Tokens Classification: a Morphological Framework
This issue is the second of the OverTheBlock Tokenomics Series, a deep dive into the blockchain technology’s economic and social implications. We investigate the transformative role of the token and the rise of new business and organizational paradigms.
The framework presented in the post was accepted for publication by the Journal of Blockchain Research and Applications (link).
As discussed in the first issue of the OverTheBlock Tokenomics Series, to reach an overall understanding of the multiform nature of tokens, a comprehensive taxonomy approach is needed.
If, in general terms, tokens can be described as a quantifiable representation of decentralized and disintermediated trust, an operational definition of existing (and future) tokens must include all the diverse shades of trust they convey. Expanding the definition of tokens needs a systematic and ordered procedure, and resorting to the concept of taxonomy, borrowed from the natural sciences, has established itself as a common practice among scholars and experts dealing with this challenge.
Drafting a taxonomy is, indeed, ordering classified items within a hierarchical arrangement. Therefore, before creating a tokens taxonomy, we need to establish a comprehensive tokens classification framework that allows us to accurately map all the different branches of value representations that tokens can convey. In performing such classification, it is fundamental to keep in mind that trust is core and permeates all the expressions of value brought by every kind of token.
we need to establish a comprehensive tokens classification framework that allows us to accurately map all the different branches of value representations that tokens can convey
State of the Art in Tokens Classification
A fair number of tokens classifications and taxonomies have been proposed so far. Oliveira et al. present one of the most comprehensive studies on the topic, combining extensive literature research with insights collected through 16 interviews with experts and practitioners. The resulting classification characterizes tokens using 13 different parameters, describing both their technical features and business-related aspects. Furthermore, mapping 18 tokens against those parameters, eight token archetypes were identified by analyzing recurring features patterns. Oliveira’s work is grounded on several classifications previously proposed, two of which are worth being mentioned.
The Token Classification Framework presented by Thomas Euler classifies tokens along five dimensions: purpose, utility, legal status, underlying value, and technical layer. Such a framework has the goal to look at a single token from different perspectives simultaneously, and it is a first tentative to capture and summarize the polyhedric nature of tokens.
William Mougayar provides another relevant contribution to the collective effort in capturing the nature of tokens. His framework revolves around three classification dimensions: role, features, and purpose. It focuses primarily on the business-related aspects of tokens, with particular reference to their entanglement with the token issuer’s business model and the incentives arising for the token holders.
Looking at the contribution in the field of token classification and taxonomy coming from companies and business practitioners, the Taxonomy Report on Cryptoassets by CryptoCompare is an extensive analysis that provides several valuable approaches to the classification of tokens. More than 200 tokens have been examined and classified using 30 unique attributes, covering economic, legal, and technological features. In particular, two grouping criteria are of particular interest: Rationale to Possess and Economic Value Drivers. The former classifies tokens according to the main reason that drives token holders to acquire and keep their tokens, while the latter groups them based on the mechanisms underlying their price trend and fluctuations. These grouping criteria represent a well-structured approach to tokens’ classification relating to the incentive system that they convey and the behavior they intend to induce into token holders.
More recently, in November 2019, the Token Taxonomy Initiative (now InterWork Alliance) published the first version of the Token Taxonomy Framework, resulting from a broad cross-industry effort to define a common standard to describe and design tokens. In particular, such a framework aims at introducing not another standard, but rather to create a metastandard. Its role is to bridge token protocols and platforms to ensure effective interoperability, lay the foundation of a shared understanding of tokens, and create a shared language that can empower communication among technology experts and business practitioners. Proceeding on first-principles thinking, the Token Taxonomy Framework classifies tokens along with five characteristics: Token Type, Token Unit, Value Type, Representation Type, and Template Type. Those characteristics are intended to describe a token nature and the role it plays within a business model. Furthermore, the Token Taxonomy Framework’s description approach is particularly interesting, since it represents each token through a formula combining base types, behaviors, and property sets of the token. It is a modular approach that can reuse and combine token features and, even if it is at an early conceptual stage, it is promising.
From this overview of the most relevant token classifications and taxonomies available in the literature, it results that the approaches adopted are very diverse and the methodology heterogeneous. Therefore, to perform a comparative assessment of the classification frameworks, a normalization is needed at first.
General Morphological Analysis
Both tokens and their classifications show a high degree of variety. Such heterogeneity calls for a structured methodology, and we chose the General Morphological Analysis (GMA). GMA was developed by Fritz Zwicky as a method to describe and assess problem complexes, characterized by non-quantifiable and multi-dimensional properties, by mapping all the possible relationships, or configurations, occurring in the given problem complex. Such a morphological approach is eventually targeted at identifying recurring patterns and building non-quantified inference models.
Given its characteristics and previous applications, GMA represents a comprehensive methodology that can be effectively applied in the description of tokens comprising all their non-quantifiable and multi-dimensional properties. In particular, a token classification framework can be represented using a morphological field, resulting from the following steps:
- identify and adequately define the dimensions of the problem, i.e., the parameters along which each token can be mapped;
- for each dimension of the problem, a spectrum of values must be defined. These values represent the relevant states or conditions that each dimension can assume.
