TokenSpace: A Conceptual Framework for Cryptographic Asset Taxonomies

Abstract

This work addresses the ongoing lack of legal clarity and inconsistent pronouncements regarding the regulatory status of cryptographic assets by introducing a novel series of classification approaches employing non-binary scoring systems. Novel taxonomies have been constructed based upon multi-level categorical and numerical discrimination methods following design science of information systems best practices. The aim is to provide greater explanatory insight with respect to the nuanced and complex ensemble of attributes which may be exhibited within this sui generis type of objects. The notions of Securityness (S), Moneyness (M) and Commodityness (C) are proposed as candidate meta-characteristics for "TokenSpace": a three-dimensional visual construction of subjective classification approaches towards a coherent and customisable conceptual framework. TokenSpace can be used to make reasoned qualitative and / or quantitative comparisons of asset properties. TokenSpace has more in common with successful prior classification frameworks in other domains and greater development potential using axiomatic, empirical and qualitative approaches than the sorting, clustering, intuitive or naıve categorisation approaches previously employed for cryptographic assets. TokenSpace provides a basis upon which real-time information feeds and predictive analytical tools may be developed in future.

Full text

TokenSpace: A Conceptual Framework for Cryptographic Asset
Taxonomies?
Dr. Wassim Z. Alsindi[0000−0002−6701−0655]
Parallel Industries wassim@pllel.com
www.pllel.com
Abstract This work addresses the ongoing lack of legal clarity and inconsistent pronouncements regard-
ing the regulatory status of cryptographic assets by introducing a novel series of classification approaches
employing non-binary scoring systems. Novel taxonomies have been constructed based upon multi-level
categorical and numerical discrimination methods following design science of information systems best
practices. The aim is to provide greater explanatory insight with respect to the nuanced and complex
ensemble of attributes which may be exhibited within this sui generis type of objects. The notions of Secu-
rityness (S), Moneyness (M) and Commodityness (C) are proposed as candidate meta-characteristics for
“TokenSpace”: a three-dimensional visual construction of subjective classification approaches towards a co-
herent and customisable conceptual framework. TokenSpace can be used to make reasoned qualitative and
/ or quantitative comparisons of asset properties. TokenSpace has more in common with successful prior
classification frameworks in other domains and greater development potential using axiomatic, empirical
and qualitative approaches than the sorting, clustering, intuitive or na¨ıve categorisation approaches pre-
viously employed for cryptographic assets. TokenSpace provides a basis upon which real-time information
feeds and predictive analytical tools may be developed in future.
Disclaimer, A Note to the Reader & Contributions The intended purpose of this work is to ad-
dress a series of unclear and complex issues in the regulatory, compliance and legal domains through the
application of a novel conceptual approach to asset classification.
The classification system design choices, scoring outputs and discussion of situational context have been
made in an ad hoc,approximate and subjective manner and do not necessarily correlate to an objective
representation of reality. The author is not a lawyer, regulator or legal professional and has no definitive
opinion on the regulatory or compliance status or consequences of assets being classified with particular
assignations by any territorial or jurisdictional legislature. By reading this document any further you agree
that the author accepts no liability or responsibility for the results outlined below or any discussions aris-
ing thereof. TS10, TSL7 and TSTDX TokenSpace scores are provided for intellectual purposes and the
aforementioned TokenSpaces is an abstract and hypothetical representation based upon the methodologies
developed in this work. If you do not agree to these stipulations, you are not permitted to read this docu-
ment.
Dear reader, the methodologies and conceptual frameworks included in this manuscript are intended to
be useful to legal, regulatory and compliance professionals as well as researchers, investment managers,
asset issuers and token engineers in order to compare and contrast the evolving properties of cryptographic
networks and assets over time. Though the example taxonomies and TokenSpaces included here are largely
based on certain perspectives, the methodology is sufficiently generalisable to be readily repurposable.
Sections 1 and 2 provide introductory background, historical and justification for the development of
cryptographic asset classification approaches. Section 3 outlines design choices and considerations for a
generalised methodology to build TokenSpaces and the exercise applied to the meta-characteristics iden-
tified given the context in sections 1 and 2 to create the TS10 instantiation of TokenSpace. Section 4
contains the complete TS10 TokenSpace taxonomies, outputs, analysis and findings alongside miniature
TSL7 and TSTDX TokenSpace case studies facilitating comparisons of legacy and cryptographic assets
and time-dependence of asset characteristics respectively.
Keywords: Cryptocurrency ·Taxonomy ·Blockchain ·Asset Characterisation
?Last major revision: April 2019. Funded by Parallel Industries. This work is made public using a Creative Commons
BY-NC-SA 4.0 license.

II
TokenSpace Primer The instantiation of TokenSpace presented in this work may be considered by anal-
ogy with our own spatio-temporal conception of reality, consisting of a three-dimensional space delineated
(for convenience and visual clarity) by orthogonal axes S,Mand Cas depicted in Figure A. Assets may
possess a score or range on each axis between 0 and 1 inclusive giving rise to an object inhabiting a region
of TokenSpace described by the (x,y,z) coordinates (C,M,S). Time-dependence of object properties may
also be incorporated to reflect the dynamic nature of cryptocurrency protocol networks and their native
assets, tokens issued atop them and network fragmentations such as ledger forks (see §3.3.6).
S,Mand Ccorrespond to intuitively reasoned assignments of subjective classificatory meta-characteristics
Securityness, Moneyness and Commodityness which together form the basis of TS10 classification methods
(see §4). Each asset’s location in TokenSpace is intended to be derived from a weighted scoring system
based upon taxonomy, typology, intuitive, elicited and / or quantitative methods depending on the choices
and assertions of the user - which may or may not be identical to those proposed in this work.
Figure A: TokenSpace visual impression
Definitions of the proposed meta-characteristics:
S- Securityness. The extent to which an item or instrument exhibits characteristics of a securitised asset.
For the purposes of clarity this meta-characteristic does not refer to how secure (robust / resistant) a
particular network or asset is from adversarial or malicious actions.
M- Moneyness. The extent to which an item or instrument exhibits characteristics of a monetary asset.
C- Commodityness. The extent to which an item or instrument exhibits characteristics of a commoditised
asset.
Example scores for a range of assets are outlined in Tables A, B and C below with graphical presentation
in Figure B. Ideal types are postulated canonical examples of particular asset types and are discussed in
§2.2.
It is the aim of this and future research to provide suggestions for classification approaches and some
examples on how TokenSpace may be utilised to comparatively characterise assets from the perspective
of various ecosystem stakeholders. Time-dependence may also be significant in certain instances and can
be incorporated into this framework by evaluating an asset’s location in TokenSpace at different points in
time and charting asset trajectories.
TokenSpace is expected to be useful to regulators, investors, researchers, token engineers and exchange
operators who may construct their own scoring systems based on these concepts. Careful review of territory-
specific regulatory guidance and judicious consideration of boundary functions for example delineating
“safe”, “marginal ” or “dangerous” likely compliance of assets with respect to particular regulatory regimes
are recommended and an example is presented in Figure C. Parallel Industries has developed hybrid multi-
level hybrid categorical / numerical taxonomies for each meta-characteristic alongside time-dependent
and probability distribution functions for anisotropic score modelling and is available to develop bespoke
TokenSpaces for clients on consulting and contract research bases.

III
Securityness: example scores as of April 2019
Asset SNotes
AAPL 1.00 Ideal type for securitised asset*
XRP 0.75 Supply & node operation highly concentrated, no validation reward,
missing ledger history
DAO 0.90 Collective investment vehicle, capital risked
BTC 0.09 Leaderless, permissionless
ETH (2015) 0.75 Minimal network functionality
ETH (2019) 0.48 “sufficiently decentralised”**
GOLD (metal) 0.00 Ideal type for non-securitised asset
SOY (beans) 0.00 Ideal type for non-securitised asset
USD 0.20 Reliance on faith in fiscal prudence of US Government & Federal Re-
serve
*see Bailey §2.1, **see Hinman summer 2018 comments §1.3.3
Moneyness: example scores as of April 2019
Asset MNotes
AAPL 0.05 Approaching ideal type of non-monetary asset, limited utility as MoE
XRP 0.10 Used as regulatory arbitrage vehicle and speculative asset with limited
utility. Central parties can censor
GOLD (metal) 0.40* Non-standardised, prone to dilution, necessitates verification of mass
and purity
GOLD coins (pre-
state minting)
0.50* Dilution via clipping
GOLD coins
(state mint)
0.60* Improved anti-counterfeiting measures
SOY beans 0.05 MoE restricted to barter, consumption or use as underlying for a deriva-
tive instrument
CHF 0.80 Approaching ideal type of modern fiat currency
USD 0.70 Inflationary, with supply debasement (Triffin dilemma)
GBP 0.50 Post-reserve currency decline, Brexit uncertainty
BTC 0.41** Post-bootstrap uncertainty
ETH (2015) 0.03***
ETH (2019) 0.22*** Not intended to be a monetary asset but has become an MoE and UofA
in some circumstances
*Loss of Mover time due to digitalisation / simulacrisation of money (see §1.3.1)
*Uncertainty of post-bootstrap phase network security incentives
**Uncertainty of post-bootstrap phase network security incentives
***Uncertain monetary policy, central influence, technical debt & future viability
Commodityness: example scores as of April 2019
Asset CNotes
AAPL 0.00 Ideal type of a non-commoditised asset
XRP 0.18 Censorability of payments and supply concentration among insiders too
great to be freely tradeable
DAO 0.45 Hybrid of security and commodity
BTC 0.68 Ideal type of a digital commoditised asset
ETH (2015) 0.10 Minimal network functionality
ETH (2019) 0.42 Used as a digital utility for token sales and persistent scripts
GOLD (metal) 0.95 Ideal type of a material commoditised non-consumable asset
GOLD (coins) 0.80 Purity assessment and faith in provenance
SOY beans 1.00 Ideal type of a material commoditised consumable asset
USD (pre-1971) 0.55 Proxy for precious metals, state backed
USD 0.25 Loss of gold peg, debasement of supply
*Increase of Cover time due to functionality and adoption (see §1.3.2)
Tables A, B and C: Example TokenSpace meta-characteristic scores listed to two decimal places

