries and other roles (e.g. Kleros), or more advanced ex-ante enforcement controls by means of external reasoners (see e.g., Idelberg et al., 2016; Liu & al., 2020).

Misconceptions

There are many misconceptions in the discussion around smart contracts. First, smart contracts are often believed to be script-like programmes executed on a blockchain, though from a technical perspective, the operations of smart contracts are ultimately defined by the set of instructions fed (in the form of bytecode) into the virtual machine, which will be executed by the underlying blockchain network. This means that the actual performance of a smart contract does not depend on the subjective expectation of the parties, based on their interpretation of the source code, but merely on the operations dictated by the compiled bytecode deployed to the blockchain (De Filippi & Hassan, 2018).

This leads us to a second key misconception about smart contracts: that they can act as the technical representation of a legal contract. This conception is flawed for at least two fundamental reasons. Firstly, in the nature of their expression: smart contracts are inherently more rigid (and therefore more limited) than legal contracts (De Filippi & Wright 2018). While the clauses of a legal contract (written in natural language) may apply to an indefinite number of situations—because of the inherent flexibility and ambiguity of natural language—the provisions of a smart contract are expressed in a formalised language that does not have nearly the same degree of flexibility as natural language (Levy, 2017). As a result, many contractual clauses (e.g. bona fide obligations) cannot be codified in a blockchain-based infrastructure because they simply cannot be expressed in code (Sklaroff, 2017). Rigidity is also partially due to the closure determined by specific technological choices; for instance, although Solidity considers the use of libraries (i.e., reusable smart contract deployed code), those cannot be updated, and their semantic staticity is reflected in the contracts relying upon them. That being said, such limitations also represent one of the key benefits of a smart contract, as contracting parties may want their contractual performance to rely exclusively on precise and quantifiable outcomes.

Secondly, in the scope of their performance: only a very limited class of contractual obligations can be fully embedded into a smart contract (Mik, 2017). At a computational level, smart contracts enjoy the convergence of imperative instructions with positive duties, but this also means that they do not include explicit directives about prohibitions for instance, nor about institutional power. This would not really be problematic if smart contracts were only concerned about operations under their control. However, most legal contracts refer to rights and obligations outside of the blockchain infrastructure, which cannot therefore be administered via a smart contract. If contractual obligations are triggered by external conditions, a smart contract will depend on a third party-operated programme (i.e., an oracle) to record all the relevant information about such external conditions onto a blockchain (Egberts, 2017). If the contractual obligation itself requires an external intervention, no blockchain-based infrastructure will ever be able to guarantee the proper performance thereof. In particular, legal title to, or beneficial interest in, any property or asset that exists outside of the blockchain infrastructure (i.e. anything other than a blockchain-based asset) cannot be transferred merely by recording a state change into a blockchain, but only in accordance with applicable law. For instance, transferring land ownership cannot be performed automatically by a smart contract because it requires administrative formalities that cannot be completed on a blockchain. In this case, a smart contract could only record the payment, along with the current owner’s intention to transfer ownership to a third party—e.g. via the transfer of an asset-backed token.

Sometimes, the mere act of transacting with a smart contract could give rise to a legal agreement, provided that the minimum legal requirements for contract formation are met in the relevant jurisdiction (Werbach & Cornel, 2017). Conversely, any additional provisions that cannot be fully codified in (and therefore automated by) a blockchain will merely qualify as a promise under an executory contract that may only be enforced through a court order (Herian, 2020). Thus, just as a vending machine can automate the performance of a contract to sell only the physical goods contained within it, so a blockchain-based smart contract can provide automatic performance of a contract relating only to transactions in blockchain-based assets (Hulicki, 2017).

A related problem is the impossibility of technically nullifying the execution of a smart contract in case some underlying conditions make its execution invalid from a legal point of view. Even if such a situation could be identified by means of an external oracle, the chain of transactions stemming from an invalid performance cannot be recovered, unless the possibility has been pre-codified within the smart contract itself.

Several other misconceptions about smart contracts are related to trust. First, it is often said that smart contracts are entirely self-executing (Zhou & al., 2019). Yet, as highlighted above, a smart contract will always rely on a certain amount of trust and/or verification, especially when its execution depends on external information recorded onto a blockchain by a third party (Guadamuz, 2019). If the smart contract depends on a given oracle for its basic functionality, the failure of such an oracle to provide the necessary information will prevent the execution of the smart contract (Muhlberger & al., 2020). More fundamentally, a smart contract’s proper functioning ultimately depends on the network of miners that operate the underlying blockchain network (De Filippi & al., 2020). Were these miners collectively to decide to prevent the execution of a smart contract, they could either censor all transactions addressed towards that particular smart contract’s address (a soft fork) or modify the blockchain protocol in order to change the code of the smart contract or its implementation (a hard fork). While such an intervention is unlikely to happen on a recurrent basis, it is not merely theoretical—as shown by the hard fork of the Ethereum blockchain in the aftermath of TheDAO attack[5] (Reijers & al., 2018).

Conclusion: provision of a sound, working definition suitable for quotation in multiple academic fields

A smart contract is code deployed in a blockchain environment, or the source code from which such code was compiled. It is executed in a distributed manner by the miners of the underlying blockchain network if and when the underlying conditions are met. Execution of a smart contract is triggered via a blockchain transaction and will produce a change in the blockchain state.

References

Alharby, M., & Van Moorsel, A. (2017). Blockchain-based smart contracts: A systematic mapping study. arXiv preprint arXiv:1710.06372

Buterin, V. (2013). Ethereum white paper: a next-generation smart contract and decentralized application platform. https://ethereum.org/en/whitepaper/

Chen, L., Xu, L., Shah, N., Gao, Z., Lu, Y., & Shi, W. (2017, April). Decentralized execution of smart contracts: agent model perspective and its implications. In International Conference on Financial Cryptography and Data Security (pp. 468-477). Springer, Cham

Cohn, A., West, T., & Parker, C. (2017). Smart after all: Blockchain, smart contracts, parametric insurance, and smart energy grids. Georgetown Law Technology Review, 1(2), 273-304.

Corrales, M., Fenwick, M., & Haapio, H. (Eds.). (2019). Legal Tech, Smart Contracts and Blockchain. Springer.

De Filippi, P., & Mauro, R. (2014). Ethereum: the decentralised platform that might displace today’s institutions. Internet Policy Review, 25(08)

De Filippi, P., & Wright, A. (2018). Blockchain and the law: The rule of code. Harvard University Press.

De Filippi, P., & Hassan, S. (2018). Blockchain technology as a regulatory technology: From code is law to law is code. arXiv preprint arXiv:1801.02507.

De Filippi, P., Mannan, M., & Reijers, W. (2020). Blockchain as a confidence machine: The problem of trust & challenges of governance. Technology in Society, 62, 101284.

Egberts, A. (2017). The Oracle Problem-An Analysis of how Blockchain Oracles Undermine the Advantages of Decentralized Ledger Systems. Available at SSRN 3382343.

Guadamuz, A. (2019). All watched over by machines of loving grace: A critical look at smart contracts. Computer Law & Security Review, 35(6), 105338

Herian, R. (2020). Smart contracts: a remedial analysis. Information & Communications Technology Law, 1-18.

Hulicki, M. (2017). The Legal Framework and Challenges of Smart Contract applications. In Conference on System Sciences (pp. 3-4).

Idelberger F., Governatori G., Riveret R., Sartor G. (2016) Evaluation of Logic-Based Smart Contracts for Blockchain Systems. Rule Technologies. Research, Tools, and Applications. RuleML 2016. Lecture Notes in Computer Science, vol 9718. Springer

Lauslahti, K., Mattila, J., & Seppala, T. (2017). Smart contracts–How will blockchain technology affect contractual practices?. Etla Reports, (68).

Levy, K. E. (2017). Book-smart, not street-smart: blockchain-based smart contracts and the social workings of law. Engaging Science, Technology, and Society, 3, 1-15.

Liu, L., Sileno, G., & Engers, T. Van. (2020). Digital Enforceable Contracts (DEC): Making Smart Contracts Smarter. JURIX 2020: The 33rd Annual Conference on Legal Knowledge and Information Systems.

Mik, E. (2017). Smart contracts: terminology, technical limitations and real world complexity. Law, Innovation and Technology, 9(2), 269-30

Mühlberger, R., Bachhofner, S., Ferrer, E. C., Di Ciccio, C., Weber, I., Wöhrer, M., & Zdun, U. (2020, September). Foundational Oracle Patterns: Connecting Blockchain to the Off-chain World. In International Conference on Business Process Management (pp. 35-51). Springer, Cham.

Reijers, W., Wuisman, I., Mannan, M., De Filippi, P., Wray, C., Rae-Looi, V., ... & Orgad, L. (2018). Now the code runs itself: On-chain and off-chain governance of blockchain technologies. Topoi, 1-11.

Savelyev, A. (2017). Contract law 2.0:‘Smart’contracts as the beginning of the end of classic contract law. Information & Communications Technology Law, 26(2), 116-134.

Sklaroff, J. M. (2017). Smart contracts and the cost of inflexibility. U. Pa. L. Rev., 166, 263

Sergot, M. J., Sadri, F., & Kowalski, R. A. (1986). The British Nationality Act as a logic program. Communications of the ACM, 29(5).

Szabo, N. (1996). Smart contracts: building blocks for digital markets. EXTROPY: The Journal of Transhumanist Thought,(16), 18(2).

Szabo, N. (1997). The idea of smart contracts. Nick Szabo’s Papers and Concise Tutorials, 6.

Voshmgir, S. (2017). Disrupting governance with blockchains and smart contracts. Strategic Change, 26(5), 499-509

Werbach, K., & Cornell, N. (2017). Contracts ex machina. Duke LJ, 67, 313.

Zou, M., Cheng, G., & Soria Heredia, M. (2019). In Code We Trust? Trustlessness and Smart Contracts. Trustlessness and Smart Contracts (April 1, 2019). Computers and Law.

1. A programming language is Turing-complete if it is computationally equivalent to a Turing machine. That is, any problem that can be solved on a Turing machine using a finite amount of resources can be solved with that programming language using a finite amount of resources. ↑

2. By contrast, Bitcoin Script is not Turing-complete. ↑

3. Note that, although limited in its capabilities, Bitcoin’s simple script language also allows for the creation of custom smart contracts like multisignature accounts, payment channels, escrows, time locks, atomic cross-chain trading, oracles, or multi-party lottery with no operator. ↑

4. Note that because smart contract code is inherently and necessarily deterministic, randomized action—such as selecting a lottery winner—rely on novel sources of pseudo-randomness which are based on the content of previous blocks. ↑

5. TheDAO was a decentralised investment fund deployed as a smart contract on the Ethereum blockchain in 2016, which raised over $150 million dollars in less than one month. However, a few days before the launch, a vulnerability was found in the code of the smart contract governing TheDAO, which was exploited in order to drain over $60 million dollars from the fund. ↑

Alexandra Giannopoulou, University of Amsterdam
Fennie Wang, Dionysus Labs

Definition of the term

The concept of self-sovereign identity[1] (SSI) emerged from the aspiration of self-determination and of self-governance (Orgad, 2018) for each individual identity. In particular, SSI relies on technological design to create a digital identity independent of third-party actors, which prioritises security, privacy, and individual empowerment.