- a morphological field is generated, including all the possible combinations of values assumed for each dimension. The number of all the possible configurations of the problem complex is the product of the number of conditions (values) under each parameter (dimension).
The GMA methodology is used in the first place to perform a comparative analysis of the token classification frameworks. In particular, this procedure is leveraged to address the need for normalization previously highlighted, and all the frameworks are traced back to a morphological field representation to make them comparable. Once normalized, the different token classification frameworks are critically analyzed to identify recurring morphological dimensions and values, identify gaps and eventually uncovered token properties, and dismiss redundant or misleading definitions.
A classification of classifications
We performed an extensive analysis of both scientific and grey literature. We selected eight token classification frameworks based on their relevance, comprehensiveness, and role of the respective issuer, considered as a proxy of the potential normative impact. For each framework, we analyzed the number of dimensions (i.e., mapping parameters of the token) and the average number of the values that each dimension can assume.
Different authors adopt different approaches in the creation of the respective token classification framework. Some give priority to a multi-dimensional outlook (a higher number of dimensions). In contrast, others limit the description to a small number of parameters, but they dive deep into the detailed alternatives for the selected dimensions (a higher number of values).
It’s important to highlight that the number of dimensions provides a quantification of the multi-dimensionality of the framework, i.e., the various points of view that are concurrently adopted in the token description. The average number of values that each dimension can assume is a proxy of the level of detail of the non-quantitative alternatives available for the representation of the token. Finally, the number of all possible configurations — the product of the number of values under each dimension — represents the overall size of the resulting morphological field.
In general, the size of the morphological field scales faster with the number of dimensions, so multi-dimensionality is to be prioritized over the level of detail, to obtain the most comprehensive framework.
The OverTheBlock Morphological Token Classification Framework
After comparing the GMA-normalized token classification frameworks, we used them also as a starting point to draft our new framework.
As a starting point, we thoroughly and critically analyzed the cumulative 42 dimensions identified in the eight frameworks. Then we selected the most meaningful ones and filtered a subset of adequate and sufficient dimensions to describe a token altogether. To structure such a selection process, at first, we grouped the dimensions into five significant domains.
- Technology domain, including all the technical characteristics of the token, referring to the level of integration along the technological stack, the blockchain infrastructure, and protocol;
- Behavior domain, including all the inherent functional characteristics of the token, that rule the possible actions that can be performed with the token (capabilities or restrictions);
- Inherent Value domain, including the dimensions that describe the economic value of the token itself and, in particular, how this value is originated, which factors influence it and cause price fluctuations;
- Coordination domain, including all the token dimensions that enable coordination among the actors of the token-based ecosystem;
- Pseudo-archetypes, consisting of token dimensions that anticipate a set of token archetypes, since they implicitly combine different token characteristics.
The subdivision of the dimensions among the domains they belong to was functional to streamline the identification of redundant elements, i.e., superimposable dimensions appearing in different frameworks. Furthermore, this approach allowed us to identify and dismiss pseudo-archetypes, which implicitly summarize a set of token characteristics and, therefore, generate confusion.
Out of the initial 42 dimensions, we selected and kept 11. One further dimension, belonging to the Technology Domain, was added to fill a gap related to the nature of the underlying blockchain, with particular reference to the accessibility to transaction validation, i.e., permissionless vs. permissioned blockchain. Afterwards, we merged the Inherent Value domain with the Coordination one since the dimensions representing the economic value of the token, and its dynamics, have a direct impact on the actors’ behavior. We also reorganized the dimensions and values that define the incentive scheme, grouping them into Incentive Enablers and Incentive Drivers. Finally, we revised the spectrum of values for each dimension, filling the lacks identified and improving the uniformity and consistency of the terms used.
The outcome is the morphological token classification framework presented below.
The OverTheBlock Morphological Token Classification Framework is characterized by 14 dimensions, grouped in 3 domains, and almost 5 million possible configurations, creating an extensive morphological field. As initially stated, the purpose of the token classification framework is to provide guidance in the description of a token, ensuring completeness, consistency, and adequate comparability. You can find a complete and detailed walk-through of the framework in the next issue of the OverTheBlock Tokenomics Series.
The OverTheBlock Morphological Token Classification Framework is characterized by 14 dimensions, grouped in 3 domains, and almost 5 million possible configurations, creating an extensive morphological field.
The Token Classification Framework that we developed it’s not carved in stone. Since tokens and blockchain ecosystems are in continuous evolution, the methodologies that describe them shall be flexible and prone to adapt to the emerging paradigms.
Therefore, if you have any feedback or suggestion, please share your thoughts to progress it further.
Please cite as:
Freni P., Ferro E., Moncada R. (2020), “Tokens Classification: a Morphological Framework”, OverTheBlock Innovation Observatory, https://medium.com/overtheblock/tokens-classification-a-morphological-framework-c131a2cd5230
OverTheBlock is a LINKS Foundation’s initiative carried out by a team of innovation researchers under the directorship of Enrico Ferro. The aim is to promote a broader awareness of the opportunities offered by the advent of exponential technologies in reshaping the way we conduct business and govern society.
We are chain agnostic, value-oriented, and open to discussion.