IV
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Commodityness
Moneyness
Securityness
BTC
ETH
DAO
XRP USD
AAPL
GOLD
Figure B: Example of objects inhabiting a TokenSpace
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Commodityness
Moneyness
Securityness
Figure C: Regulatory / compliance boundary visualised in TokenSpace. Arbitrary polynomial for illustrative purposes

Table of Contents
TokenSpace: A Conceptual Framework for Cryptographic Asset Taxonomies ......................... I
Dr. Wassim Z. Alsindi
Sections
1 Introduction & Historical Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Necessity for the Work, Regulatory Opacity & Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Hybrid Character of Cryptocurrency Networks & Assets, the Complex Provenance & Nature
ofDecentralisation ................................................................... 2
1.3 Legal, Economic & Regulatory Characteristics of Cryptographic & Legacy Assets . . . . . . . . . . . . . 3
1.3.1 Nature of Money Throughout the Ages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3.2 What Makes a Good Become Commoditised? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3.3 Regulating Securitised Asset Issuance in a Post-Howey Paradigm . . . . . . . . . . . . . . . . . . . . . 6
1.3.4 Legacy Assets Exhibiting Hybrid Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Classification Approaches & Design Science of Information Systems. . . . . . . . . . . . . . . . 9
2.1 Definitions of Terms Within Paradigms of Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Philosophy of Design Science & Classification Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Selected Examples of Taxonomy & Typology Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Recent Approaches to Classifying Monetary & Cryptographic Assets . . . . . . . . . . . . . . . . . . . . . . . . 15
3 Designing TokenSpace: A Conceptual Framework for Cryptographic Asset Taxonomies . . . . . . 18
3.1 Introduction & Problem Statement of Taxonomy Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 Construction of the TokenSpace Framework: Components & Methodology . . . . . . . . . . . . . . . . . . . 18
3.2.1 Building Robust Taxonomies based on Information Systems Best Practices . . . . . . . . . . . . . 18
3.2.2 Three Conceptual-to-Empirical Approaches to Short-Listing Taxonomy Dimensions &
Characteristics ................................................................. 20
3.3 Design Choices & Justifications for TokenSpace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.1 TokenSpace as a Conceptual Representation of Spatio-Temporal Reality . . . . . . . . . . . . . . . 22
3.3.2 DefiningBoundaries............................................................. 22
3.3.3 Dimensionality&Clarity ........................................................ 22
3.3.4 Categorical & Numerical Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3.5 Score Modifiers & Weightings for Taxonomy Characteristics & Dimensions . . . . . . . . . . . . . 24
3.3.6 Time-Dependence............................................................... 24
3.3.7 BoundaryFunctions............................................................. 25
3.3.8 Non-point & Anisotropic Asset Locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4 Creating a TokenSpace: TS10 .................................. 31
4.1 Iterative Construction of Taxonomies, Indices & Score Modifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2 Placing Assets in TS10 ............................................................... 31
4.3 Cluster Analysis & Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.4 TSL7 : Using TokenSpace to Compare Cryptographic & Legacy Assets . . . . . . . . . . . . . . . . . . . . . . 37
4.5 TSTDX : Time-Dependence of Selected Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5 Discussion & Considerations Informing Further Development of TokenSpace . . . . . . . . . . . 43
6 Acknowledgements....................................... 45

VI

1
1 Introduction & Historical Review
1.1 Necessity for the Work, Regulatory Opacity & Uncertainty
Open permissionless systems’ architecture, functionality and behaviour are radically different to legacy finance
structures and there are numerous unanswered questions as to how best to integrate the two, if they are even
meaningfully compatible at all. The canonical example of the present day disconnect between traditional finance
and crypto-finance is without doubt the explosion in disintermediated early-stage venture fundraising through
non-equity cryptographic token issuance known popularly as Initial Coin Offerings (ICOs). Key drivers for this
proliferation are combination of a lack of political, legislative and economic clarity at the nation-state level with
respect to cryptographic assets (cryptoassets), enabling regulatory arbitrage using these nascent “decentralised”
technologies [1]. For the purposes of this work cryptoassets are considered to be token-based cryptoeconomic
primitives typically issued in trust-minimised distributed environments.
Surrounding these peer-to-peer (P2P) networks and token issuers are a sprawling industry of service providers
such as exchanges, public relations advisors, crowdsale consultants, mining operations, crypto-lawyers, wallet
hardware and software providers, asset custody services and merchants accepting cryptocurrency which collec-
tively bear resemblance to a wild distributed boomtown facilitated by the borderless nature of the technology
enabling sophisticated forms of regulatory arbitrage such as dynamic jurisdiction shopping as well as other less
savoury practices. Regulators and lawmakers have yet to converge upon a coherent legal basis upon which to
attempt to regulate cryptographic assets, and due to variations between jurisdictions a global game of regulatory
arbitrage in extremis has been taking place in numerous locations. Implementing regulation is proving to be
far from straightforward, not least because the very act of doing so would likely trigger so-called KYC factional
disintegration events as is currently in danger of being experienced in the Tezos ecosystem [2].
Since 2008, the scope of capability for financial technology has broadened significantly. Indeed for value-
oriented protocol networks and associated applications of cryptography the landscape has changed unrecognis-
ably. Bitcoin has heralded the instantiation of a new paradigm of open-source, leaderless and permissionless
value transfer without rent-seeking intermediaries, giving rise to a realised vision of Friedrich Hayek’s Prize
in Economic Sciences in Memory of Alfred Nobel winning exploratory swathe encapsulated in “The Dena-
tionalisation of Money” [3]. This optimistic future is resplendent with sunny uplands of individual freedom,
monetary sovereignty and empowerment at the expense of figuratively or literally bankrupt legacy finance,
wealth management and banking institutions. Bitcoin comprises an effectively dispersed leaderless network,
inventively combining a series of technological elements taken from applied cryptography and distributed sys-
tems. These are chiefly public-key cryptography to secure wallets and transactions through digital signatures,
a thermodynamic solution to the double-spend distributed consensus problem incorporating game theoretical
elements - proof-of-work (PoW) and Nakamoto Consensus respectively and strengthening of the linked list data
architecture. These elements are brought together to create a novel type of pervasive, high-assurance data struc-
ture with desirable tamper-resistant characteristics as a basis for the robust implementation of a triple-entry
financial ledger possessing formally specified characteristics such as persistence (tamper resistance and / or
evidence), liveness (synchronous messaging requirements placing upper bounds on delivery delay) and dynamic
availability (nodes can join and leave the network at will). In other words, a so-called “blockchain”, “timechain”,
“brickstring” or “timecube” [4].
In the past decade, Bitcoin and related value-oriented protocol networks have proliferated in a manner akin
to the Cambrian Explosion, with myriad permutations of Bitcoin’s characteristic parameters being varied in
order to launch independent but similar networks either with a new genesis block - a codebase fork - or by
continuing the Bitcoin network’s ledger history upon divergence at some specified point in time - a ledger fork
[5]. The same has occurred with cryptocurrency projects built upon novel codebases such as Ethereum, leading
to the proliferation of ostensible families of genealogically-related codebase and ledger forks. As with the pre-
historic Cambrian Explosion, many of these upstart networks and factions do not appear to be created upon
sound foundations and as such their longevity may be particularly influenced by the equivalent of an incoming
regulatory Ice Age. This rapid expansion in complexity and diversity of cryptographic assets poses a series of
challenges for protocol developers, token issuers, cryptocurrency researchers, legal professionals and lawmakers
attempting to navigate nation-state regulation of these items. Due to the borderless nature of the technology in
question and many grey areas in the nature, usage and function of these networks and assets, the aforementioned
prospect of regulatory arbitrage with multiple mechanisms is being widely leveraged by token issuers, exchange
operators and even nation-states attempting to entice digital enterprise to their territory.
Certain countries have moved quickly to increase their attractiveness to those engaging in what has become
a dynamic jurisdictional shopping competition with particular cases of note currently including Malta, Singa-
pore, Hong Kong, Vanuatu, Puerto Rico, Mauritius, Panama, Bermuda, Belarus, Georgia and Britain alongside
associated Crown territories such as Gibraltar, Isle of Man and the British Virgin Islands.
Indeed Malta has taken to using the epithet “Blockchain Island ” to further attract cryptocurrency related
industry participants, promising attractive taxation and banking arrangements to many billion-dollar valuation