Origin

Bringing the Westphalian term of sovereignty to the individual level, identity is considered to be foundational for social equality, freedom, democracy, even financial independence (Verhulst & Young, 2018). Free from exclusive state control, self-sovereign authority refers to ‘the actual default design parameter of Human identity, prior to the "registration" process used to inaugurate participation in Society’ (The Moxy Tongue, 2012). ‘The act of “registration” implies that an administration process controlled by Society is required for “identity” to exist. This approach contrives Society as the owner of “identity”, and the Individual as the outcome of socio-economic administration’.[2] The qualification of autonomy as a determining act of self-sovereignty is aligned with transcendentalism. According to Trotter (2014), ‘each of us is owned by the state, which grants leeway (…) to govern and dispose of certain aspects of our bodies and lives’. Nowadays, the race towards digital sovereignty, i.e. the ability to act independently in the global digital environment, relies partially on digital identity management.

The term self-sovereign identity was recaptured by Christopher Allen (2016), who used it to describe a principle-based framework that would create a decentralised system of user-centric, self-administered, interoperable digital identities. This system is driven by ten foundational principles, following Kim Cameron’s Laws of Identity[3]: 1) Existence, 2) Control, 3) Access, 4) Transparency, 5) Persistence, 6) Portability, 7) Interoperability, 8) Consent, 9) Minimalisation, 10) Protection, that would aim to constitute the (missing) “identity layer” on the internet (Preukschat & Reed 2021). It embodies a specific vision of decentralised digital identity, separated from pre-existing centralised and federated models, which aims to decouple identity issuance by the state in order to bring it to the full control of the citizen (The Moxy Tongue, 2016). Ultimately, SSI ‘makes the citizen entirely responsible for the management, exploitation and protection of one’s data’ (Herian 2019). While implementations of these principles vary substantially, it can be said that SSI aims to ‘enable a model of identity management that puts individuals at the center of their identity-related transactions, allowing them to manage a host of identifiers and personal information without relying upon any traditional kind of centralized authority’ (Renieris, 2020). This does not imply that the (private or public) actors responsible for issuing elements of one’s identity will be stripped from their privilege,[4] but rather that an individual in possession of more identifiers can present all claims correlated to those identifiers ‘without having to go through an intermediary’ (Wagner et al., 2018).

Evolution

The use of SSI has been tied to the use of a blockchain. However, SSI is blockchain-adjacent, but not blockchain-dependent. As Cheesman points out, ‘[s]ome bemoan the conflation of “true SSI” with ill-defined concepts such as “user-centric” digital identity, which may not require blockchain technology or use it to its full imagined, decentralised potential.’ (2020).

The technical dimension of SSI has so far been anchored in decentralised identifiers (DID), verifiable claims (VC) and other related standards from the World Wide Web Consortium (W3C), the same internet standards organisation behind the common internet protocols we are familiar with today such as HTML and HTTPS.[5] We shall refer to these standards collectively as decentralised identity standards. They are a set of technical standards for linking and associating data about an identity-subject together in a persistent and universal manner, such that the identity-subject not only has control over how information is linked and used, but is the owner of the profile, rather than a third-party service provider. Thus, the set of linked data, called attestations or claims, may be globally portable. Attestations may include credentials that grant the identity-subject access rights or privileges, or may include verification of information such as a link to identity documents, professional certifications, credit history, or any other data or information. Every attestation that is linked to an identity-subject must be signed digitally by another identity-subject.

SSI systems may be compatible with a blockchain or distributed ledger for documenting and attaching the transactions to each identity-subject’s profile. The blockchain would record transactions that include the adding or signing of attestations, the granting or revocation of access privileges, and so on. The blockchain documentation creates a record of the data-integrity of a set of information linked to an identity-subject. However, a blockchain is not commonly used to store the underlying personal identifying information, as this would create data protection concerns and blockchain ledgers are not cost-effective for storing large amounts of data.

SSI hinges on the technical efficiency of its core concepts. For instance, no two people should have the same identifier, which can be described as the concept of unicity, whereby the identifier cannot reference more than one identity-subject. This condition can be satisfied through the use of cryptography, i.e. mathematically ensuring that only unique identifiers are issued and preventing them from being reissued. In other cases, such as voting or credit checks for cross leverage, no one person should have more than one identifier, which can be described as the concept of singularity whereby the relationship between the identity-subject and identifier is one-to-one only. This condition may be the most challenging in a pseudonymous and decentralised identity system. In a world which requires singularity of identification, technical tools and/or legal requirements that are exogenous to an SSI system appear to be a solution. The singularity quality of an identifier and identification system has traditionally been solved through centralised databases, wherein all sources of information can be aggregated to one authority that can cross check whether one identity-subject has multiple identities and identifiers (Wang & De Filippi, 2020).

Coexisting uses/meanings

As described above, SSI is oftentimes used interchangeably with terms such as decentralised identity and digital identity (Renieris, 2020). While the first two terms refer to a rather similar identity management system, one that applies technological architectures such as the ones mentioned above guided by political and ideological agendas, digital identity represents a broader techno-legal societal shift towards incorporating physical identity values in a digital form. It is supported by a network of legal reforms, and facilitated by technological developments (Sullivan & Berger, 2017).

The management of (physical and digital) identity is subject to national regulation, as an expression of digital state sovereignty (Madiega, 2020). On a European level, several initiatives have been launched with a focus on digital identity services. In its recent communication entitled ‘Shaping Europe’s digital future’, the European Commission mentions that ‘a universally accepted public electronic identity (eID) is necessary for consumers to have access to their data and securely use the products and services they want without having to use unrelated platforms to do so and unnecessarily sharing personal data with them. Europeans can also benefit from use of data to improve public as well as private decision-making’.[6] Similarly, the ‘Digital Finance Strategy for the EU’[7] specifies that ‘by 2024, the EU should implement a sound legal framework enabling the use of interoperable digital identity solutions’, which would bring technological standardisation, interoperability, and broader security in customer/user identification and authentication by financial institutions.

According to the Commission, the promotion and regulation of digital identity is essential in maintaining an ‘open, democratic, and sustainable society’, which is one of the main objectives of this data strategy. For this, trusted and secure interactions are essential. The objective would be to ensure appropriate, and most importantly, interoperable, identification and authentication frameworks. Current digital identity reforms are tied to SSI efforts for the creation of user-centric data sovereignty (Herian, 2020). However, and as pointed out by Sheldrake, ‘although SSI has been scoped, architected and built as technology, it is not merely technology. By definition, it’s sociotechnology (involving the application of insights from the social sciences to design policies and programs)’ (2020).

Issues currently associated with the term

While there have been considerable reforms that have facilitated the proliferation of identity solutions,[8] numerous legal compliance shortcomings remain with regard to the implementation of decentralised (self-sovereign) identity, and to its adoption.

Specifically, the eIDAS Regulation defines different levels of trust services and provides the regulatory environment that enables the creation of numerous interoperable digital identity solutions (Alamillo, 2020; Schroers, 2018). According to the Regulation, electronic identification is ‘a material and/or immaterial unit containing person identification data and which is used for authentication for an online service’. Any form of cross-border digital identity (self-sovereign or not) would have to function within a mutually recognised identity framework between EU member states for authentication and access to electronic services.

In addition, identity providers have to conform to data protection regulation such as the GDPR (Renieris, 2020; Giannopoulou, 2020). Compliance appears to be rather challenging, due to constraints related to the governance, architecture, and the technological design of the identity project. For instance, actor liability of decentralised architectures remains uncertain (Finck, 2019). Similarly, the exercise of data subjects’ rights within a self-sovereign identity architecture has yet to be tested, especially with the emergence of new types of trust actors.

Many applicable legal norms are sector-specific. In financial regulation, the Payment Services Directive 2 aims to facilitate financial data sharing in order to expand the technological abilities of the existing financial infrastructures (Westermeier, 2020) and to ‘promote innovative mobile and internet payment services’. Identity and the use of strong authentication technological standards are both key in applying and implementing the aspirations of the European legislator within the financial sector. This is also apparent when reviewing the anti-money laundering (AML) and know your customer (KYC) obligations, revised by the AML5 Directive, which require a digital identity that facilitates transparency and accountability of financial intermediaries. Evidently, the application of these obligations in the broader cryptocurrency network of actors remains unclear. However, the proposed MiCA Regulation[9] does address issues of identity, in relation to the cryptocurrency market.

Public discourse highlights SSI’s foundational goal of placing the identity subject in control of their identity data[10] (user-centric identity), and views SSI solutions as a much needed global infrastructure that would provide documentation to large populations that have none, better integrating them in modern digital society (World Bank Group, 2018; World Economic Forum, 2018). However, there are considerable risks related to the expansion of global SSI systems for purposes such as refugee identification. As pointed out by Cheesman (2020), ‘the emancipatory potential of decentralised, user-owned modes of identification came into tension with the geopolitical reality of the nation-state system in which states’ prerogative is to control the legitimate means of movement – or, indeed, identification’. The persistent integration of an identity layer cannot account for anonymity nor for the contextual, interpersonal nature of most expressions of our identity (Hopman & M’Charek, 2020). Following a tradition of identification technologies, ‘intensified regimes of surveillance, securitisation and control’ (Lyon, 2008; Cheesman, 2020) would tend to emerge, further solidifying existing inequalities (Gstrein & Kochenov, 2020). As pointed out by Sheldrake, ‘viewed atomistically, technologically, SSI looks quite sensible. At scale, as sociotechnology, the emergent consequences are malignant’ (2020).

While often lauded, the commodification of identity (Birch, 2014) could result in states competing in an open market for (sovereign) citizens. Finally, as reputation (Mac Sıthigh & Siems, 2019) is becoming essential in producing trust within modern platform-mediated digital services (Bodó, 2020), decentralised identity is regarded as an equalising force between power asymmetries. Lately, new intermediaries have started to emerge in the field of decentralized reputation systems, and with them, comes the potential for a new societal order of surveillance (Foucault, 2004), defined by the consequences of assigning persistent identities to control financial, criminal, and human flows.

Conclusion

Self-sovereign identity (SSI) is rooted in the belief that individuals have the right to an identity independent of reliance on a third-party identity provider, such as the state or any other central authority. Its implementation requires the development of technical standards, as well as socio-political adaptations rooted in legal amendments in order to be successful. Overall, SSI is implemented as blockchain-adjacent, but not blockchain-dependent identity management systems, which are guided by the fundamental principle of user-centric design, using technical standards that enable user-generated and user-controlled decentralised identifiers, associated credentials, and attestations. This is supplemented by legal and policy requirements to ensure that the objectives for particular use cases are achieved, including balancing competing societal goals between user privacy, security, law enforcement, financial inclusion and risk management.

References

Allen C (2016), The path to self-sovereign identity, Life with Alacrity, 25 April. Available online at: https://www.lifewithalacrity.com/2016/04/the-path-to-self-soverereign-identity.html#dfref-1212

Alamillo Domingo I (2020), SSI eIDAS Legal Report. How eIDAS can legally support digital identity and trustworthy DLT-based transactions in the Digital Single Market?. Available online at: https://joinup.ec.europa.eu/collection/ssi-eidas-bridge/document/ssi-eidas-legal-report

Birch D (2014), Identity is the new money, London Publishing Partnership

Bodó, B. (2020). Mediated trust: A theoretical framework to address the trustworthiness of technological trust mediators. New Media & Society. DOI: 10.1177/1461444820939922

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Finck M (2019), Blockchain Regulation and Governance in Europe, Cambridge University Press

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Giannopoulou A (2020), Data Protection Compliance Challenges for Self-Sovereign Identity. IN: J. Prieto et al. (Eds.): BLOCKCHAIN 2020, AISC 1238, pp. 1–10.