2
companies which were otherwise itinerantly nation-hopping - with highest profile example being the leading
global cryptoasset exchange Binance. The act of facilitating this effective safe harbour for Binance and fellow
Asian exchange OKEx had an overnight double-digit impact on the GDP of Malta with the action doubling as a
high-visibility signal that Malta was welcoming to businesses struggling with onerous regulatory and compliance
requirements in their existing domiciles [6].
1.2 Hybrid Character of Cryptocurrency Networks & Assets, the Complex Provenance &
Nature of Decentralisation
With reference to well established classes or types of legacy assets - such as precious metals, stocks, bonds
and derivatives - that exhibit fairly clear similarities and differences, cryptoassets appear to be less well-defined
by comparison to individual legacy asset classes and may be considered sui generis, without close historical
counterparts. They frequently but non-exhaustively exhibit hybridised properties being part commoditised
bearer asset, part monetary payments medium and network, with the possibility of commodity-like intrinsic
functionality - in the sense of a so-called utility token - or security-like on-chain cashflows from more recent
alternative extensions to base protocol blueprints such as masternodes or staking.
A complicating factor is that there may be different regulatory agencies within a jurisdiction that cover
specific subsets of financial assets - e.g. in the United States both the Securities & Exchange Commission
(SEC) and Commodities & Futures Trading Commission (CFTC) agencies claim primacy over cryptocurrencies
and related products - therefore a BTC Futures contract would be regulated in the US by the CFTC whilst
a Bitcoin Exchange-Traded Fund (ETF) or an ICO startup might be regulated by the SEC or both SEC and
CFTC. This is without even considering overlapping purview with the Financial Crimes Enforcement Network
(FINCEN) or other US government agencies. This may well lead to a attention bias or Rorschach test scenario,
where regulatory officials with specific remits may be inclined to see specific aspects of cryptoassets as being
the leading traits and therefore claim the dominant regulatory purview falls within their domain.
In particular the complex and multi-faceted meaning and provenance of decentralisation is far from trivial
to characterise, with its influence and impact on the security-like properties of an asset challenging to elucidate.
As a phrase originally coined by de Toqueville as an antonym to the centralisation of state power before and
after the French Revolution, a precise definition of the term decentralisation in the technological context would
greatly reduce the lack of linguistic precision routinely encountered in the context of tokenised networks and
assets [7]. A number of approaches to characterise decentralisation as a meaningful or even quantifiable metric
have been made, with varying insights and degrees of success [8, 9]. In the opinion of the author decentralisation
is an emergent, non-binary characteristic of P2P distributed networks which is contributed to by an ensemble of
factors and is commonly found alongside several other similarly difficult to parametrise characteristics. Examples
of these are immutability, defined here as persistence of the canonical transaction set from which the ledger
is constructed, permissionlessness which refers to the lack of prevention at the social layer of any network
participant from transacting and censorship-resistance, taken here to correspond to the inability of third parties
to prevent network participants from transacting using actions at the protocol layer [4].
A helpful framework for the rationalisation of phenomena in cryptocurrency networks is to coarsely consider
the entire network and ecosystem as a “stack of layers” as is commonly done with computational networks
such as the Open Systems Interconnection model [10]. An example delineation of the cryptocurrency network
meta-stack has been proposed by Alsindi & Breen following work by Buterin and this is incorporated in Table
1. [4, 11].
Social / Political All human decision making and interests arising from the chief stakeholder groups of a network.
Typically developers, users, miners, validators and businesses.
Monetary Transactions, addresses, tokenised incentives and / or monetary issuance (via for example proof-
of-work), emergent economic characteristics arising from human and autonomous agents employ-
ing a P2P monetary network as a value transfer mechanism. (e.g. M1, M2, stock-to-flow, price
inelasticity of supply).
Protocol Cryptographic primitives, data structures employed, protocol specifications, nodes implementing
the network consensus rules and P2P network messaging behaviour.
Logical /
Architectural
Is the data itself stored in a highly redundant and / or replicated manner. Does the network
rely overly on centralised backbone infrastructure?
Table 1: Facets of Decentralisation [4, 11]

3
With this conceptual model in mind, it could be said that immutability is an attribute primarily observed
at the protocol layer - upon which the monetary layer depends for persistence - and censorship-resistance is
primarily observed at the monetary and social / political layers. Similarly, the word decentralisation could be
taken to have different meanings when considering the various layers in question. Protocol decentralisation could
refer to distribution of nodes and other incentivised stakeholders such as miners and stakers that undertake
transactions and / or block creation and validation activities. Monetary decentralisation could be described
by the distribution of the supply of the asset, which the Gini coefficient attempts to encapsulate [8]. Social
decentralisation could be related to the decision-making (or governance) process of a network, and whether
some subset of stakeholder constituents are able to exert undue degrees of explicit or implicit influence over a
network’s outcomes.
1.3 Legal, Economic & Regulatory Characteristics of Cryptographic & Legacy Assets
It is instructive to revisit historical descriptions and characterisations of legacy asset classes - in this case moneys,
commodities and securities - to understand how prior classificatory and ontological approaches developed and
how these might be integrated into novel subjective conceptual frameworks such as TokenSpace (§3).
1.3.1 Nature of Money Throughout the Ages Scholars in the domains of natural philosophy, law and eco-
nomics have taken varying approaches to the assessment of properties of monetary assets and objects throughout
history. Aristotle may be regarded as the first to seriously attempt an informed characterisation of the attributes
which a monetary good may exhibit in the 4th century BC, by listing the most crucial properties as durability,
fungibility,transportability and intrinsic value [12]. Jevons introduced the notions of three principal functions
which a monetary good fulfils in the 19th century AD, characterising them as “store of value ” (SoV), “medium
of exchange” (MoE) and “unit of account” (UofA) and these definitions are employed often by issuers of crypto-
graphic assets [13]. United States Federal Reserve economist Kocherlakota defined “money as memory” in 1996
[14], in the sense that money performs a function of providing a structured collective memory which facilitates
expedient verification of the canonical state of the record kept in such a system, be they high assurance digital
data structures, paper money, commodity money with specie such as gold & silver coins, African glass beads or
even giant millstones employed by stone-age Pacific islanders such as the Rai stones on Yap island [15].
As much as 20 years ago, economists at the IMF characterised an ostensible trade-off in the attainable
control of economic properties of Central Bank Digital Currencies (CDBCs), which may be thought of as digital
currencies not necessarily intended to circulate publically but rather utilised to perform wholesale functions such
as inter-bank settlement and clearing. Stone et al. determined that it would not be possible for a central bank
to have control of monetary supply issuance, a free-floating exchange rate against other assets and a centrally-
controlled interest rate [16]. Similarly, the Triffin dilemma raises the prospect of a perpetual fiscal instability
within any nation state that issues a currency which is deemed to the be the global reserve currency of that
time and indirectly suggests that the ideal solution would correspond to the Hayekian vision of a global reserve
money distinct from state-issued currency [3]. By virtue of a currency becoming the de facto global reserve, other
nations are obliged to hold substantial amounts for international trade and settlement. Therefore an enlarged
supply of currency is required to satiate foreign demand and trade deficits are the usual consequence, setting
up a dissonance between domestic and international economic priorities.
An interesting instance of the multi-faceted nature of moneys can be seen in the changing nature of fiat
currencies as they morphed from being fully redeemable for underlying assets such as precious metals to being
primarily instruments reflecting the faith in fiscal management of nation-state governments, monetary issuers
and / or central banks. Prior to the 16th century, the British currency (Pounds Sterling) was little more than
a UofA, being equivalent - first literally and then via redemption - for 454 grams of 92.5% purity silver. As the
Crown and later Bank of England loosened this peg, the necessity for the currency to become a SoV itself arose.
Indeed since the breaking of this peg, GBP has lost over 99% of its value versus silver, which itself is considered
to currently be in the depths of a severe bear market having lost approximately two thirds of its exchange price
versus USD since its peak in 2011.
In the months prior to the release of the Bitcoin whitepaper [17], Chung took a novel twin legalistic and post-
modern philosophical approach informed by absurdist existential thinker and radical monetary theorist Jean
Baudrillard with respect to the nature of money [18]. Chung asserts that Baudrillard’s notion of a simulacrum - a
simulation or abstracted representation of the real - is useful in understanding the development of what societies
adopt and consider as money, given the increasing pace of technological advancements over time [19]. The
progression from direct use of commodity moneys of varying quality such as locally rare seashells, glass beads
or precious metals to minted coins and gold-backed paper money issued either as proxy for the underlying or as
a faith-based instrument backed by governments demonstrated this simulacrisation. The recent development of
na¨ıvely digitised paper fiat instruments such as internet banking and payment intermediaries such as PayPal and
finally natively digital programmable trust-minimised P2P monetary networks such as Bitcoin lend credence to

4
the notion that as digital technology increasingly pervades all aspects of modern human society, the monetary
goods which such a society will accept and use for the exchange of goods and services will also follow a similar
trend. It is reasonable to suggest that Bitcoin - and similar cryptocurrency networks - instantiate the most
hyperreal monetary simulacrum to date. In Baudrillard’s parlance, the realness of Bitcoin may even surpass
that of original commodity money bearer assets such as gold and silver.
Related to the above concepts are those of the hardness or goodness of money [20]. In essence, a good or
hard money is one which retains its value and usefulness over time, chiefly due to judicious choice of monetary
material in question. This may be rationalised in terms of the characteristic of price elasticity of supply [21]
which may be thought of as a measure of the marketplace’s supply-side response to the increased price of a
good. Failed local currencies such as glass beads, fiat currencies and na¨ıvely digitised government moneys differ
widely from scare assets such gold and bitcoin in this respect. Glass beads were used in many parts of Africa as
a monetary asset as they were locally rare, however European explorers making early expeditions throughout
the continent quickly realised the inter-continental opportunities available for exploitation and rapidly inflated
the local supply of the beads, acquiring much of the local wealth, engendering human slavery and causing
rampant inflation of goods prices as accounted for in the local monetary asset - the so-called Cantillon Effect
[22]. Unbacked “fractional reserve” fiat money also exhibits shortcomings here, in that it is trivial for a central
issuer to inflate currency supply with or without this necessarily becoming apparent to holders of the currency
until inflation arises throughout the wider economy.
Gold has some price elasticity of supply insofar as extractive mining necessitates significant capital resources,
environmental distress and capital to realise highly purified metal, however should the price of gold double
overnight it is reasonable to suppose that the associated motivations and incentives will facilitate a greater
degree of exploration and extraction of the metal, thereby increasing its supply. Bitcoin is ostensibly the most
supply-inelastic monetary good observed to date on account of its algorithmically determined disinflationary
supply issuance schedule fixed at the genesis of the network with very little prospect for change. Every 144000
blocks (ca. 4 years) the subsidy awarded to a mining participant who solves a cryptographic puzzle halves
in amount, having started at 50 bitcoin (BTC) per block and currently comprising 12.5 BTC per block. The
issuance schedule is completely independent of any crossrate valuation of BTC in some other unit of account,
and therefore the supply is completely inelastic with reference to internal network parameters (block height and
number of BTC issued).
There have been numerous discussions that due to the long window of mining difficulty readjustment - which
recalibrates the likelihood of a miner finding a valid block to satisfy Bitcoin’s consensus rules every 2016 blocks
(approximately twice a month) - an advancing BTC-fiat crossrate may incentivise the deployment of further
fiat-sequestered computational resource onto the network thereby decreasing the average inter-block issuance
times and by extension increasing the effective supply as measured by calendar time rather than internal network
time (block height). The long-term average of inter-block time in Bitcoin is approximately 9.5 minutes over 10
years to date, consistent with this notion as the vast majority of difficulty adjustments are increases, reflecting
the general trend of increasing computational resource directed to the network. Creator of Bitcoin progenitor Bit
Gold Nick Szabo encapsulated this desirable nature of good money as “unforgeable costliness” [23] as regards
the asymmetry between the difficulty of asset replication, dilution, reverse-engineering and so on versus facile
verification of authenticity. Indeed this takes the notion of price inelasticity of supply to its logical conclusion,
especially with reference to natively digital assets for which solving the issues around double-spending are
non-trivial.
More recently, work by Gogerty and Johnson has explored Network Capital valuation approaches. By con-
sidering all monetary systems as protocols, the notion of potential future transaction networks known as trans-
actomes is proposed as a powerful concept [24]. As such, the transaction graph of a monetary protocol network
moves from an objectively known and understood status in the past, to an intersubjective transient state in the
present and perceived subjective status in the future. This model may offer an improved perspective of network
effects as a monetary protocol expands in scale as compared to Metcalfe’s Law [25], proposing that the rate of
change of network utility (dN/dt) is a higher quality heuristic than network utility itself (N). Table 2 briefly
outlines relevant aspects of this model.
P = Max[R, (N+S)]
where P = Price, R = Redemption utility, N = Network utility, S = Speculative utility
An important consideration when discussing money is the breadth of the protocol in question. As crypto-
graphic assets are digital bearer objects, there is little discussion or development of instruments of debt, credit,
re-hypothecation and so on atop cryptoassets to date in comparison to traditional assets, though the Ethereum-
oriented ”DeFi” movemement is currently addressing this. This makes them objectively narrow moneys or
currencies. Bearing this in mind is helpful when considering the current monetary characteristics of Bitcoin in

5
Price Price is only known at the completion of a transaction, therefore the equation aims to address
this uncertainly through the use of the additional terms.
Redemption utility Redeeming asset-backed currency for the underlying collateral, consuming / transforming goods
such as oil or ether, eating fish or corn.
Network utility The expected value to be realised from transaction with a network of economic actors who would
willingly accept that asset as money. The transactome network corresponds to the expected set
of agents willing to accept an asset as money in a transaction at some point in the future within
the time domain in question.
Speculative utility As the “true” value of a good is not known until the moment of transaction, this term allows
for sentiment and supply / demand considerations to affect the market-determined price over
time.
Table 2: Aspects of Gogerty & Johnson’s Network Capital valuation approach [24]
comparison to legacy moneys such as gold and US Dollars. At present, there is little “financial engineering” oc-
curring using the base currency as collateral, which in tandem with no underlying and therefore no redemption
utility, limited but improving network utility and high speculative utility the case can readily be made that
BTC is currently a reasonable currency but somewhat of a poor money with the potential to become a better
one in the future.
1.3.2 What Makes a Good Become Commoditised? In a general sense, a commodity is a commercial
good that becomes standardised and possesses a sufficiently developed market that it may be considered largely
interchangeable with another like good - in other terms, highly fungible. In a value transfer sense, fungibility
and liquidity are key drivers of commoditisation and a healthy future prospect to remain so. It is instructive
to distinguish between time-sensitive (consumables / perishables) and ambient / transformable commodities
which are time-insensitive as to their usefulness or delivery value. In the cryptoasset domain, the concept of
usefulness or utility maps - at least coarsely - onto the generalised notion of a commodity. How useful a token
is depends on the demand to hold or use it, and how necessary it is to engage in a worthwhile activity such
as private transaction or access to a decentralised service built atop a blockchain-architected protocol network.
Burniske and Tatar’s nomenclature of cryptocommodities does seem to be apt and it is reasonable to presume
significant levels of utility for BTC, Ether (ETH) and some other PoW-based protocol tokens such as Ethereum
Classic (ETC), Decred (DCR) and Monero (XMR) [26]. Analogies have historically been drawn between BTC
and digital gold with respect to rarity, durability and mining. Likewise, ETH is often thought of as digital oil as
it is used as gas to pay for computation in the Ethereum Virtual Machine (EVM) which executes the network’s
persistent scripts commonly known as “smart contracts”.
A handful of countries remain that still use something resembling commodity money, even in an abstract
sense with indirect central bank backing. Mongolia has a significant level of precious metals backing implied
by the ongoing extractive resource mining boom, whilst Lebanon has surprisingly high reserves given its level
of paper debt. It has been remarked that globalist economic policies as leveraged by transnational financial
organisations such as the International Monetary Fund (IMF) and the World Bank are leading contributors
to the decline of nation-state gold reserves, as “rescue packages” given to countries with large current account
deficits often involve “liberation” of sequestered commodity reserves. [27]. In the context of a digital monetary
network, extent of intrinsic utility or commodityness is also related to unforgeable costliness as the supply
of newly mined bitcoin is strictly algorithmically controlled with ever-decreasing supply inflation with mining
subsidy attenuating stepwise as the 21 million BTC supply limit is approached. Commodity money was pervasive
and long-lived in human society because it held its value well against reference items, as an effective store of
value due to limited supply increases. Since the collapse of the Bretton-Woods agreement in 1971 and the
subsequent abandonment of an explicit USD gold standard, fiat money has been diverging from its commodity
backed roots and losing utility as backing becomes primarily based on faith in the financial management of a
territory’s ruling regime [28].
In some ways the disinflationary hard cap supply maximum concept does seem to echo the difficulty of
prospecting for rare physical minerals and materials, versus an uncapped supply philosophy which intends to
maintain transactional utility and lack of friction typically with a constant or declining inflation rate. Some
privacy-oriented cryptocurrency networks have shielded token pools within their networks for which the supply
is not knowable without breaking the entire cryptographic scheme of the network protocol. Therefore the not
uncommon “inflation bugs” which appear even in mature and well-tested codebases such as the Bitcoin Core
client and Zcash which were notoriously used to counterfeit large amounts of coins on Bytecoin (a progenitor of
Monero) and Bitcoin Private (a derivative of Zcash) may be abused to debase monetary supply without wide
knowledge of clandestine currency issuance [29, 30, 31].

6
1.3.3 Regulating Securitised Asset Issuance in a Post-Howey Paradigm What do orange groves and
golf courses have to do with BTC, ETH, and TheDAO tokens?
A detailed history of securities laws and regulation in the USA, UK and other relevant jurisdictions is beyond
the scope of this text and therefore the reader is invited to peruse the referenced works on these matters [32,
33, 34, 35], whilst a brief overview primarily based on American precedent and events will follow. The nominal
definition of a security or a securitised asset is a fungible, tradeable product which constitutes an agreement
between issuer and purchaser. A security such as an equity - for example a share or stock - or a debt-based
instrument such as a bond may further formalise the right to claim future proceeds, cashflows or other outcomes
arising from partial ownership of an underlying, securitised asset [36]. The act of issuing a security agreement
based upon an underlying asset is known as securitisation.
Three landmark rulings in the United States established a great deal of precedence which is still employed
in the present day to legislate and regulate securities issuance and offerings using the Securities Exchange Acts
of 1933 & 1934. Given that (for the time being) the US commands a dominant position in global politics and
finance, these are often cited as worldwide benchmarks for financial conduct. The case of The SEC against
WJ Howey (1946) related to a collective investment contract which offered claims on future cash flows arising
from the proceeds of orange groves in California [37]. Whereas the trees and oranges are clearly not securities
themselves, the investment contract was found to be consistent with that of a security agreement as the contract
established several key aspects within the relationship between contract issuer and investment participant: claims
on future proceeds and / or expectation of profit, fractional ownership of the underlying asset, a person or team
who are relied upon to maximise shareholder returns and voting control / influence over the outcome of outputs
from the underlying. The case involving Silver Hills Golf Club (1961) is also an oft-cited precedent as the
fractional ownership offered by collective investment into a golf course constituted the allocation of risk capital
with the expectation of capital growth of the principal in addition to any cashflows realised [38]. In the case of
Reves versus Ernst & Young (1990), The US Supreme Court adopted a “family resemblance” test to determine
whether a particular financial agreement type known as a note is a security or not by comparison with existing
assignments of security status of an asset. Key attributes were found to be motivations of seller and buyer,
the plan of distribution of the instrument, reasonable expectations of investors and the presence of alternative
regulatory regime which would lead to a lowering of investment risk [39].
The Financial Conduct Authority (FCA) in the UK and the SEC in the US have both made pronounce-
ments in 2017 and 2018 that they deem the Bitcoin network, and bitcoin tokens themselves to definitively not be
security-like and therefore not subject to securities regulation. The leaderless, permissionless and decentralised
operation of the network through thermodynamic means to assign block creation privileges and selection of the
canonical ledger history via Nakamoto Consensus is typically cited in rationalisations of these governmental
decisions [40, 41]. The meaningfulness of statements justified using these characteristics do suffer from impre-
cision of definitions (for example not specifying network layers or specific stakeholder constituencies), lack of
comprehension of detailed function of a permissionless network, lack of familiarity with open-source software
(OSS) development workflows and an ostensible mischaracterisation of the techniques of obfuscation of implicit
extra-protocol power structures and diversionary nature of what has become known as decentralisation theatre
[43]. This finding is in keeping with the SEC’s existing Howey / Silver Hills regulatory paradigm as Bitcoin offers
none of the characteristics of leadership, control, cashflows, collectively risked capital or expectation of profit
specifically when considering the native token as its own unit of account. Framing this another way, despite
the volatile fluctuations of the price of BTC in another unit of account one bitcoin is always still equal to one
bitcoin, as with other bearer assets such as the dollar or an ounce of gold - not accounting for demurrage. Con-
sidering the functionality of the Bitcoin network, the current value of one bitcoin may be understood implicitly
as the value of 75 seconds of the computational resource directed at defending the network from thermodynamic
attacks and providing a high probability of assurance that the integrity of the canonical ledger will continue to
be maintained.
Comments made in summer 2018 by SEC official William Hinman gave heavy implication that he deemed the
Ethereum network to have become “sufficiently decentralised” for its native token ether to not be considered a
security, although the token crowdfunding event in 2014 most likely was a securities offering [44]. These comments
ostensibly necessitate two features for a robust present-day classification paradigm: a non-binary framework of
how security-like an asset is; and a time-dependent component to allow for changes as the network and /
or asset matures. This may be justified as follows: the evolving characteristics of proliferating tokenised P2P
networks appear to have a significant bearing on the opinions of senior regulators with respect to the security
status of particular assets, and no objective boundary (or “securityness threshold”) to separate objects on either
side of exists a priori. The discussion regarding security status of bitcoin had previously also been effectively
decided on the poorly defined pseudo-metric of decentralisation which leaves substantial uncertainty over the
perceived bounds at which US securities regulators would be prompted to act and make legal pronouncements.
By implication in a plurality of SEC officials’ public statements (official or personal in nature) was that most
other subsequent token offerings were very likely unregistered securities with particular reference to “TheDAO”
(DAO), a quasi-securitised leaderless “Decentralised Autonomous Organisation” which suffered a catastrophic