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1. We will use the term sovereign identity and SSI interchangeably. ↑

2. See transcendentalism, and libertarian individualistic approaches towards societal constructs. ↑

3. https://www.identityblog.com/stories/2005/05/13/TheLawsOfIdentity.pdf ↑

4. In that regard, it distances itself from the concept of sovereignty (Manski & Manski 2018). ↑

5. https://www.w3.org/ ↑

6. Communication from the Commission to the European Parliament, the Council, the European economic and social committee and the committee of the regions, ‘Shaping Europe's digital future’, COM(2020) 67 final, 19.02.2020. ↑

7. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, Digital Finance Strategy for the EU, Brussels, 24.9.2020, COM(2020) 59 ↑

8. See for example eIDAS Regulation and the new Payment Services Payment 2 Directive (PSD2). ↑

9. Proposal for a Regulation of the European Parliament and of the Council on markets in crypto-assets, and amending Directive (EU) 2019/1937, COM/2020/593 final ↑

10. This objective is perfectly aligned with the ideals of decentralisation that drove the development of blockchain technology in general (Bodó & Giannopoulou, 2020). ↑

Wassim Zuhair Alsindi, Massachusetts Institute of Technology
Laura Lotti

Origin

Cryptocurrency mining was initially understood to refer to processes incorporating proof-of-work (PoW) (i.e., the spending of costly computational resources such as CPU cycles via a mechanism originally developed to mitigate spam) (Dwork and Naor, 1992; Back, 2002). PoW is usually a permissionless process (i.e., anyone can partake) with miners’ identities unknown (anonymous/pseudonymous). Precursor digital money projects such as Bit Gold and b-money (Szabo, 2005; Dai, 1998) proposed the use of PoW-type mechanisms to avoid resource exhaustion and message flooding attacks or Sybil attacks from large numbers of dishonest sockpuppet nodes (Douceur, 2002).

While the Bitcoin Whitepaper (Nakamoto, 2008) did not refer to PoW explicitly as mining, reference was made to the gold mining analogy. The term was used colloquially in online forums and chatrooms including BitcoinTalk and IRC (Internet Relay Chat, a long-running instant messaging protocol) as far back as 2010. Indeed, the source code of the first version of the Bitcoin software referred to the process of generating coins as mining (Nakamoto, 2009).

The chain selection heuristic which uses PoW to ensure the eventual network-wide consistency of the Bitcoin ledger is referred to as Nakamoto Consensus. This requires a 51% majority of “work” to reach agreement on the latest valid block and a "guarantee that all honest parties output the same sequence of blocks throughout the execution of the protocol" (Kiffer et al., 2018). Blockchains grow in height incrementally as new candidate blocks are constructed by miners and added to the canonical chain. In PoW-based networks this takes place through the combination of nonces (i.e., an arbitrary variable which is progressively iterated) with the proposed block header to generate hashes which are then compared against the network-determined difficulty of finding a block. The miner chooses the identity and order of transactions contained within a proposed candidate block and this has potential economic implications including front-running and re-ordering of transactions (Daian et al., 2019).

The mining process is mediated by a difficulty adjustment feedback mechanism, which periodically recalibrates the effective probability of finding a valid block so as to maintain the network’s target inter-block times. Should the hash of a candidate block be found that satisfies the network’s difficulty requirements, the miner will announce it to the network and fellow network participants will confirm validity of the block. Within the block, the miner may claim a so-called mining subsidy or block reward by including a transaction payable to themselves, in addition to any mining fees paid by transactions included.

The key cryptographic component of Bitcoin mining is the hash puzzle. Hashing refers to a one-way deterministic process that converts an input of arbitrary length to one of fixed length. An ideal cryptocurrency hashing puzzle algorithm must have the following properties (Narayanan and Clark, 2017): (i) it is difficult to compute so that shortcuts or undue advantages are not available to some participants; (ii) cost is parameterisable so that the energetic expenditure required to mine a valid block is not fixed over time; and (iii) it is trivially easy to verify the correctness of the hashed output from the input material. Since cryptographic hash functions are deterministic (i.e., given a fixed block with a fixed nonce - and a broad subset of possible hash values satisfying the difficulty requirements exist), it is entirely plausible that more than one valid candidate block may be found by competing miners at very similar times. In such an eventuality there begins a block propagation competition per se which allows the network to reach agreement on the latest state of the transaction ledger.

Since there can only be one block with a particular height in a blockchain, should multiple candidates emerge the prospect of a persistent network partition known as a fork arises if subsets of the population of validating nodes do not overwhelmingly agree on the latest block. Such partitions may be short-lived in the case of stale blocks such as “orphans” and “uncles” (terms used with respect to Bitcoin and Ethereum mining respectively) which represent discarded timelines as the canonical chain built upon another candidate block. In other cases, a fork can happen due to a malicious attack, such as a “51% attack” - when a nefarious actor manages to take control of the majority of hashing power and is able to modify the order of transactions or reverse the transactions that they themselves made, leading to double-spending (i.e., spending the same digital coins twice).

Combining these various elements, we can take the original meaning of cryptocurrency mining to be a thermoeconomic[1] process employing PoW and a parameterisable feedback mechanism (difficulty adjustment) with direct incentives provided by block rewards from an algorithmically regulated network-level issuance schedule alongside transaction fees.

Evolution

Since Bitcoin’s PoW, the range of activities falling under the nominal banner of mining has broadened substantially over time.

A number of alternative PoW strategies have emerged in recent years, at first hypothetical and subsequently observed in the wild, which afford favourable game-theoretic outcomes by deviating from honest mining behaviour as originally intended by the Bitcoin protocol (Eyal and Sirer, 2018, Grunspan and Pérez-Marco, 2018). Selfish mining, also known as block withholding, may be conducted by a miner who finds a valid block but instead of immediately broadcasting to peers, the block is withheld and kept secret. The miner then begins to search for a valid block atop the previous clandestine block, with the aim of finding a valid second block (and then announcing the first secret block) before another participant finds an alternative valid first block. It has been claimed that this adversarial strategy is more beneficial than honest mining for a sufficiently well-resourced miner.

As the blockchain space has matured, the processes at the core of decentralised consensus have become unbundled and abstracted from the materiality of computational work, while at the same time capital and other exogenous resources have become more integrated. One popular approach to this virtualisation of work is staking, which involves locking (i.e., rendering illiquid) some form of collateral in a protocol and being rewarded for participating in network consensus proportionally to the amount staked. Since it extends and further virtualises the novelty of Bitcoin’s consensus model, staking via proof-of-stake (PoS) has also been called “generalised mining” or “mining 2.0” (Brukhman, 2018). In fact, staking was initially proposed as a less computationally-intensive alternative to PoW to prevent double-spending in base layer chains such as Ethereum (King and Nadal, 2012), but the model has found broad application in ‘layer-2’ cryptoeconomic protocols (Brekke and Alsindi, forthcoming), made possible by smart contracts. An area in which staking has found significant application in layer-2 protocols is Decentralised Finance (DeFi), in which liquidity mining is currently (at the time of writing) a popular term used to describe the incentivised provision of collateral and liquidity for the most disparate financial activities: lending, borrowing, insurance, synthetic derivatives, and governance over the risk parameters of a decentralised bank.

Issues currently associated with the term

Critiques of the mining metaphor

The analogy between PoW-secured digital currency and gold has been widely discussed. In general it echoes the desirable commodity money characteristics prized by adherents to modern libertarian ideals or the Austrian School of Economics (Alsindi, 2019), among which is Szabo’s concept of unforgeable costliness (Szabo, 2008) relating to the inelasticity of supply of Bitcoin (and most subsequent PoW cryptocurrencies). The strict resource scarcity that arises from Bitcoin’s algorithmically regulated issuance schedule and the analogy with gold mining have become expressions of the digital metallism that characterises Bitcoin’s discourse (Maurer et al., 2013).

Swartz (2018) further differentiates between digital metallism and infrastructural mutualism, that is, two techno-economic imaginaries stemming from the cryptoanarcho-libertarian and cypherpunk subcultures, respectively. Here mining, and the diverse meanings that emerged around this misnomer, illustrate the tensions between these two positions, which ultimately led to an ideological fork of the Bitcoin network in mid-2017: “Digital metallists understood the act of mining as an opportunity to extract the greatest amount of Bitcoins to be used as a store of speculative value, whereas infrastructural mutualists saw mining as an act of collaboration to produce a shared privacy-protecting payment network” (Swartz, 2018).

These divergent ideologies profoundly influenced the development of the blockchain ecosystem beyond Bitcoin. Here we could argue that Satoshi Nakamoto and Hal Finney were much more in line with the infrastructural mutualism vision; early message logs exist where the two earliest known Bitcoin network participants were hopeful that solely altruistic behaviour could be encouraged as a community ethos (Cryptography Mailing List, 2008). However, at the core of the process of mining is neither the minting of new coins, nor the access to decentralised economic flows per se, but the assurance of settlement through decentralised consensus (Antonopolous, 2018; Carter, 2019). In Bitcoin and other PoW chains, this assurance comes from the distribution of the computational power used to search for blocks, whereas in staking protocols it is a matter of economic distribution so that, in principle, no single actor is able to accumulate more than 51% of the proving resource (i.e., hashrate for PoW and token supply for PoS).

Ecological and thermodynamic critiques

As the term mining is now used to describe cryptoeconomic processes as well as thermoeconomic ones, the previously strained analogy now appears to be a pure simulacrum (Baudrillard, 1981). PoW mining is by necessity an energetically costly process, consisting of irreversible computation (Landuaer, 1961). At the time of writing, Bitcoin electricity consumption is estimated to be over 64 TWh, approximately equivalent to that of the Czech Republic or Austria (CBECI, 2020). Proofs-of-useful-work such as those used in cryptocurrencies such as Primecoin (King, 2013) have been proposed as more eco-friendly alternatives to Bitcoin-type PoW. In reality, useful work may not reduce the overall thermodynamic footprint of a cryptocurrency, as the effective worth of the useful work may simply be treated as a universal discount by all mining participants (Sztorc, 2015).

It has been proposed that Bitcoin liberates stranded, illiquid energy and the majority of PoW mining employs renewable energy from geothermal and hydroelectric sources far from population centres (CoinShares, 2019). However, the insensitivity of PoW cryptocurrencies to the energy sources used to secure them has led to criticism as to their inability to mitigate their ecological externalities.

Conclusion

In the context of blockchain networks, mining describes a permissionless process intended to ensure the global consistency of a decentralised ledger. Mining requires the consumption of a costly computational resource to participate in a probabilistic competition that confers specific privileges to a node. These privileges typically relate to the proposal of a new block, including the identity and order of transactions contained within. It is incentivised via an algorithmically regulated provision of rewards, usually in the form of newly generated coins and/or transaction fees.

Acknowledgements
The authors would like to thank Yuval Kogman, Anil Bawa-Cavia and Sam Hart for helpful feedback during the preparation of this article.

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Landauer, D. (1961). Irreversibility and Heat Generation in the Computing Process, IBM Journal: 183-191. https://doi.org/10.1147/rd.53.0183

Maurer, B., Nelms, T. C., and Swartz, L. (2013). “When Perhaps the Real Problem is Money Itself!”: The Practical Materiality of Bitcoin. Social Semiotics. http://dx.doi.org/10.1080/10350330.2013.777594

Nakamoto, S. (2008). Bitcoin: a peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf

Nakamoto, S. (2009). Bitcoin v0.1 source code repository. https://github.com/bitcoin/bitcoin/tree/4405b78d6059e536c36974088a8ed4d9f0f29898

Narayanan, A., and Clark, J. (2017). Bitcoin's academic pedigree. Communications of the ACM, 60(12): 36-45. http://dx.doi.org/10.1145/3132259

Swartz, L. (2018). What was Bitcoin, what will it be? The techno-economic imaginaries of a new money technology. Cultural Studies, 32(4): 623-650. https://doi.org/10.1080/09502386.2017.1416420

Szabo, N. (2005). Bit Gold. https://unenumerated.blogspot.com/2005/12/bit-gold.html

Sztorc, P. (2015). Nothing is Cheaper than Proof of Work. https://www.truthcoin.info/blog/pow-cheapest/

1. A portmanteau of thermodynamic and economic, not associated with the heterodox field of thermoeconomics. ↑

María-Cruz Valiente, Universidad Complutense de Madrid
Florian Tschorsch, Technical University of Berlin

1. Definition of the term

In recent years, blockchain(-based) technologies have attracted the interest of a wide variety of actors and stimulated a large amount of academic research. The topic is increasingly part of academic and public debates. Unfortunately, there is neither a formal definition nor a common understanding of what blockchain-based technologies means, that is, what properties and technical features the term implies. The main question that needs to be answered is: what fundamental requirements have to be met in order for a software to be classified as blockchain technology? Therefore, a good understanding of the term blockchain is needed to design, engineer, implement, and manage such technologies effectively.