7
failure in 2016 following an exploitation of flawed smart contract code [45]. Hinman made further comments in
November 2018 suggesting that any token which has its value predicated on the expectation of returns would
also likely be considered a security which has potentially wide-reaching implications for a large number of assets,
particularly tokens issued by exchanges for the purposes of profit-sharing and / or fee reduction such as Binance
Coin (BNB) [46]. Further guidance was issued in early 2019 which made some clarifications as to the SEC’s
current views but provided little in the way of concrete and actionable advice [47].
The UK’s FCA reported findings from its “Cryptoasset Taskforce” which largely mirrored US policy but
with a somewhat softer tone, focussing on the need to maintain financial stability and consumer protection
with regard to cryptographic asset issuance, trading and offerings [48]. There is still a lack of clarity over policy
specifics and indeed more general government sentiment in the major Asian trading locations Korea, China
and Japan [49, 50]. A current view of the worldwide regulatory status of cryptoassets can be found in the 2019
Edition of Cambridge University Centre for Alternative Finance’s Regulatory Landscape Study [51].
Beginning in 2013 - with early examples such as Mastercoin,Ethereum and MaidSafe - and proliferating
enormously in the midst of the 2017 bull market in cryptoasset markets, Initial Coin Offerings have become syn-
onymous with unsavoury practices and behaviours. ICOs promised the disintermediation and democratisation
of early-stage venture investing, widening participation beyond the typical retinue of Venture Capital, Angels,
Hedge Funds, Family Offices, Trusts and wealthy individuals (collectively referred to as accredited investors)
to technically savvy retail investors who were early adopters of cryptocurrency technology. The nomenclature
bears uneasy and striking resemblance to the long-established securitisation mechanism of conducting an Initial
Public Offering (IPO) whereby a privately held company would become listed on a public stock exchange thus
facilitating a wider distribution of prospective share ownership. Naturally such a mechanism would be subject
to well-established securities laws in the relevant jurisdiction, with sufficient prior case, tort or written law
precedent to guarantee stiff penalties (monetary and / or incarceratory) to disincentivise any potential foul
play or unfair practices such as insider trading or unauthorised issuance of securities. A superficially different
but semantically equivalent term to describe the issuance of a cryptographic token - usually atop an existing
blockchain-architected network platform - is Token Generation Event (TGE), with critics of the phenomena
stating that this is simply a last-gasp attempt by regulatory arbitrage participants to engage in security theatre
so as to avoid nation-state law enforcement, allowing more time to make their exit from projects or territories
coming to realise the complexities of the regulatory situation at hand [52].
In the interests of brevity, a full treatment of observed phenomena and discussion of root causes of the rise
to prominence of ICOs / TGEs is beyond the scope of this text. The reader is directed to referenced literature
for further information [53]. The key issue to date with this movement has been the cavalier attitudes exhibited
by the founders and insiders of such projects. Most token sales to date have suffered from incentive architecture
misalignment in extremis : founders collect unconfiscatable fungible assets at the outset (typically BTC or
ETH) with essentially no conditions or stipulations on project performance or milestones. Shadow marketing
houses and mercenary smart-contract developers pump out misleading promotional materials and unaudited
code predictably leading to an extremely high percentage of outright failure or chronic under-delivery of project
outcomes [54]. Even the attempt by industry participants to self-regulate and create the Simple Agreements for
Future Tokens (SAFT) Framework has not been viewed particularly positively by legal commentators as being
definitively legally compliant in the US [55].
With this tilt of incentives towards short-termism it is no surprise that the ICO space has become a magnet
for a morally dubious get rich quick ethos, with heavy promotion of unrealistic or even magical claims and a
lack of critical counterpoint or technical rebuttal. There has been a wide practice of what the author terms
blockchain first, ask questions later whereby the perceived advantages of temporally-sequenced data structures
are proffered uncritically and without discussion of the trade-offs necessary to achieve the dubiously optimistic
characteristics proffered in marketing material. A combination of magical claims, greed, absent technical acumen
and lack of previous entrepreneurial success constituted the typical unregulated ICO in 2017 and 2018, and
from comments made in December 2017 by SEC Commissioner Jay Clayton, at that time zero ICO projects
had registered their tokens with US regulators as security offerings [56], though this has recently changed [57].
It is common to observe token sales taking place with the exclusion of US investor participation, no doubt due
to the United States’ Federal Government policy of extraterritorial jurisdiction. A selection of ICO enforcement
actions undertaken by the the SEC can be found at the referenced materials, with court proceedings increasing
in incidence in early 2019 [58].
There were numerous high-profile examples of the circumvention of typical investor protections by ICO
fundraises in 2017 and 2018. Responsible for nominally raising tens of billion dollars collectively, the token sales
took place in a situation of network pre-functionality - insofar as the native networks intended to house the
tokens were not active or even close to ready - and token-holders were given no rights or claims in the terms of
agreements between issuer and purchasers. Frequently companies responsible for creating the network software
went to great lengths to explicitly state the lack of rights participants would have on tokens in the future
network, or any functionality that those tokens may or may not have in future. These examples are typical of
the attempted circumvention of responsibilities as asset issuers in order to engage in both jurisdictional and

8
securities arbitrage, as some opted for a binary company-foundation model while others took their operations
to opaque offshore jurisdictions [59, 60].
More recently it has become clear that researchers, lawmakers and / or regulators believe a second type of
regulatory arbitrage to have taken place, which can be thought of as psuedo-desecuritisation or decentralisation
theatre [43]. By carefully attempting to manufacture asset characteristics so as not to resemble those typically
encountered in the traditionally employed definitions of financial product types such as securities, many cryp-
toasset project founders have intended to steer a course around legislation intended to function as consumer
protections for the average investor.
Comment should also be made regarding so-called utility tokens which are typically non-native tokens issued
atop blockchain networks such as Ethereum and are designed to be required for the use of a protocol, thereby
giving their asset a “unique utility” which is hoped to reduce its resemblance to a security. However this utility
token layering model does not appear to have convinced many regulators who seem increasingly suspicious about
the claims of intrinsic usefulness when a more straightforward and lower-friction crypto-economic model would
be to simply use the base network’s native token e.g. ETH. Consideration should also be made to the stability
of any asset issued atop an existing network, as network characteristics and performance may vary widely
over time and economic or vulnerability exploit attacks are an ever-present prospect. This is especially true in
less established networks, which are typically undergoing protocol development with no guarantee of successful
outcomes. Stablecoins are another interesting subset of utility tokens which are designed or intended to have
reduced volatility versus fiat currencies than typical cryptoassets, and are usually engineered to achieve this
by either fully / over-collateralising a two-way peg, algorithmic management or by a seigniorage shares model
where adjustments are made in response to market dynamics by adjusting the circulating supply dynamically.
Basis is an example of a stablecoin which was under development following highly successful fundraising which
has returned capital to investors and ceased work on the project citing regulatory and compliance issues [61]. It
is possible that certain stablecoin issuance and / or price stability mechanisms may make the asset in question
significantly more security-like.
Security Tokens are another relatively novel subset of utility token, though these are explicitly declaring
themselves to be security-like. By following regulatory and compliance procedures in relevant jurisdictions,
security tokens may therefore may give direct rights to part ownership in an enterprise, on-chain cashflows,
voting / governance rights or other securitised agreements for future claims. Iconomi is an example of an existing
ERC20 utility token that is being reissued as a security token [62]. There are questions over the viability of
existing security token models due to the onerous compliance requirements for both issuers and holders [63].
1.3.4 Legacy Assets Exhibiting Hybrid Characteristics Brief mention should be made of legacy assets
that may not fit cleanly into one of the above pure asset classes. Gold coins are an example of commodity-
moneys, albeit their monetary role has diminished over time. Short-dated securitised government debt such as
3-month US Treasury bills may function as reasonable proxies for money and arguably exhibit commodity-like
characteristics as well. To the best of the author’s knowledge few other assets exist which exhibit characteristics
of both commodities and securities exist unless derivatives such as gold futures, mining company stocks and
ETFs are considered to have qualities of the underlying.