In the literature, we can find several approaches to define the notion of blockchain. For example, García-Barriocanal et al. (2017) define blockchain as “the core data structure of a category of decentralized database architectures that rely on cryptographic techniques and distributed consensus to provide tamper-proof distributed ledgers”. Another definition is found in the writings by Governatori and colleagues, where blockchain is defined as “a network of geographically distributed computing nodes, sharing a common append-only data structure to record blocks of transactions, where revisions or tampering are made prohibitively difficult due to the modus operandi of the infrastructure” ( Governatori et al., 2018) According to Staples et al. (2017), a blockchain is “a computational platform” that executes smart contracts (i.e., a small piece of code deployed on the blockchain) as transactions; and a distributed database replicated across multiple nodes, which records the transactions between different parties. On the other hand, Janssen et al. (2020) point out that blockchain technologies must provide trust, anonymity, security and data integrity without having to use any third party controlling organisations. Iansiti and Lakhani (2017) remark that the definition of blockchain involves three basic concepts: transaction, block, and chain, where chain refers to the blocks connected in chronological order. For requirements elicitation, the work of Janssen et al. (2020) presents a conceptual framework for the adoption of blockchain technology, capturing the complex relationships between governance, business information (namely business processes), and technical issues.

In summary, we observe that the meaning of the word blockchain is and remains controversial. It has no standard technical definition. Rather it is used as a loose umbrella term to refer to systems that bear resemblance to the Bitcoin protocol, or more generally the Nakamoto Consensus (Narayanan and Clark, 2017). At the same time, we observe an effort to use Distributed Ledger Technology (DLT) as a reaction to this ambiguity classifying blockchains are a subset of DLTs. Hence, DLT becomes the technical accurate term, referring to consensus of replicated data in a peer-to-peer network.

2. Issues currently associated with the term

Blockchain technology originally emerged to support new forms of digital money. It was first proposed in the birth of Bitcoin by Satoshi Nakamoto in 2008 and presented at a time where the trust in banks and other financial institutions was at a low due to the world-wide financial crisis. In short, Bitcoin can be defined as the first and (at the time of writing) most popular cryptocurrency. It consists of a digital currency (i.e., bitcoin) and online payments (i.e., the Bitcoin network), which operates independently of a central bank (Swan, 2015). In this way, payments performed through Bitcoin avoid the services of a middleman, such as commercial banks, lawyers, and notaries, which destabilises adopted state monopolies on the production and verification of money and transactions (Karlstrøm, 2014). Since the blockchain records every single change made in the network (first and foremost to reject double spends), Bitcoin probably became the most transparent financial system. In the following, we look beyond Bitcoin to convey the technological diversity with respect to blockchains. By doing this, we intend to emphasise the difficulties to capture this technology in a single definition.

By the end of 2013, Vitalik Buterin created Ethereum, a general-purpose blockchain-based distributed computing technology (Buterin, 2014). Using Ethereum, developers can create web applications known as Decentralized Applications (DApps) without knowledge about the underlying mechanisms, such as peer-to-peer networks and blockchain in general.

Another complementary blockchain-based technology, Filecoin was created in 2017 as a decentralised storage network built on a decentralised market (Protocol Labs, 2017). Filecoin runs on a blockchain with a native token (i.e., filecoin), which miners earn (i.e., they compete to mine blocks with sizable rewards) by providing storage to customers. Clients on the other hand use filecoins to pay for storing and distributing data in the network.

Apart from these most known blockchain-based technologies, we can find many research entries focused on presenting new forms of blockchain-based technologies. For example, Singh and Kim (2018) propose a blockchain technology aimed at building a secure and a trusted environment for intelligent vehicle communication. In another line of research, Wang and Su (2020) review the application of blockchain technology in the energy field. They highlight the advantages of applying blockchain which can be summarised in the following aspects: (i) distributed/decentralised energy supply systems; (ii) stakeholders can directly connect each other to conduct transactions at all levels through the blockchain network; (iii) energy and storage flows are controlled automatically through the implementation of smart contracts; and (iv) all energy flows and business activities are securely recorded.

A prime example to highlight the ambiguity of the term blockchain is the tension between so-called permissionless and permissioned blockchains. Permissionless blockchains, such as Bitcoin, do not require a permission to contribute to the consensus. The permission to generate a new block is organised in a completely decentralised manner. In contrast, permissioned blockchains, such as Hyperledger, define a closed group of nodes, who can contribute to the consensus. This group is often determined by a central entity. In the literature, both are referred to as blockchains. While permissionless blockchains are clearly in line with the Nakamoto consensus, permissioned blockchains exhibit more resemblance to the area of Byzantine fault tolerance (Lamport, Shostak, and Pease, 2019). Such ambiguities and many more misconceptions motivated articles that explore suitable application domains of blockchains by trying to give an answer to the question “do you need a blockchain?” (Wüst and Gervais, 2018). The dissonance clearly reveals the issues with the definition of the term blockchain.

3. Conclusion

Blockchains are supposed to offer diverse technological possibilities. With a range of use cases that go far beyond virtual currencies applications, they are proposed as a technological means to achieve trust, security, and privacy. After more than a decade of research and experimentation, however, the utility of blockchains seems to be circumscribed to few use cases, with cryptocurrencies still representing their most relevant application.

The value proposition of blockchain seems to be that of offering a global, open and censorship-resistant network for peer-to-peer transactions. Its key innovation is the deployment of consensus algorithms that offer reasonable security in open peer-to-peer networks. The main characteristics attributed to blockchain-based technologies include: (i) decentralised consensus, i.e., no central entity or third party is responsible for decision-making; (ii) immutable archive, i.e., an ordered list of transactions that cannot be removed or altered; (iii) transparency and verifiability, i.e., all recorded entries can be accessed and verified locally; (iv) resilience to failure, i.e., the system can handle Byzantine failure up to a certain threshold.

The term blockchain remains vague, even controversial. Often, the term is used merely to point at the ideologies that have been attached to it, with imprecise references to technological specifications. This makes it difficult to classify a given application as blockchain-based technology. While not clearly defined, blockchains typically exhibit a resemblance to Bitcoin, which is commonly considered its archetypal example, repeating its technical characteristics or following similar goals. From a purely technical point of view, blockchains are a type of DLT. Therefore, they can be understood as a distributed network of computers, ideally organised in a decentralised way, mutually agreeing on a common state while tolerating failures (incl. malicious behaviour) to some extent.

References

Buterin, Vitalik, 2014. Ethereum: A Next-Generation Cryptocurrency And Decentralized Application Platform. Bitcoin Mag.

García-Barriocanal, E., Sánchez-Alonso, S., Sicilia, M.-A., 2017. Deploying Metadata on Blockchain Technologies, in: Garoufallou, E., Virkus, S., Siatri, R., Koutsomiha, D. (Eds.), Metadata and Semantic Research, Communications in Computer and Information Science. Springer International Publishing, Cham, pp. 38–49. https://doi.org/10.1007/978-3-319-70863-8_4

Governatori, G., Idelberger, F., Milosevic, Z., Riveret, R., Sartor, G., Xu, X., 2018. On legal contracts, imperative and declarative smart contracts, and blockchain systems. Artif. Intell. Law 26, 377–409. https://doi.org/10.1007/s10506-018-9223-3

Iansiti, M., Lakhani, K.R., 2017. The Truth About Blockchain. Harv. Bus. Rev. 95, 118–127.

Janssen, M., Weerakkody, V., Ismagilova, E., Sivarajah, U., Irani, Z., 2020. A framework for analysing blockchain technology adoption: Integrating institutional, market and technical factors. Int. J. Inf. Manag. 50, 302–309. https://doi.org/10.1016/j.ijinfomgt.2019.08.012

Karlstrøm, H., 2014. Do libertarians dream of electric coins? The material embeddedness of Bitcoin. Distinktion J. Soc. Theory 15, 23–36. https://doi.org/10.1080/1600910X.2013.870083

Lamport, L., Shostak, R., & Pease, M. 2019. The Byzantine generals problem. In Concurrency: the Works of Leslie Lamport (pp. 203-226).

Nakamoto, S. 2019. Bitcoin: A peer-to-peer electronic cash system.

Narayanan and Clark. “Bitcoin’s academic pedigree.” Communications of the ACM, 60(12), 2017.

Protocol Labs, R., 2017. Filecoin: A Decentralized Storage Network [WWW Document]. Protoc. Labs Res. URL https://research.protocol.ai/publications/filecoin-a-decentralized-storage-network/ (accessed 7.16.20).

Singh, M., Kim, S., 2018. Branch based blockchain technology in intelligent vehicle. Comput. Netw. 145, 219–231. https://doi.org/10.1016/j.comnet.2018.08.016

Staples, M., Chen, S., Falamaki, S., Ponomarev, A., Rimba, P., Tran, A. B., Weber, I., Xu, X., Zhu, J., 2017. Risks and opportunities for systems using blockchain and smart contracts. Data61 (CSIRO), Sydney. https://doi.org/10.4225/08/596E5AB7917BC

Swan, M., 2015. Blockchain: blueprint for a new economy, First edition. ed. O’Reilly, Beijing Cambridge Farnham Köln Sebastopol Tokyo.

Wang, Q., Su, M., 2020. Integrating blockchain technology into the energy sector — from theory of blockchain to research and application of energy blockchain. Comput. Sci. Rev. 37, 100275. https://doi.org/10.1016/j.cosrev.2020.100275

Wüst, K., Gervais, A., 2018. “Do you need a blockchain?”. In Crypto Valley Conference on Blockchain Technology (CVCBT 2018).

Jaya Klara Brekke, Durham University
Wassim Zuhair Alsindi, Massachusetts Institute of Technology

1. Definition

Cryptoeconomics describes an interdisciplinary, emergent and experimental field that draws on ideas and concepts from economics, game theory and related disciplines in the design of peer-to-peer, cryptographic systems.

Origin

The term cryptoeconomics entered casual usage in the formative years of the Ethereum developer community in 2014-5. The coining of the phrase is typically attributed to Vitalik Buterin with the earliest public usage being in a 2015 talk by Vlad Zamfir entitled “What is Cryptoeconomics” (Zamfir, 2015). For Buterin, the aim of cryptoeconomics is “as a methodology for building systems that try to guarantee certain kinds of information security properties" (Buterin, 2017, pp. 46-56). While for Zamfir, the focus is more broadly on the distribution of efforts, goods and services in new digital economies: "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" (Zamfir, 2015). The term is uncommon amongst Bitcoin developers, but is used to discuss adversarial scenarios such as state-sponsored defensive mining and transaction censorship (Voskuill, 2018).

Cryptoeconomics was coined by the Ethereum community but was initially inspired by the use of economic incentives in the Bitcoin protocol (Nakamoto, 2008). Bitcoin mining is designed with the intention that it would be more profitable and attractive to contribute to the network than to attack it. With the development of Ethereum as the first general-purpose blockchain protocol, the idea of using economic incentives was also generalised as an approach to achieve a broad variety of behavioural and information security outcomes for decentralised systems. This has led to experimentation with the use of cryptographic techniques and incentives in organisational, financial, market and monetary experiments (Davidson et al., 2016; Halaburda et al., 2018; Voshmgir, 2019).