9
2 Classification Approaches & Design Science of Information Systems
2.1 Definitions of Terms Within Paradigms of Classification
Without downplaying the enormous contributions of biology, botany, zoology, entomology and related life sci-
ences fields to the development of classification systems derived from the Linnean binomial paradigm, emphasis
will be directed in this work on broader and more recent approaches into the categorisation of objects and
concepts into classes, categories or other types of divisions. It should be noted for clarity that classification is a
general term which may appropriately describe any of a number of approaches to the organisation of domain-
specific knowledge. Within classification, there are several subsets which shall be briefly defined and discussed
for precision moving forwards.
Classification was defined by Bowker & Star, and Bailey:
“The ordering of entities into groups or classes on the basis of their similarity.” [64]
“A spatial, temporal, or spatio-temporal segmentation of the world.” [65]
Such a classification may be approached conceptually, axiomatically, empirically or intuitively. Discrimina-
tion between objects is often univariate (e.g. number of legs an organism possesses to determine a biological
specimen’s phylum, class, order and so on) but may also be multivariate where two or more attributes are
employed to differentiate between objects. One potential criticism of classification methods in general is that
na¨ıve categorisation may be inadequate to appropriately encapsulate the variety or heterogeneity observed in a
set of objects, with issues arising at boundaries of continuous variables or with edge cases.
Aclassification system was described by Bowker & Star and later developed by Nickerson et al. to comprise
the following:
“A set of boxes (metaphorical or literal) into which things can be put to then do some kind of work -
bureaucratic or knowledge production.” [64]
“The abstract groupings or categories into which we can put objects” with the term classification used
“for the concrete result of putting objects into groupings or categories.” [66]
Framework was characterised by Nickerson building upon work by Schwarz et al. as follows:
“a set of assumptions, concepts, values and practices that constitutes a way of understanding the research
within a body of knowledge.” [67]
The term typology is applied correctly to a series of conceptually-derived groupings, often multivariate -
thereby being more discriminatory than simple classification systems - and are predominantly qualitative in
nature. [65, 68]
Taxonomy is the most widely used term to describe classificatory approaches, though from a recent literature
survey it does also appear to be a term which is often used with a lack of precision [66]. A taxonomic system can
be understood as a subset of classification systems as defined above, and a taxonomy itself can be generated and
derived from a taxonomic system. Most literature adopts the term taxonomy for empirically-derived classification
systems in contrast to conceptually-derived typological systems. However it is clear from classification literature
that taxonomy may refer to both empirically and conceptually derived classifications and this broader, modern
usage of the term is employed by this work. A subset of taxonomies employing quantitative classifications are
termed phenetic approaches, which are typically empirically-derived groupings of attribute similarity and are
largely arrived at using statistical and data analytical approaches such as correlation mapping, data clustering
or principal component analysis. Similarly cladistic approaches are akin to historical, deductive, evolutionary or
genealogical inter-relationships of sets of objects. Relevant examples include the fragmentation and proliferation
of Linux codebase / kernel / distribution descendants, upstart networks employing the CryptoNote protocol
codebase and ledger forks of cryptocurrency networks such as Bitcoin [5]. Taxonomy helps researchers study
relationships between objects and concepts [69], and such approaches may further help researchers find voids in
parameter space which may be a result of anomalous emergent characteristics or mismatch between ensembles
of attributes [66].
2.2 Philosophy of Design Science & Classification Approaches
Following Weber [70], Bailey characterised the notions of ideal types and constructed types with reference
to typology and taxonomy development respectively [65]. For the most part typologies conceptually derive an
ideal type (category) which exemplifies the apex (or maximum) of a proposed characteristic whereas taxonomies
develop a constructed type with reference to empirically observed cases which may not necessarily be idealised

10
Term Definitions & Notes
Classification Spatial, temporal or spatio-temporal segmentation of the world
Ordering on the basis of similarity
Classif. System A set of boxes (metaphorical or literal) into which things can be put to
then do some kind of work
A construction for the abstract groupings into which objects can be put
Framework A set of assumptions, concepts, values and practices that constitutes a
way of understanding the research within a body of knowledge
Typology A series of conceptually-derived groupings, can be multivariate and
predominantly qualitative in nature
Taxonomy Empirically or conceptually derived classifications for the elucidation
of relationships between artifacts
Taxon. System A method or process from which a taxonomy may be derived
Cladistic Taxon. Historical, deductive or evolutionary relationships charting the ge-
nealogical inter-relationships of sets of objects
Phenetic Taxon. Empirically derived groupings of attribute similarity, arrived at using
statistical methods
Table 3: Classification terminology [64, 65, 66, 67, 68, 69]
but can be employed as canonical (or most typical) examples. Such a constructed type may subsequently be used
to examine exceptions to the type. Bailey exemplifies this distinction by equating an ideal type to the optimum
or most extreme value in a collection of data, whereas the constructed type may be taken from the mean or
median of a population. In developing a typological system through conceptual or theoretical foundations, the
structure of a typology may be elucidated through deduction or intuition. This approach may be employed to
build multi-layered systems using conceptual, empirical and combinations of elements thereof - termed indicator
/ operational levels. Such a method can be used to transition in either direction between conceptual and empirical
bases for the classification system as classification is iteratively developed. Nickerson et al. summarise Bailey’s
approach:
“A researcher may conceive of a single type and then add dimensions until a satisfactory typology is
reached, in a process known as substruction. Alternatively the researcher could conceptualise an extensive
typology and then eliminate certain dimensions in a process of reduction.” [65, 66]
In contrast to Kuhn’s paradigmatic assessment of the evolution of concepts, Popper’s Three Worlds provides
some philosophical bedrock from which to develop generalised and systematic ontological and / or epistemolog-
ical approaches. The first world corresponds to material and corporeal nature, the second to consciousness and
cognitive states and the third to emergent products and phenomena arising from human social action [71, 72].
Niiniluoto applied this simple classification to the development of classifications themselves and commented:
“Most design science research in engineering adopts a realistic / materialistic ontology whereas action
research accepts more nominalistic, idealistic and constructivistic ontology.” [73]
Materialism attaches primacy to Popper’s first world, idealism to the second and anti-positivistic action
research to the third. Design science and action research do not necessarily have to share ontological and
epistemological bases. Three potential roles for application within information systems were identified: means-
end oriented, interpretive and critical approaches. In terms of design science ethics Niiniluoto comments on
taxonomy as a descriptivistic endeavour:
“Design science research itself implies an ethical change from describing and explaining the state of the
existing world to shaping and changing it.” [73]
Ivari considered the philosophy of design science research itself:
“Regarding epistemology of design science, artifacts of taxonomies without underlying axioms or theories
do not have an intrinsic truth value. It could however be argued that design science is concerned with
pragmatism as a philosophical orientation attempting to bridge science and practical action.” [74]
Methodology of design science rigour is derived from the effective use of prior research (i.e. existing knowl-
edge). Major sources of ideas originate from practical problems, existing artifacts, analogy, metaphor and theory
[75].
Following on from Plato and Aristotle’s notion of essentialism - a characteristic essence of every entity,
concept and material [76] - the epistemology of design science as evinced by taxonomy development until

11
the Industrial Revolution was at least partially informed by a na¨ıve and pre-Darwinian essentialist sensibility.
There is a lack of agreement as to the extent that early classifiers such as Linneaus and Haeckel were complete
essentialists that fully believed that the biosphere was composed of static, time-independent ensembles of living
things. Ivari makes a post-essentialistic statement as follows, highlighting the value of abstract or conceptual
approaches as possible intermediaries in the ontological quest:
“Conceptual knowledge does not have an intrinsic truth value, but is a relevant input for the development
of theories representing forms of descriptive knowledge, which may have a truth value.” [74]
Figure 1: Linnean binomial classification example [77]
2.3 Selected Examples of Taxonomy & Typology Approaches
The scholarly pastime of classifying objects systematically is usually traced back to Carl Linneaus the Swedish
botanist, physician, and zoologist who was active in the 17th century and prepared a number of thorough
approaches to classifying living things including the formalised binomial nomenclature which is still in use for
the naming of species [77, 78]. Prior to this, Aristotle’s Predicamenta laid the conceptual foundations for the
activity of categorising concepts and objects [79]. These were simple inductive approaches which began with
conceptual or empirical inquiry and led in some successful cases to axiomatic reasoning, however given the
paucity of reliable and objective information available at that time, it is entirely understandable that thorough
and concise frameworks did not develop immediately. Oil and coal products trees in the 19th century as shown
in Figure 2 were inspired by this [80, 81]. As the Industrial Revolution was carbon driven, complex organic
materials comprised of myriad constituents principally derived from coal and oil were purified, refined and
processed into a new generation of high performance products. Fortunes were made and lost on classification
accuracy with fractionation and purification of carbon feedstocks yielding a plethora of fuels, dyes, lubricants and
other organic compounds previously undiscovered or unobtainable by chemical synthesis. These classification
approaches were still informed by the natural sciences’ categorical branching hierarchy paradigm and would
be thought of as cladistic taxonomies as the interrelation of objects is directly associated with their incidence,
derivation and provenance.
Hertzprung and Russell employed empirical data from astronomical surveys in the early 20th century and
found that stars could be grouped into families based on their surface temperature and luminosity, affording
insight into their probable future fates. By studying the evolution of thermodynamic, nucleosynthetic and photo-
physical characteristics of stellar objects through these clusters, the Hertzsprung-Russell phenetic taxonomy has

12
Figure 2: Example of a coal products tree [82]
Figure 3: Example of a Hertzsprung-Russell diagram [84]
over time been refined, simplified and developed further into a highly successful visual classification mechanism
with an example shown in Figure 3 [83, 84].
The so-called periodic table of the chemical elements that exists in the present day is an evolution of tax-
onomic approaches initially developed phenomenologically then refined with increasingly meaningful heuristics
as scientific knowledge developed from the 17th century to the present. Newton studied elemental properties
under the auspices of alchemy when, as Master of the British Royal Mint he ostensibly attempted to systematise
approaches to reverse-engineer gold, at that time the most unforgeably scarce material known [85]. Geoffroy
later developed symbolic matrices empirically studying affinities between materials [86]. Lavoisier and Priestley
are credited for the discovery of elemental oxygen and its role in combustion, invalidating incumbent phlogiston
theory and the long-lived mythic notion of an element of fire [87, 88]. D¨obereiner attempted to group materials
based on elemental mass triads in the 1820s, after Dalton’s work lent credence to Democritus’ atomic theory
as depicted in Figure 4 [89]. In the mid-19th century theories developed to bridge the gap between empirical

13
pattern-finding and axiomatic classification of elements on the basis of atomic mass and number, with varying
approaches developed in isolation by de Chancourtois, Newlands, Meyer and Mosey [90, 91, 92, 93].
Figure 4: Dalton’s Notes from Chemical Philosophy, 1808 [94]
Elemental taxonomy progress is well documented and disseminated widely as each physical instantiation
of the periodic table constitutes a snapshot in time given that heavy elements continue to be discovered and
advances in scientific theory further the progress from empirical to axiomatic bases for this taxonomy approach.
As with Kekul´e’s elucidation of the aromatic cyclical structure of the benzene molecular, Mendeleev was thought
to have made the necessary deductive leaps in an ouroborosian dreamtime reverie, perceiving a rotary concept
to be the key inventive step towards a unified chemical ontology [95, 96].
In the late 19th and early 20th century linear (Figure 5) and cyclical (Figure 6) schemas both developed as
classification discriminant improved from ranking of elemental oxides to atomic number and outer shell electron
configuration as determined by permutations of quantum numbers. Circular designs such as Soddy’s have a
greater conceptual and explanatory power than linear ones by dispensing with the need to choose a position for
the empty-shelled noble gases [97]. Moving across the table, outer electron orbital shells are populated according
to thermodynamic principles with quantum mechanical orbital theory providing the geometries and energetic
characteristics of s,p,dand f-type orbital probability density functions (PDFs) [98].
Figure 5: Example of a rare periodic table found at the University of St. Andrew’s, dated approximately to 1885 [99]

14
Figure 6: Soddy’s circular model of elemental classification [99]
In the present day, elemental discoveries continue and systematised classification frameworks exist to explain,
predict, observe and categorise. Periodic tables constitute mature taxonomy approaches predominantly employ-
ing axiomatic reasoning and empirical validation. Indeed this taxonomy format has itself become a memetic
simulacrum, representing and signalling the triumph of scientific traditions, though critical debate as to its abso-
lute veracity continues [100, 101, 102]. The symbolic meaning of form may surpass that of contents as “periodic
tables” of unrelated objects proliferate, with perhaps the most egregious misuse of this term and of taxonomy
itself to date discussed below in §2.4. As with all information systems, the principle of GIGO (Garbage In,
Garbage Out) applies [103].
Figure 7: Brave New Coin visual classification excerpt [104]

15
Figure 8: CryptoCompare visual classification excerpt [105]
2.4 Recent Approaches to Classifying Monetary & Cryptographic Assets
Figure 9: The least useful self-proclaimed “taxonomy” of cryptographic assets to date, by Kirilenko [106]
In contrast to the above historical successes of taxonomy, classification approaches to legacy and crypto-
graphic assets have been rather limited in scope and depth to date. Both transnational banking institutions such
as the Bank for International Settlements (BIS), International Monetary Fund (IMF) and credible commenta-
tors are largely yet to progress beyond somewhat na¨ıve classification methods, which provide little explanatory
power or exhaustiveness of classification [107, 108]. In the current era of regulatory inconsistency and opacity,
a more logical and robust conceptual framework using more considered classification approaches would allow
existing taxonomic, scoring or rating philosophies to be integrated into a more versatile conceptual framework.
Brave New Coin and CryptoCompare are the sources of the most thorough characterisations of cryptographic
assets to date, building upon some of the categorisations and nomenclature employed by Greer, Burniske and
Tatar [26, 110] with illustrations in Figures 7 and 8. In the Burniske / Tatar approach, cryptoassets are con-
sidered to be classified sufficiently by two sets of binomial categorisation: 1) “value-protocol use or not” and 2)
“direct value or not”, “functional value or monetary”. Some unique characteristics of the various attributes were
identified but ultimately this framework approach largely fails to withstand scrutiny of the Nickerson et al. re-
quirements of a valid taxonomy regarding the lack of exhaustiveness, possessing multiple overlapping attributes
and being descriptive rather than explanatory (see §2.1, §2.2 and §3.2) [66]. Some of the classification approaches
contained within both studies would map reasonably well onto the Nickerson et al. construction of a taxonomy

16
Figure 10: BIS money flower (adapted from Bech and Garratt) [108, 109]
problem statement and canonical requirements (see §3.1), though they would be better described as databases
or information repositories of relevant metrics and attributes. Other self-proclaimed cryptoasset classification
attempts fare less well when judged by these criteria. Of particular note is the “taxonomy” produced under
the auspices of a “periodic table of cryptocurrencies” which rather resembles an arbitrary scatterplot with a
polynomial fitting line “connecting” the axes of risk and reward (Figure 9). Such a simple intuitive “clustering”
of cryptographic asset types - if indeed the “data” presented is genuine and has been legitimately been examined
using pseudo-taxonomic approaches - appears to be absolutely devoid of explanatory power [106]. A number of
largely trivial Venn type sorting approaches have been attempted by Bech & Garratt and others for both public
and private legacy moneys. For example the so-called BIS Money flower [108, 109] fails as a useful taxonomy in
the Nickerson et al. sense - being inexhaustive, possessing multiple overlapping attributes, and being primarily
descriptive rather than explanatory.
Figure 11: Classification results of Fridgen et al. [112]
Linear, hierarchical and uni / bivariate sorting approaches including lists, scoring systems and typologies
have been employed with examples such as the SpacesuitX ICO scoring system and Swiss regulator FINMA with
its one-dimensional “utility, payment, asset, hybrid” delineation [111]. CryptoCompare commented in October
2018 that by FINMA’s rationale, over 50% of major cryptographic assets would be classified as securities by
Swiss law [105]. In addition to many informal self-proclaimed taxonomies, the word does seem to be used loosely
enough to be applied to simple lists of phenomena or objects or even a polynomial fitting function overlaid upon
ostensibly arbitrarily placed data with some creative license [106].

17
Figure 12: Clusters in parameter space as observed by Fridgen et al. [112]
The most complete example to date of a strictly valid cryptographic asset taxonomy is from a recent
conference proceedings article by Fridgen et al. entitled Don’t Slip on the ICO and this study employed Nickerson
methodology to arrive at a reasonably useful taxonomy with cluster analysis performed on their desktop research
and expert judgement derived dataset. Figures 11 and 12 contain key findings from this publication [112]. This
work was presented at an EJIS conference in 2018, suggesting that the information systems research domain is
leading the way with robust application of taxonomy design in the domain of cryptographic assets, rather than
attempts arising from within the nascent cryptocurrency research community itself. Worthy mentions also go to
Glaser et al. and Tasca et al. for producing meaningful classificatory work in the related areas of decentralised
consensus mechanisms and blockchain technologies respectively [113, 114].

18
3 Designing TokenSpace: A Conceptual Framework for Cryptographic Asset
Taxonomies
3.1 Introduction & Problem Statement of Taxonomy Development
Much of the taxonomy development methodology developed here is based on the work of Nickerson et al. [66,
115] which developed generalised frameworks for the creation of classifications, taxonomies and typologies in the
domain of design science for information systems. As the purpose of a taxonomy development is to structure and
/ or order knowledge or objects within a specific domain, this allows researchers to understand how concepts are
interrelated and whether there are anisotropies in the relative incidence of ensembles of characteristics within
the group of objects to be classified, if voids or dearths of combinations of characteristics exist within the
possibility space and so on.
Using the methodology of Nickerson et al. with terminology as outlined in §2.1, we can recall the use of the
generalised term taxonomy to refer to conceptual, axiomatic, intuitive, elicited, empirical or hybrid approaches
to classification. Phenetics - numerical taxonomy - may be employed to score or weight branches of a taxonomy
based on quantitative metrics - such as the number or proportion of fully validating nodes in a network - or
to arrive at some initial phenomenological groupings of objects using clustering or other statistical grouping
techniques. It is instructive to state explicitly that though classifications may be a step towards ontology as
evinced by the periodic table of chemical elements in §2.3, the exercise of developing taxonomies themselves is
often conducted with a large degree of intuitive reasoning or ad hoc decision making as the process of developing
component taxa - and the taxonomies they are constructed from - is typically iterative. Using this approach,
Nickerson and coworkers have made a valuable contribution to the systematisation the act of systematising
domain information - in a sense, meta-taxonomy [66, 115].
In the binomial classification paradigm of Linneaus - as applied to biology, botany and zoology - there exists
a hierarchy of categories that the user of the taxonomy must follow in order to classify a living thing as a
member of a taxonomic rank:kingdom,phylum,class,order,family,genus or species [116]. Such approaches
may be considered as cladistic as well (§2.1), in that they are based on geneaology, provenance or ancestry.
Codebase forkonomies - fragmentation maps of OSS codebases, kernels and distributions - are a more modern
example of simple cladistic taxonomies [5].
3.2 Construction of the TokenSpace Framework: Components & Methodology
3.2.1 Building Robust Taxonomies based on Information Systems Best Practices Nickerson and
co-authors describe what can be considered a taxonomy with flat dimensions, arrived at by following the process
outlined in Figure 13 and approach details in Table 4:
A taxonomy T is a set of nDimensions Di(i= 1, ..., n) each consisting of ki(ki>1) mutually exclusive
and collectively exhaustive Characteristics Cij (j= 1, ..., ki) such that each object under consideration has one
and only one Cij for each Di.
T = {Di,i= 1, ..., n|Di={Cij ,j= 1, ..., ki; ki>1}}
Prat et al. later extended this formal definition to allow hierarchical dimensions, so that characteristics may
be grouped into categories, which may themselves be nested in a higher-level category - Nickerson’s dimensions
- as many times as necessary until the highest (root) category is reached which corresponds to the meta-
characteristic in Nickerson’s lexicon [117].
Method sufficiently describes the objects in domain of interest
Considers alternative approaches to tax. dev.: empirical, conceptual, intuitive, elicited, mixed
Reduces possibility of including arbitrary or ad hoc attributes
Can be completed in a reasonable period of time (ending conditions)
Straightforward to apply & “useful”
Table 4: Taxonomy methodology development [115]
Meta-characteristics and characteristics: taxonomy development requires the determination meta-characteristics,
to serve as the basis of choice of characteristics within the taxonomy. Choice of meta-chararacteristic should be
based on the purpose of the taxonomy, and purpose should be based on use. The choice of meta-characteristic
in a taxonomy may be arrived at iteratively, as “themes” of the object characteristics being analysed become