The motivation for the development of cryptoeconomics came about in order to solve specific information security, organisational and economic problems that manifest in decentralised systems. This affinity to decentralisation as an axiomatic aim and primary concept originates from a longer history of the development of peer-to-peer systems as a means to establish autonomous networks (Brekke, 2020). With the invention of Bitcoin, economic ideas were added to the toolbox of computer engineers developing peer-to-peer systems. For some, the motivation was to enable economic autonomy and fair distribution of efforts and rewards within such decentralised networks, what scholar of money and the internet Swartz calls infrastructural mutualism, while for others the promise of provably scarce and unforgeable virtual commodities - digital metallism - was the main attraction (Swartz, 2018), the latter often drawing on economic and monetary ideas of the US far right (Golumbia, 2016).

Evolution

Over time there has been a broadening in the scope of what can be considered cryptoeconomics as the variety of consensus systems and token types has proliferated. The different approaches to cryptoeconomics are beginning to settle into distinct layers of a cryptoeconomic 'stack': 'layer 1' referring to the information security of a network protocol such as proof-of-work and proof-of-stake; and 'layer 2' referring to the token, market or mechanism capacities offered by emerging cryptoeconomic platforms (Alsindi, 2019).

In recent years a number of networks affording general-purpose computation with facile smart contracting and token creation capabilities have emerged. This layer 2 cryptoeconomics entails the creation of notionally valuable economic assets without being connected to the underlying security properties of the network substrate - for example ERC20-type Ethereum tokens, Non-Fungible Tokens (NFTs) and more recently Decentralised Finance (DeFi) synthetic tokens. Whilst having notional economic value, these assets provide negligible security benefits to the base layer of the network: the abstracted non-native assets of 'layer 2' may increase the incentive to attack 'layer 1', as has been discussed in relation to ledger forks (Alsindi, 2019), Initial Coin Offering launches and sudden market-moving events are seen regularly in the hyperfinancialised DeFi sector (Daian et al., 2019).

The scope and definition of cryptoeconomics is still undergoing epistemic formation (0xSalon, 2020) and thus entails specific areas of focus:

Information security engineering

Where the primary focus of the cryptoeconomic endeavour is on the security properties for peer-to-peer 'layer 1' protocols.

Mechanism design

Where the focus is specifically on the use of incentives for behavioural engineering of rational agents in a game theoretical setting (Brown-Cohen et al., 2018).

Token engineering

Where the primary focus is on the functionality and properties exhibited by tokens used in a system. Tokens might for example grant token holders specific rights (such as service access or voting privileges as commonly encountered with the ERC-20 pseudo-standard), be fungible or non-fungible such as NFTs, be generated and distributed through mining, or through airdrops. Different token designs are understood to encourage different types of behaviours and organisational properties (Voshmgir, 2019).

Market design

Where the focus is on employing blockchain protocols and tokens in order to experiment with new kinds of markets that generate specific types of outcomes. For example, bonding curves determine the price of tokens depending on the supply or other factors, with an aim to influence the behaviour of investors (Titcomb, 2019).

2. Issues currently associated with the term

Cryptoeconomics is generally understood to combine cryptographic techniques and economics. However, much of the field of cryptoeconomics “shows an interesting but also alarming characteristic: its underlying economics is remarkably conventional and conservative” (Virtanen et al., 2018). Out of the long-standing and broad fields of economics and associated fields of political economy, monetary theory, finance and social study of finance, most literature on cryptoeconomics takes an overly formalist approach to the contested field of game theory (Green and Viljoen, 2020). Virtanen et al. quote a revealing tweet from the influential Nick Szabo: “An economist or programmer who hasn’t studied much computer science, including cryptography, but guesses about it, cannot design or build a long-term successful cryptocurrency. A computer scientist and programmer who hasn’t studied much economics, but applies common sense, can.” This, adds Lotti, means that the disruptive potential of cryptoeconomic approaches, “in spite of their noble intentions, these projects do not in fact break with the current financial paradigm” (Lotti, 2016, p. 105).

Alternative approaches to cryptoeconomics take a broader societal outlook, for example focusing on the economics of new organisational forms (Davidson et al., 2016), the design of economic space (Virtanen et al., 2018), or on economic and monetary design that draws on mutual credit systems (Brock et al., 2018) and commons approaches (de Filippi and Hassan, 2015; Catlow, 2019). There is, in other words, much broader economic experimentation taking place with and through peer-to-peer cryptographic systems, however, those explicitly labelled cryptoeconomic often imply narrow and formalist approaches limited to Austrian school economics, right wing monetary ideas and game theory, especially apparent in the usage of the term in reference to Bitcoin (Golumbia, 2016; Voskuill, 2018).

One of the ongoing challenges encountered in cryptoeconomics is inherent to mechanism design and market design economics more generally (Ossandón, 2019). Namely the contradiction between the promise of deterministic outcomes in theory and the complex, emergent behaviours and effects of the systems in real deployments. On the one hand, the market design approach in cryptoeconomics promises to deliver specific properties (information security or behavioural outcomes). But on the other hand, the simple rules of the systems designs produce complexity and unintended outcomes (Voshmgir and Zargham, 2019). A contradiction off-handedly commented on by Ethereum developer Floersch when discussing the Casper proof-of-stake approach: "[W]e have this complex behavior emerging from really simple economic rules, and this actually not specific to Casper by any means, this is any protocol that are messing around with economics" (Floersch, 2017, pp. 12-18).

This contradiction is nevertheless “productive” for those seeking to promote economic approaches to social problems: the promise of deterministic outcomes makes the models convincing and attractive from a formalist perspective (Green and Viljoen, 2020), while the complexity obscures any “failures” of the design (Nik-Khah and Mirowski, 2019). These shortcomings are instead relegated to being a problem “of the social” or “with humans” or that the implementation was not sufficiently faithful to the protocol, or even that the protocol implementation was not being expansive or radical enough. This contradiction is extensively covered in political economic and economic history and comprises one of the main critiques of the Austrian school of economics in particular (Mirowski and Nik-Khah, 2018; Heilbroner, 1998), what is also called the performative aspects of economics. From an information security perspective, the incorporation of economic incentives into protocol design in this sense radically increases the complexity of peer-to-peer systems, and correspondingly also increases the attack surfaces and variety of vulnerabilities (Alsindi, 2019).

3. Conclusion

In summary, cryptoeconomics refers to an emerging field that employs economic concepts, primarily from game theory, in the design of peer-to-peer cryptographic systems. The origins of the field lie in specific information security problems arising out of such systems. Competing approaches draw from a much wider field of economic and political economic thinking, including mutual credit systems and commons frameworks, in order to address questions of organisation and societal outcomes more broadly.

References

0xSalon (2020). Epistemic Trespassing Salon Report - Aside on Cryptoeconomic Systems - a case study in attempted epistemic formation. https://0xsalon.pubpub.org/pub/it4vigwo/release/8#aside-on-cryptoeconomi...

Alsindi, W. Z. (2019). TokenSpace: A Conceptual Framework for Cryptographic Asset Taxonomies, Parallel Industries. https://tokenspace.pubpub.org/pub/z0gjv399/release/6#regulating-securiti....

Brekke, J. K. (2020). Hacker-engineers and Their Economies: The Political Economy of Decentralised Networks and ‘Cryptoeconomics’. New Political Economy. https://doi.org/10.1080/13563467.2020.1806223

Brock, A., Atkinson, D., Friedman, E., Harris-Braun, E., Mcguire, E., Russell, J. M., Perrin, N., Luck, N., and Harris-Braun, W. (2017). Holo Green Paper. https://files.holo.host/2017/12/Holo-Green-Paper.pdf.

Brown-Cohen, J., Narayanan, A., Psomas, C., Weinberg, S. M. (2018). Formal Barriers to Longest-Chain Proof-of-Stake Protocols. https://arxiv.org/abs/1809.06528

Buterin, V. (2017). Introduction to Cryptoeconomics, Ethereum Foundation. https://youtu.be/pKqdjaH1dRo.

Catlow, R. (2019). Decentralization and Commoning the Arts. In Free/Libre, Technologies, Arts and the Commons. Nicosia, Cyprus: University of Nicosia Research Foundation.

Daian, P., Goldfeder, S., Kell, T., Li, Y., Zhao, X., Bentov, I., Breidenbach, L., and Juels, A. (2019). Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges. https://arxiv.org/abs/1904.05234

Davidson, S., De Filippi, P., and Potts, J. (2016). Economics of Blockchain. http://dx.doi.org/10.2139/ssrn.2744751.

De Filippi, P., and Hassan, P. (2015). Measuring Value in the Commons-Based Ecosystem: Bridging the Gap Between the Commons and the Market. In Moneylab Reader: An Intervention in Digital Economy, 74–91. Amsterdam: Institute of Network Cultures. https://ssrn.com/abstract=2725399

Floersch, K. (2017). Casper Proof of Stake, Cryptoeconomics and Security Conference, Berkeley. https://youtu.be/ycF0WFHY5kc.

Golumbia, D. (2016). The Politics of Bitcoin, Software as Right-Wing Extremism. Minneapolis: University of Minnesota Press.

Green, B., and Viljoen, S. (2020). Algorithmic Realism: Expanding the Boundaries of Algorithmic Thought. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT '20). Association for Computing Machinery, New York, NY, USA, 19–31. DOI: https://doi.org/10.1145/3351095.3372840

Halaburda, H., Haeringer, G., Gans, J., and Gandal, N. (2018). The Microeconomics of Cryptocurrencies. NYU Stern School of Business, Baruch College Zicklin School of Business Research Paper No. 2018-10-02, https://dx.doi.org/10.2139/ssrn.3274331

Heilbroner, R., 1998, The self‐deception of economics. https://www.tandfonline.com/doi/abs/10.1080/08913819808443490

Mirowski, P., and Nik-Khah, E. (2018). The Knowledge We Have Lost In Information – The History Of Information in Modern Economics. Oxford University Press.

Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf.

Nik-Khah, E., and Mirowski, P. (2019). On Going the Market One Better: Economic Market Design and the Contradictions of Building Markets for Public Purposes. Economy and Society 48 (2): 268–94. https://doi.org/10.1080/03085147.2019.1576431.

Ossandón, J. (2019). Notes on Market Design and Economic Sociology. Economic Sociology 20 (2): 9.

Swartz, L. (2018). What Was Bitcoin, What Will It Be? The Techno-Economic Imaginaries of a New Money Technology. Cultural Studies 32 (4): 623–50. https://doi.org/10.1080/09502386.2017.1416420.

Titcomb, A. (2019). Deep Dive: Augmented Bonding Curves. 4 October 2019. https://medium.com/giveth/deep-dive-augmented-bonding-curves-3f1f7c1fa751.

Virtanen, A., Lee, B., Wosnitzer, R., and Bryan, D. (2018). Economics Back into Cryptoeconomics. https://medium.com/econaut/economics-back-into-cryptoeconomics-20471f5ceeea.

Voskuill, E. (2018). Cryptoeconomics Wiki. https://github.com/libbitcoin/libbitcoin-system/wiki/Cryptoeconomics

Voshmgir, S. (2019). Token Economy: How Blockchains and Smart Contracts Revolutionize the Economy. Berlin: Blockchain Hub Berlin.

Voshmgir, S., and Zargham, M. (2019). Foundations of Cryptoeconomic Systems. Cryptoeconomic Systems Conference 2020, MIT. https://epub.wu.ac.at/7309/

Zamfir, V. (2015). What Is Cryptoeconomics? Cryptoeconomicon, CCRG. https://youtu.be/9lw3s7iGUXQ.

Aron Fischer, Colony
María-Cruz Valiente, Universidad Complutense de Madrid

1. Existing definitions of the term ‘governance’

The importance of governance is well recognised in the information technology (IT) industry (ITSM Library, 2008), and this term is widely used in academic, economic and policy debates. In the blockchain space, this term has been tightly linked to Decentralised Autonomous Organisations (DAOs) (Ethereum Whitepaper, 2013). Unfortunately, there is no common understanding, or generally accepted formal definition of governance, when associated with blockchain-based technologies. In pursuit of a formalisation of this term, before going more deeply into its evolution in the context of blockchain technology, we will briefly chart out a few common definitions.

The origins and most common approaches to governance are thoroughly dealt with by Hufty (2011), and as stated by Bevir (2011), at the most general level, governance can be associated with “theories and issues of social coordination and the nature of all patterns of rule”. The Oxford English Dictionary defines governance as “the action or fact of governing a nation, a person, an activity, one's desires, etc.; direction, rule; regulation.” In an economics context, governance is defined as “the use of institutions, structures of authority and collaboration to allocate resources and coordinate the effort and activity in society or in the economy” (Bell, 2002).

On the other hand, from an IT perspective, governance is composed of the leadership and the set of structures and processes that guarantee that the IT of an organisation provides support and extends the organisation’s strategy and objectives in a manner that is focused on achieving a better alignment between the business and IT (Bon, 2008). In contrast, corporate governance is “the whole set of legal, cultural, and institutional arrangements that determine what publicly traded corporations can do, who controls them, how that control is exercised, and how the risks and returns from the activities they undertake are allocated” (Clarke, 2012). However, the meaning of corporate governance could vary considerably according to the values, institutions, culture and objectives pursued by each organisation as well as the corporate governance system in the jurisdiction where the corporation is registered (Pollman, 2019; Norbäck and Persson, 2009). Corporate governance is not just about accountability, and it has an important role enabling strategising, value creation and innovation, as highlighted by Kraakman et al. (2017).

2. Issues currently associated with the term in the Blockchain domain

Despite the gap in literature due to the lack of a formal, comprehensive and holistic definition of what governance means in different domains, we can find several papers focused on governance whose approaches are applied or could be applied to the blockchain technology.

For example, Fuster (2014) proposes a comprehensive framework encompassing all the diverse aspects determining governance of Online Creation Communities (OCCs), based on notions of governance of the commons derived from the study of natural resources, particularly the work of the Nobel-laureate Elinor Ostrom (1990). Here governance is considered as the direction, control and coordination of a dynamic process, which evolves over time and manages several aspects of power classified by eight interrelated categories, from cultural principles/social norms and formal rules or policies, to infrastructure provision. More concretely, Rozas et al. (2018) specifically explore the transformative potential of blockchains while drawing on Ostrom’s (1990) principles for self-governance. They identify and conceptualise six affordances that blockchains may provide including tokenisation, formalisation and decentralisation of rules, autonomous automatisation, decentralisation of power over the infrastructure, increase in transparency and codification of trust.

De Filippi and Wright (2018) review blockchain technology and explore the idea that governance attached to organisations could be implemented in blockchain-based decentralised software systems through smart contracts (i.e., small pieces of code deployed on the blockchain). Similarly, Davidson et al. (2016) share the idea that by eliminating the need for trust of agreed contracts through consensus and transparency, blockchains enable a new type of economy for self-governing organisations with the coordination properties of a market. Nevertheless, as stated in Yermack (2017), the fact that the blockchain is operated autonomously, could itself raise problems for corporate governance, such as corporate record-keeping and the maintenance and upgrading of blockchains themselves.

Meanwhile Reijers et al. (2016) explore how blockchain technology enables the configuration of specific forms of political organisation using the Ethereum network as a case study.

In another line of research, Karjalainen (2020) presents an interesting survey of governance models in blockchain-based decentralised networks. It is worth highlighting that consensus mechanisms inherent in blockchain transactions have been excluded from this study.

3. Usage of the term ‘blockchain governance’

We find interesting visions of governance in the context of blockchain, for instance, in the works presented by Finck (2018) and Reijers et al. (2018). However, the academic research for blockchain governance is still somewhat sparse (see also: Pelt et al., 2020), and while governance is a much discussed topic at blockchain conferences, such as Ethereum Devcon (DevCon Archive), EDCon (EDCON), ETHCC (ETHCC) and DAO Fest (DAOFEST), the written record still comprises mostly of blog posts and social media entries of dubious quality.

As stated earlier, all governance is ultimately a social construct, comprising not simply laws (or by-laws), but also norms, culture, institutions, and individuals. Despite impassioned claims to the contrary, this is no different in regard to blockchains.

To understand the (mis-)usage of the notion of blockchain governance, we must first consider what specifically blockchains bring to the table: they enable systems in which adherence to procedure is automatically enforced, relying neither on norms nor a legal system, and leaving no room for individual discretion. This strict separation of enforceable procedure on the one hand and norms and discretion on the other is genuinely novel, but its import is exaggerated. Among the more enthusiastic supporters of blockchain technology, we observe a tendency to wilfully ignore all questions of norms and culture and equate governance entirely with coded procedures ("code is law"). Once all governance is reduced to procedure, it is hard to resist the claim that blockchains change everything.

This mixture of confusion and hubris is exemplified nicely in Singh (2020), who introduces "standard" governance as being either direct governance or representative governance, thus conflating governance with voting procedures, and asserting that everything is different with the blockchain: "We can broadly categorize the governance types into two major categories: Standard Governance and Blockchain Governance".

A further ambiguity stems from the fact that blockchain governance is used in two related but distinct contexts, on top of which usage in the first context is further complicated by the highly polarised and politicised nature of the blockchain space where we observe different factions reinterpreting and redefining the phrase to fit their outlook.

In this first usage, blockchain governance refers to governance of the blockchain (i.e. the specific question of making consensus-relevant changes to the software running a blockchain). Consensus relevance here means a change to the internal rules of the blockchain that must be applied (i.e., software must be updated) by all relevant participants in the blockchain network such as cryptocurrency exchanges, wallet software providers, miners, and users. If a large enough portion of the network does not apply the changes, then the network splits into two: those following the new rules and those following the old rules - this is called a hard fork[1].

Examples of this approach include: (i) Curran (2020), who uses blockchain governance to vaguely mean whatever process leads to consensus-relevant changes in the software, and hard forks are hailed as a safety valve for users to choose their own fork if things go awry; and (ii) Rajarshi (2020), where governance is conflated with voting procedures, and hard forks are hailed as enabling "much more flexibility in operation than traditional structures" because "a user is free to choose which blockchain to follow."

In this context, we typically observe the introduction of a strict separation of governance into off-chain governance and on-chain governance.

The main idea of on-chain governance is to use coded procedures within a blockchain that represent voting procedures by which decisions about consensus-relevant software upgrades are mediated through the consensus system itself. Usage of the term in industry is neatly summarised by Frankenfield (2018): "On-chain governance is a system for managing and implementing changes to cryptocurrency blockchains. In this type of governance, rules for instituting changes are encoded into the blockchain protocol. Developers propose changes through code updates and each node votes on whether to accept or reject the proposed change".

Proponents of this way of doing things disparage the off-chain (human) world as begin outdated in its reliance on people, norms, and culture to achieve governance, specifically alleging that procedures might be ill-defined or opaque: "off-chain collectives that organize over phone calls or at conferences, which either leads to shadow hierarchies where only a few, unwritten people make decisions" (Petrowski, 2020). Central to this line of thought is that anything on-chain is transparent and thus fair, and anything off-chain is hidden and potentially nefarious. This stands in contrast to the Bitcoin notion that all consensus relevant changes are bad because they represent human involvement and in as much as code is law, they are breaking the law (de Filippi and Wright, 2018). On-chain governance, they argue, only aids and abets such law breaking; arguing that the goal is not coordinated updates to the network, but immutability.

The other context in which blockchain governance is used ignores the previous question entirely and focuses on "using the blockchain to achieve governance". It presupposes the existence of a functioning blockchain network such as Ethereum, which can be leveraged to deploy smart contracts that encode the procedures of a decision-making paradigm. The blockchain is used to force/guarantee adherence to procedure, but the decisions being made have nothing to do with the blockchain itself (i.e., upgrading, avoiding hard forks). Rather, the goal of this form of on-chain governance is to enable the creation and operation of DAOs (i.e., organisations whose by-laws are written in code and enforced by the blockchain).

Once a DAO has been deployed to a blockchain, its rules can no longer be changed - short of a hard fork of the underlying network. Envisioning the need for future changes, DAO authors must incorporate the rules-for-changing-the-rules in the original deployment. We may think of this as analogous to an ordinary legislative process, coupled with a process for amending the constitution that the legislation is based on.

Current prominent examples of DAO platforms such as Aragon (Aragon, 2020) and Daostack (DAOstack, 2020) place heavy emphasis on a process in which proposals - usually to reallocate cryptocurrency funds - are put forward, a voting procedure then determines passage of the proposal, and eventually the funds are moved. This all happens on the blockchain, though off-chain communication and discussion are alluded to. Other examples such as Colony (Colony, 2020) take a more holistic view of governance, involving primarily off-chain interactions between human beings to come up with ideas and make decisions, and usage of the blockchain is reserved for enforcement, as opposed to decision making, whenever this is feasible.

It is worth noting that all DAO projects are ultimately a mixture of off-chain and on-chain elements, echoing the idea that even with blockchains and cryptocurrencies, governance consists of more than coded procedures.

4. Conclusion

As we have seen, the concept of blockchain governance is still under development and it can be understood differently depending on the domain of the application area under discussion.

In a broad sense, blockchain governance can be regarded as the integration of norms and culture, the laws and the code, the people and the institutions that facilitate coordination and together determine a given organisation. Importantly it refers to the entirety of motivations, rules, and activities that feed into the establishment of choices and subsequently deciding on them, and includes, but is not limited to, any coded on-chain rules that guide these processes.

However, blockchain governance also refers to two distinct dimensions: off-chain governance vs on-chain governance.

When referring strictly to smart contracts, one should specify that one is referring specifically to the on-chain elements of the governance system in question. Further care should also be taken to clarify whether one is talking about governance of a blockchain's own consensus relevant rules, or whether the governance system in question is merely using a blockchain to enforce on-chain rules in an otherwise unrelated off-chain domain.

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1. This term itself is not well defined. Thus hard fork may refer to a network split where different actors in the network follow different rules, whether due to an update that was not universally installed or due to a software flaw; but it is also used to describe a successful network upgrade that could have led to a split but did not. ↑

Mortiz Becker, Weizenbaum-Institute for the Networked Society
Balázs Bodó, University of Amsterdam

Conceptual background

The notion of trust is of key significance, with a broad literature spanning from social sciences via law to computer science (Blöbaum, 2016; Bodó, 2020; Botsman, 2017; Clarke, 2006; Fukuyama, 1995; Gambetta, 1988; Giddens, 1990; Hardin, 2002; Luhmann, 2017; McKnight et al., 2011; Putnam, 2001; Schneier, 2012; Sztompka, 1999). This leads to substantial confusions when it comes to discussing trust in the context of digital technologies in general, and in the case of distributed technologies in particular (Baldwin, 2018; Bellini et al., 2020; Dingle, 2018; Jacobs, 2020; Werbach, 2018a). We do not try to represent all aspects of these different disciplinary discussions. Instead, we used a simplified model of trust adapted from the work of McKnight et al. (2011) to give a basic overview and point out the issues that are most relevant for the issue of trust in the context of blockchains and other distributed techno-social systems.

Trust relationships always involve a number of actors: (1) a trustor, with his or her individual attitudes, trusting beliefs, stands towards trusting, and “generalized faith in humanity”, (2) a trustee, that can be an individual, in which case we talk about interpersonal trust (Hardin, 2002), or an institution, the government, or a profession, in which case we talk about institutional, or systemic trust (Giddens, 1990). Trust is the instrument with which the trustor manages the contingencies that relate to trusting the trustee to act competently, in the interest of the trustor in concrete given contexts.

The emergence of trust has three prerequisites. First, it depends on the attitudes, beliefs of the trustor. Second, it is a factor of the (perceived) trustworthiness of the trustee: its past actions, reputation, objectively verifiable, or faith based qualities to be competent, benevolent, and maintain integrity (Mayer et al., 1995). Third, both sides are embedded in wider, institutional environments, which create shared knowledge, a shared understanding of general, and context specific rules of the game (Shapiro, 1987; Zucker, 1985), and which can provide structural assurances on the behaviour of the trustee for the trustor. These latter include legal instruments, such as laws (Balkin, 2016; Hall, 2002), contracts (Foorman, 1997), government regulatory and oversight bodies, professional codes of conduct, governance and quality assurance, or market-based functions, such as insurance against risk.

Trust and distributed technologies

Within the context of trust and distributed technologies, therefore, the question of trust can have many dimensions. If the role of the distributed techno-social system is to connect people, if it allows, or relies on the collaboration of individuals, in the interpersonal trust dimension, the question is how can we (or: do we need to) trust the (often anonymous) stranger with whom we use the same distributed system? On the other hand, we also need to have some level of confidence in the system itself, and in that case we need to look at the institutional aspects of trust. Here, the main question is whether the technologies we rely on are trustworthy (Bodó, 2020). We can define technology in a narrow way, and thus the questions of trust in and trustworthiness of technical systems, and artefacts is simplified into the question of technical reliability: the security of computer systems, them being free of errors, and bugs, working as intended and advertised (Clarke, 2006). A broader definition would also consider the human and institutional elements which develop and operate those technical systems, and therefore give them agency. In such an approach, the question of trust becomes more akin to more traditional forms of institutional trust. The governance of technology covers these human and institutional elements, and the impact of the governance on the trustworthiness of technical systems turned this issue into a rapidly developing research field (Campbell-Verduyn, 2018; Elkin-Koren & Perel, 2019; Katzenbach & Ulbricht, 2019; Mattila & Seppälä, 2018). Finally, some technical systems mediate and produce trust relationships themselves (Bodó, 2020). For example, online reputation systems are designed to facilitate interactions that require trust. In these cases, the trustworthiness of these “trust producing systems” becomes an important issue in itself. The following remarks use blockchain as a case study to take a closer look at the controversies and questions associated with it from the perspective of trust.

The academic discussion on blockchain and trust

Blockchain technology – which was first introduced in 2008 in the context of the digital currency Bitcoin – is often seen as a trust producing technology that might make trustworthy intermediaries such as banks obsolete. Instead, it is often said to replace human-based intermediaries by “system based on cryptographic proof instead of trust” (Nakamoto, 2008, p. 1) i.e. a network in which all interactions between network participants are coordinated by mathematical and cryptographic code instead of human actors (Dodd, 2018, p. 37; Swartz, 2016). As a consequence, the technology takes a major role in the current public and academic discussion on trust and distributed technologies: some see it as a “machine for creating trust” (Berkeley, 2015), as reducing the cost of trust (Shahaab et al., 2020) or as an enabler of new technology-based modes of trust – “trustless trust” (e.g., Werbach, 2018a, 2ß18b; Hoffmann, 2015) or “distributed trust” (Botsman, 2017) – that might have a revolutionary impact on social coordination even outside the realm of distributed systems.

These academic discussions on blockchains and trust span across multiple disciplines such as computer science, economics, law and social sciences. Within these discussions, two key controversies can be identified: the first refers to the conceptual question of what is actually meant when referring to the term trust. The second controversy refers to the substantive question of how blockchain technology and trust are related: does blockchain increase trust, decrease trust, make trust obsolete, or represent a shift in the nature of trust?

Regarding the conceptual controversy, different understandings of trust can be identified. While some works understand trust as an attribute of the technological system itself (as e.g. suggested by ‘trust models’ rooted in computer sciences, see Harz & Boman, 2019), others rather understand trust as a system of intersubjective expectations between individuals that is not necessarily determined by technology (more often so in the social sciences, e.g. Vidan & Lehdonvirta, 2018). From the perspective of trust research, it is vital to recognise these conceptual differences, as these might have a significant impact on the substantive conclusions taken in respect to the nature of trust. Moreover, many academic works provide no precise and theoretically-informed definition of trust (e.g., Davidson et al., 2018; Flood & Robb, 2017; Beck et al., 2016), leaving its meaning vague and ambiguous.

In addition to these conceptual differences, academic works also exhibit substantial differences regarding how blockchain and trust are related. Two dominant views can be identified. Proponents of the first view stress the “trust-free” (Beck et al., 2016) or “trustless” (Harz & Boman, 2019; de Filippi & Hassan, 2016; Davidson et al., 2018) capabilities of blockchain technology, assuming it to enable coordination without requiring interpersonal trust between network participants (Maurer et al., 2013, p. 261). In contrast to this view, the second line of academic works emphasises that blockchain networks are – in fact – not completely trustless and that trust enters the network at many levels and contexts (e.g., Corradi & Höfner, 2018, p. 203; Dodd, 2018; Vidan & Lehdonvirta, 2018). Rather than assuming it to abolish (interpersonal) trust, this line of studies rather assumes a shift of the nature of trust by blockchain, replacing interpersonal trust with trust (or: confidence, see de Filippi et al., 2020) in code, other intermediaries or governance structures.[1]

Takeaways for future research

Against the background of these controversies, two things can be learned for the study of trust in distributed systems: firstly, they corroborate the insight that finding a common theoretical language of the technological aspects of trust among multiple academic disciplines is of utmost importance. Secondly, the oft-quoted finding that blockchain resulting in a shift of trust rather than its abolishment leads to new empirical follow-up questions:

For instance, do network users put trust in the technology itself or in the humans behind it (Walch, 2019, p. 59)?[2] What are sources of trustworthiness of distributed (blockchain) systems, particularly in the case of legal (un-)certainty? How do users behave vis-à-vis a system which may or may not be trustworthy, e.g. in the case of the blockchain-based venture capital fund “The DAO” (DuPont, 2018)? Are the technical aspects of a blockchain system enough to establish their trustworthiness (e.g. in the case of crypto-investors against questionable financial products)? How do past accounts of the trustworthiness of institutions (e.g. Sztompka, 1999) compare in relation to blockchain technology?

Addressing these questions should be an important objective for future academic research which might foster our understanding of blockchain technology and trust as well as the role of trust in distributed systems more generally. Important steps into this direction are for instance empirical studies on specific networks using blockchain technology (e.g., Woodall & Ringel, 2019; Meijer & Ubacht, 2018; Vidan & Lehdonvirta, 2018; Lustig & Nardi, 2015) as well as theoretical works that situate the case of blockchain within the broader discourse on trust and technology (e.g., Bodó, 2020; Jacobs, 2020). Moreover, as most empirical studies on trust and blockchain technology concentrate on the Bitcoin blockchain (e.g., Vidan & Lehdonvirta, 2018; Lustig & Nardi, 2015), it would be particularly interesting to see how this case compares to other blockchain applications.

Conclusion and working definition

In conclusion, the case of blockchain illustrates that the fundamental question of distributed systems of “how can we (or: do we need to) trust the (often anonymous) stranger on the other side of a screen” is subject to historical change. Blockchain technology can be viewed as exemplifying a change in mediation structures of trust from interpersonal trust mediated by human-based intermediaries to technological intermediaries. Developing new terms of trust that can account for this institutional change by blockchain technology and conducting empirical studies on this topic are therefore of utmost importance for further research on trust and distributed technologies. Based on our theoretical reflections above, we propose the following working definition of trust that might serve as a reference point for future studies on trust in the context of distributed technologies:

Trust is a complex social phenomenon with interrelated individual (psychological, attitudinal, informational), and systemic (economic, legal, technological, social) aspects. It is best understood as a relational attribute between (1) a social actor and other actor(s) (interpersonal trust) and / or (2) actors and institutions (institutional or systemic trust) and (3) institutions and (trusting) actors (trust as shared expectations), where institutional frameworks define the nature and strength of trust relationships between different actors. In essence, trust refers to expectations of the trustor made towards the trustee about the occurrence of future actions and / or events (under specific external / environmental conditions) which are often connected to a risk for the trustor. Trust denotes the reliance on the trustee despite this risk and can thus be understood as a way of managing contingencies of modern life. It involves both emotional and cognitive elements and is thus to be distinguished from (blind) faith and confidence (Lewis & Weigert, 1985). In the face of recent technological change, we claim that the technological environment has played an increasingly important role in setting the conditions of trust relationships, as evident in the case of blockchain. Future research is needed to not only address the technical aspects of these technologies, but also study their broader social and cultural contexts shaping their emergence and production.

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1. For instance, when using a digital currency, a user has to trust the technological architecture itself, as well as new intermediaries such as crypto-currency exchanges (Brekke 2019: 83-84). ↑

2. The importance of human actors for the perceived trustworthiness of a system has e.g. been recognised by academic works dealing with the interrelationship of trust and governance (e.g. De Filippi and Loveluck 2016). ↑

Samer Hassan, Complutense University of Madrid
Primavera De Filippi, French National Centre for Scientific Research

Origin and evolution of the term

Organisation Theory has abundant literature on decentralised organisations of several kinds (Shubik, 1962; Beckhard, 1966; Freeland and Baker, 1975). Yet, the first references to actual “Decentralized Autonomous Organization” (DAO) only emerged in the 1990s, to describe multi-agent systems in an IoT environment (Dilger, 1997) or nonviolent decentralised action in the counter-globalisation social movement (Schneider, 2014).

However, the modern meaning of DAOs can be traced back to the earlier concept of a Decentralized Autonomous Corporation (DAC), coined a few years after the appearance of Bitcoin (Nakamoto, 2008). The DAC concept was used mostly informally in online forums and chats by early crypto-currency enthusiasts, using both decentralized and distributed autonomous corporations interchangeably. It was only in 2013 that the term became more widely adopted, and publicly discussed in a variety of websites (S. Larimer, 2013) (D. Larimer, 2013), in particular by the co-founder of Bitcoin Magazine Vitalik Buterin[1] (Buterin, 2013).

DACs were described as a new corporate governance form, using tokenised tradable shares as a means of providing dividends to shareholders. Such corporations were described as “incorruptible”, running “without any human involvement” and with “publicly auditable” bylaws as “open source software distributed across the computers of their stakeholders” (S. Larimer, 2013). According to this definition, anyone could become a stakeholder in a DAC by simply “buying stock in the company or being paid in that stock to provide services for the company”. As a result, the owners of a DAC stock would be entitled to “a share of its profits, participation in its growth, and/or a say in how it is run”. (ibid). Such a definition reflects the maximalist view of many blockchain advocates considering that “DACs don’t need regulation” because “you don’t want to regulate them, and happily you can’t” (ibid).

The term was inherently linked to corporate governance and therefore was too restrictive for many blockchain-based applications with a more general-purpose. Thus, several alternatives to the term appeared, leading to the emergence of Decentralized Applications (DApps) (Johnston, 2013), and later to the generalisation of DAOs as a replacement for DACs (Buterin 2014).

While some argue that Bitcoin is effectively the first DAO (Buterin, 2014; Hsieh et al., 2019), the term is today understood as referring not to a blockchain network in and of itself, but rather to organisations deployed as smart contracts on top of an existing blockchain network. Although there have been several attempts at instantiating a DAO on the Ethereum blockchain (Tufnell, 2014), the first DAO that attracted widespread attention is a 2016 venture capital fund confusingly called “TheDAO” (DuPont, 2017). Despite the short-life of the experiment[2], TheDAO has inspired a variety of new DAOs (e.g., MolochDAO, MetaCartel), including several platforms aimed at facilitating DAO deployment with a DAO-as-a-service model, such as Aragon, DAOstack, Colony or DAOhaus.

The DAO concept has enabled other derived terms: the term Decentralized Collaborative Organization (DCO) is typically referred as a DAO with strengthened collaborative aspects (Hall, 2015; Schiener, 2015; Davidson, de Filippi, and Potts, 2018); a more elaborate concept derived from those attempts is “Distributed Cooperative Organization” (DisCO), which highlights its co-op and democratic nature (DisCO Manifesto, 2019).

Definitions in the field

There are multiple coexisting definitions of DAOs in use within the industry. The most relevant are the following:

• Buterin, in the Ethereum white paper (Ethereum, 2013), defines a DAO as a “virtual entity that has a certain set of members or shareholders which [...] have the right to spend the entity's funds and modify its code”. That is, the aim is to replicate “the legal trappings of a traditional company or nonprofit but using only cryptographic blockchain technology for enforcement”.
• Some of the most popular DAO platforms, such as DAOstack and Aragon define a DAO similarly as “a network of stakeholders with no central governing body” (https://daostack.io), “which is regulated by a set of automatically enforceable rules on a public blockchain” (https://aragon.org/dao). Conversely, other DAOs platforms have opted to use a different terminology as a proxy to a DAO, such as the “colonies” of Colony (https://colony.io) or DAOhaus’ “magic internet communities”(http://daohaus.club).

In the academic literature on DAOs, although some works avoid picking a definition (Norta et al., 2015) or refer to industry definitions (DiRose and Mansouri, 2018), multiple attempts have been made at providing a specific definition of DAOs. Most of these definitions include the following distinctive characteristics:

• DAOs enable people to coordinate and self-govern themselves online.[3] Although no mention is made as to the minimum size of the group, the term “organization” is generally understood to refer to an entity comprising multiple people acting towards a common goal[4], rather than a legally registered organisation.
• A DAO source code is deployed in a blockchain with smart contract capabilities like Ethereum—arguably always a public[5] blockchain.
• A DAO’s smart contract code specifies the rules for interaction among people[6]—although it is unclear to which extent there may be other governance mechanisms that can affect or overrule such code.[7]
• Since these rules are defined using smart contracts, they are self-executed independently of the will of the parties.[8]
• The DAO governance should remain independent from central control:[9] e.g. some definitions specifically refer to self-governed (De Filippi and Hassan, 2018), self-organising (Singh & Kim, 2019), peer-to-peer and democratic control (Hsieh et al., 2018).
• Since they rely on a blockchain, DAOs inherit some of its properties, such as transparency, cryptographic security, and decentralisation[10]

Current open discussions

While the academic literature on DAOs is still fairly limited, there is a significant number of papers from the field of computer sciences focusing on blockchain technology as a technical platform for building new blockchain-based business models, such as decentralised exchanges (Lin et al., 2019; Bansal et al., 2019) or market-based platforms such as prediction markets (Clark et al., 2014) that operate as decentralised organisations with automated governance (Jentzsch, 2016; Singh and Kim, 2019). Yet, a DAO can be deployed to fulfill many different types of functions. A DAO can, for example, be used to create a virtual entity that operates as a crowd-funding platform, a ride-sharing platform, a fully automated company, or a fully automated decision-making apparatus. It is therefore important to understand that a DAO is not a particular type of business model or a particular type of organisation, but a concept that can be used to refer to a wide variety of things.

In terms of governance, a variety of scholars recently started investigating the opportunities of blockchain technology and smart contracts to experiment with open and distributed governance structures (Leonhard, 2017; Rozas et al., 2018; Hsieh et al., 2018; Jonas, 2019), along with the challenges and limitations of doing so (Garrod, 2016; DuPont, 2017; Scott & al., 2017; Chohan, 2017; Verstreate, 2018; Minn, 2019; Hutten, 2019). There is also an emerging body of literature from the field of economic and legal theory concerning DAOs. While most of these works focus on the new opportunities of decentralised blockchain-based organisations in the realm of economics and governance (Davidson et al., 2016, 2018; Sims, 2019; Rikken et al., 2019; Kaal, 2020), others focus on the legal issues of DAOs from either a theoretical (De Filippi and Wright., 2018; Reijers et al., 2018) or practical perspective (Rodrigues, 2018; Werbach, 2018; Riva, 2019).

The political discourse around DAOs is more pronounced, at least in the context of many existing blockchain communities (Scott, 2015; Swartz, 2017; DuPont, 2019). Various authors have pointed out that DAOs could be used to further economic and political decentralisation in ways that may enable a more democratic and participatory form of governance (Swan, 2015; Atzori, 2015; Allen et al., 2017, Tapscott and Tapscott, 2017). However, as the limitations of blockchain-based governance came into light, especially in the aftermath of the aforementioned TheDAO hack (DuPont, 2017; Reijers & al. 2018; Mehar et al., 2019), the public discourse around DAOs has shifted from describing DAOs as a technical solution to a governance problem (Jentzsch, 2016; Voshmgir, 2017) to a discussion on how DAOs could change the nature of economic and political governance in general (Davidson et al., 2016; Beck et al., 2018; Zwitter and Hazenberg, 2020; De Filippi et al., 2020).

The use of the term “decentralized autonomous organisation” or DAO is now fairly established in the blockchain space, yet there are still many misconceptions and unresolved issues in the discussion around the term. (1) First of all, with regard to the “decentralization” aspect of a DAO, it is unclear whether decentralisation only needs to be established on the infrastructural layer (i.e. at the level of the underlying blockchain-based network) or whether it also needs to be implemented at the governance level (i.e., the DAO should not be controlled by any centralised actor or group of actors). (2) Second, it is unclear whether a DAO must be fully autonomous and fully automated (i.e. the DAO should operate without any human intervention whatsoever), or whether the concept of autonomy should be interpreted in a weaker sense, (i.e. while the DAO, as an organisation, may require the participation of its members, its governance should not be dependent on the whims of a small group of actors). (3) Third, there are some debates as to when the community of actors interacting with a smart contract can be regarded as an actual “organisation” (independently of legal recognition). For instance, it is unclear whether the mere act of transacting with a smart contract qualifies as an organisational activity, or whether a stronger degree of involvement is necessary, such as having a governance model or collective interactions amongst participants.

The latter has triggered important discussions in the blockchain and legal field, as regards whether a DAO could be considered as an entity separate from the human entities that operate it (i.e., as a legal person) or whether it can only be considered as an entity when it is identified as such by the law (i.e., the law should identify a DAO as a legal person for the DAO to be considered as such). Yet, the common understanding today is that the autonomous nature of a DAO is incompatible with the notion of legal personhood, as legal personhood can only be established if there is one or more identified actors responsible for the actions of a particular entity. The discussion on whether a DAO should be recognised as a legal person has important implications in the legal field, as it can determine the extent to which a DAO can be considered as a separate legal entity from its human actors, and therefore the extent to which these actors can be shielded from the liabilities of the DAO.

Concluding definition

A DAO is a blockchain-based system that enables people to coordinate and self-govern themselves mediated by a set of self-executing rules deployed on a public blockchain, and whose governance is decentralised (i.e. independent from central control).

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1. Vitalik Buterin would later co-found the Ethereum platform in 2014. ↑

2. This open-source project attracted 11,000 investors and $150 million, where the funds were operated by the code implemented, theoretically safe from managerial corruption. However, a bug in its code enabled vulnerabilities exploited by an attacker who stole $50 million, requiring a fork in the Ethereum blockchain to restore the funds. ↑

3. See e.g. Singh and Kim (2019) who describe a DAO as a “a novel scalable, self-organizing coordination on the blockchain, controlled by smart contracts” ↑

4. See e.g. El Faqir, Arroyo and Hassan (2020) according to which a DAO is made up of “people with common goals that join under a blockchain infrastructure that enforces a set of shared rules” ↑

5. See e.g. Hsieh et al. (2018) claiming that a DAO should be deployed on a “public network”. ↑

6. See e.g. De Filippi and Hassan (2018), describing a DAO as a“self-governed organization controlled only and exclusively by an incorruptible set of rules, implemented under the form of a smart contract”. ↑

7. See e.g. Singth and Kim (2019)’s definition of a DAO as “an organization whose essential operations are automated agreeing to rules and principles assigned in code without human involvement”. However, this definition is put into question by Reijers, Wuisman, Mannan, De Filippi et al. (2018) distinguishing between “on-chain” and “off-chain” governance in the governance structure of DAOs. ↑

8. See also De Filippi and Wright (2018), according to which a DAO “represents the most advanced state of automation, where a blockchain-based organization is run not by humans or group consensus, but rather entirely by smart contracts, algorithms, and deterministic code.” ↑

9. See e.g. Hsieh et al. (2018) describing DAOs as “non-hierarchical organizations that perform and record routine tasks on a peer-to-peer, cryptographically secure, public network, and rely on the voluntary contributions of their internal stakeholders to operate, manage, and evolve the organization through a democratic consultation process” (Hsieh et al., 2018) ↑

10. “A decentralized, transparent, and secure system for operation and governance among independent participants” which “can run autonomously” (Beck, 2018) ↑

Cryptomarkets operating on the darknet are a recent phenomenon that have gained importance only over the last couple of years (Barratt 2012, Martin 2014). However, in the meantime they constitute an evolving part of illicit drug markets. Although selling and buying a variety of psychoactive substances on the Internet has a long history, technological innovations enable systematic drug trading on the net. Technological innovations like the combination of anonymising software and virtual currencies such as Bitcoin allow users to proceed with (illicit) drug transactions with almost completely anonymous identities and locations. This paper builds upon Beckert’s (2009) social order approach to explore how the coordination problem of cooperation is being resolved by social practices of users of cryptomarkets in order to allow for ‘stable worlds’ (Fligstein 2001). The cooperation problem refers to an asymmetric distribution of information and freedom of choice regarding price, product quality and possible intentions of exchange partners. Social practices of cooperation help to reduce the social risks entailed in the exchange process. To explore such social practices, this paper draws on digital ethnography (Coleman 2010) of cryptomarkets, including online monitoring of marketplaces, online observation of various discussion forums related to anonymous drug marketplaces and self-presentations of users on cryptomarkets. Insights into cooperative practices are presented and implications for the trade-off between ‘possibility of freedom and necessity of control’ (Jasanoff 2004) are discussed.

The current technological context of hyperconnectivity brings significant challenges to the protection of fundamental rights and to contemporary ethics, with the capacity to impact, ultimately, democracy itself. In that context, the action of algorithms can be seen in a much broader and complex context of action and decision. Algorithms today not only predict upcoming best-seller books, but also suggest our future loving partners, influence our electoral decisions, make death threats, decide who should be imprisoned, and buy illegal drugs on the Deep Web. In this sense, it is not enough just to realize the ability of algorithms to act and decide as human beings, it is necessary to think about how the public sphere is being influenced by these agents capable of shaping, structuring and mediating the way we interact. The analysis of a public sphere, based on human communicative rationality, need new epistemological and ontological lenses to rethink assumptions about agency, transparency and normativity upon understanding the influence and interactions of those non-human agents, to ensure appropriate ethical guidelines to the advances of hyperconnectivity. The theoretical frameworks chosen for this analysis are Jurgen Habermas’s concept of public sphere and communicative action in contrast with the contributions of Karen Barad's theories about new materialisms, with the purpose of becoming an university paper.