19
Hevner et al (2004, pp. 88–89). Finally, our method adds
the important concept of meta-characteristic that Bailey
does not identify explicitly or implicitly.
Figure 1 shows the method that we propose. Steps in
this figure are numbered for later reference. A step-by-
step explanation follows the figure.
The first step is to identify the meta-characteristic,
which, as discussed previously, is based on the purpose of
the taxonomy and in turn based on the users and their
expected use of the taxonomy. Next, the conditions that
end the process need to be determined. As discussed
previously, there are both objective and subjective ending
conditions. The researcher has a number of objective
conditions that can be applied (Table 2). The subjective
ones are the most difficult to identify and to apply.
Table 3 provides initial guidance but the experience of
the researcher will have an impact on the selection of
subjective conditions. In the case of multiple researchers
developing a taxonomy, various collaborative techni-
ques, including the Delphi method, could be used to
determine these conditions.
After these steps the researcher can begin with either an
empirical approach or a conceptual approach. The choice
of which approach to use depends on the availability
of data about objects under study and the knowledge
of the researcher about the domain of interest. If little
data are available but the researcher has significant
understanding of the domain, then starting with the
conceptual-to-empirical approach would be advised.
On the other hand, if the researcher has little under-
standing of the domain but significant data about the
objects is available, then starting with the empirical-to-
conceptual approach is appropriate. If the researcher has
both significant knowledge of the domain and significant
data available about the objects, then the researcher will
have to use individual judgment to decide which app-
roach is best. In the fourth case, where the researcher has
little knowledge of the domain and little data available,
the researcher should investigate the domain of interest
more before attempting to develop a taxonomy for it.
In subsequent iteration the researcher may choose to use
a different approach in order to view the taxonomy from
a different perspective and possibly gain new insight
about the taxonomy.
In the empirical-to-conceptual approach, the research-
er identifies a subset of objects that he/she wishes
to classify. These objects are likely to be the ones with
which the researcher is most familiar or that are most
easily accessible, possibly through a review of the lite-
rature. The subset could be a random sample, a syste-
matic sample, a convenience sample, or some other type
of sample. Next, the researcher identifies common cha-
racteristics of these objects. The characteristics must
be logical consequences of the meta-characteristic.
Thus, the researcher starts with the meta-characteristic
and identifies characteristics of the objects that follow
from the meta-characteristic. The characteristics must,
however, discriminate among the objects; a characteristic
1. Determine meta-characteristic
No
2. Determine ending conditions
End
3. Approach?
Yes
Empirical-to-conceptual Conceptual-to-empirical
4c. Conceptualize (new)
characteristics and dimensions of objects
5c. Examine objects for these
characteristics and dimensions
6c. Create (revise) taxonomy
4e. Identify (new)
subset of objects
5e. Identify common characteristics
and group objects
6e. Group characteristics into
dimensions to create (revise)
taxonomy
7. Ending conditions met?
Start
Figure 1 The taxonomy development method.
A method for taxonomy development Robert C. Nickerson et al 345
European Journal of Information Systems
Figure 13: Methodology for taxonomy development by Nickerson et al. [115]
apparent or alternatively may be determined via empirical investigation with statistical analysis, intuition or
by pre-existing conceptual design. After the meta-characteristic has been selected, the researcher can proceed
with either conceptual or empirical approaches to reach the first iteration of their proto-taxonomy. The meta-
characteristic should be the most comprehensive characteristic which the taxonomy should differentiate on
the basis of. Characteristics should be logical consequences of the meta-characteristic. Each characteristic that
objects exhibit should follow from the meta-characteristic but also discriminate among the objects.
Dimensions: groupings of characteristics which branch recursively from the meta-characteristics. Approach-
ing conceptually-to-empirically, dimensions are first conceived intuitively or inductively with meta-characteristic
in mind, with characteristics which then follow from the meta-characteristic and are also mutually exclusive and
collectively exhaustive amongst themselves. In an approach of empirical-to-conceptual type, dimensions may be
conceived a priori and later subjected to methodological scrutiny as to their validity. The optimal approach
depends on domain knowledge of researcher and the quality and quantity of empirical data available. Flexibility
in the approach adopted is valuable as further empirical data and / or domain objects to classify may become
available over time.
Ending conditions: using an iterative method there must be specified conditions for the taxonomy devel-
opment process to terminate / complete, as seen in Table 5. When the classification system fits the definition
of a functioning taxonomy that iteration of the taxonomy is complete, for example: characteristics mutually
exclusive and collectively exhaustive, sufficient specificity and dimensionality to adequately characterise and dif-
ferentiate domain objects of interest. That is to say, the taxonomic process can be considered to have completed
satisfactorily when no spurious dimensions or characteristics remain, new objects may be classified without need
for amendment, each object is satisfactorily classified and the activity of classification furthers understanding
of the properties of the class of objects, rather than simply describing them.

20
Concise - no spurious / non-discriminatory dimensions or characteristics
Robust - can withstand new information and objects
Comprehensive - each object should be classified
Extendible - can integrate new objects, dimensions and characteristics
Explanatory not descriptive
Table 5: Ending conditions for useful taxonomies [115, 118]
3.2.2 Three Conceptual-to-Empirical Approaches to Short-Listing Taxonomy Dimensions &
Characteristics Selected dimensions and characteristics for this instantiation of a TokenSpace based on the
legacy asset classes discussed in §1.3 follow. Individual taxonomies have been iteratively constructed using a
conceptual-to-empirical approach for each meta-characteristic. Taxonomy examples, scores and visual represen-
tations are detailed in §4.
Securityness The extent to which an asset or instrument exhibits characteristics of a security.
Proposed contributing factors to an asset’s Securityness score which are candidate attributes for taxonomy
dimensions and characteristics are listed in Table 6.
AExplicit profit-sharing or cost reductions: dividend payouts, token supply destruction
ARights to on-chain cashflows: masternodes, passive income such as staking
BIntentions or assertions of network creators / asset issuers: marketing claims and promises,
pre-functional trading, explicit / implicit
B, C, G Network functionality and / or asset utility: at t= 0 and t= T, network’s native asset or
secondary layer
C, D, E, G Developers: competence / influence, open-source codebase, originality, commit numbers /
concentration, repository control, soft power
D, E, G Nodes / users / economic participants: validation incentives, number and distribution, pooling
/ aggregation, soft power (e.g. UASF)
C, D, E, G Miners / validators: number and distribution, pooling / aggregation, soft power (S2X, hash
battles)
D, E, G Hardware manufacturers: clandestine action, influence, incentive alignment with network
health
C, D, E, G Conspicuous leaders and personalities: CEOs, thought leaders, deified figures, social media
influencers, captured regulators and politicians
C, D, E, G Foundations / insiders: resource control from token sale or self-allocation, seigniorage rights,
influence & supply concentration
D, E, G Other stakeholders, influencers, institutional stakeholders and shadow plutocrats: influence &
supply concentration
C, D, E, G Governance: decision-making & treasury control concentration, asymmetry between issuer
and investor, primacy of human or algorithmic rule, interventionist / non-interventionist.
E, G Infrastructure incentives: nodes, miners / validators, hardware manufacture, supply chain
E, G Seigniorage rights: fair, premine / arbitrary mint, superblocks, masternodes, permissioned
E, G Network topology: centrality, coordinated / permissioned, node aggloremation by number and
geography
B, D Initial issuance mechanism: PoW, costless PoX , costly PoX (e.g. Proof-of-Burn) / arbitrary
mint, premine , snapshot ledger fork, airdrop
B, D Ongoing issuance mechanism: PoW (permissionless entry) / PoX / arbitrary mint
B, G Utility: lack of consumability / intrinsic usefulness / high friction so-called utility tokens
A: Claims on future proceeds — B: Expectation of profit — C: Fractional ownership / Common Enterprise
D: Stakeholders / entities relied upon — E: Voting Control & Influence — F: Risk Capital
G: Post-Howey “Sufficient Decentralisation”
Table 6: Candidate attributes for Securityness

21
Moneyness The extent to which an asset or instrument exhibits characteristics of a monetary good.
Proposed contributing factors to an asset’s Moneyness score which are candidate attributes for taxonomy
dimensions and characteristics are listed in Table 7.
ADurability: resistance to change, degradation, reverse-engineering or retrosynthesis
APortability / Payment Friction: specie retards transportation as narrow money / currency but
protocol layers may be built atop (such as gold IOUs, credit facilities)
ADivisibility / Fungibility / Privacy: in-protocol or extra-protocol (UTXO mixing, transaction
origin obfuscation, forward secrecy, amounts hiding, bullion franking, coin milling and reeding
AIntrinsic Value: global versus local scarcity, price inelasticity of supply, unforgeable costliness,
asymmetry of replication / verification
A, B, D, E Store of Value: scarcity, costliness of replication, durability and change-resistance
A, B, F Medium of Exchange: acceptability as payment method, pervasiveness of protocol, transac-
tomes versus Metcalfe
B, F Unit of Account: pervasiveness & legitimacy of the protocol unit. USD worldwide at retail
and wholesale level. Gold worldwide but less common. Exchange base pair status.
C, D Realness: simulacrisation / memeticism / Schelling point
D, E Hardness: asymmetry of forgery or simulation vs verification, price elasticity of supply, stock-
to-flow ratio
D, E, F Supply dynamics: provable scarcity, global / locally rare, inflation-seigniorage rate, central /
treasury reserve
D, E, F Demand dynamics: Lindy, Veblen, Giffen
FResilience: network security guarantees, stakeholder incentive alignment, honesty assump-
tions, adversary mitigation, change / tamper resistance, permissionlessness, issuance mecha-
nism, native or non-native
FProtocol Breadth: evolution from narrow to broad money, credit as L2, transactomes as leading
indicator of monetary potential, adoption / acceptance as present / lagging indicator
A: After Aristotle — B: After Jevons — C: After Baudrillard — D: After Szabo — E: After Hayek
F: Network Utility & Value
Table 7: Candidate attributes for Moneyness
Commodityness The extent to which an asset or instrument exhibits characteristics of a commodity good.
Proposed contributing factors to an asset’s Commodityness score which are candidate attributes for taxon-
omy dimensions and characteristics are listed in Table 8.
Durability: in- and extra-protocol persistence resistance to change or retrosynthesis, Lindy-type time-
dependent effects.
Resilience: network security & honesty assumptions, key entity and technology risk, stakeholder alignment,
native / non-native asset.
Universality: portability / transaction friction, realness / materiality, protocol breadth, network maturity,
network legitimacy, fungibility in- and extra-protocol
Intrinsic Value: network consensus model, supply and demand dynamics
Usefulness: reason for holding asset, liquidity depth, asset utility (consumability / transformability),
crypto-economic friction
Explicit Incentives: profit / surplus sharing or cost reduction, rights to on-chain cash flows
Table 8: Candidate attributes for Commodityness

22
3.3 Design Choices & Justifications for TokenSpace
3.3.1 TokenSpace as a Conceptual Representation of Spatio-Temporal Reality The instantiation of
a TokenSpace presented in §4 is best thought of as an idealised three-dimensional space within which assets are
placed according to taxonomy-derived “scores”. Each axis maps to a particular meta-characteristic as outlined
in §3.2, “Securityness”, “Moneyness” and “Commodityness”. The TokenSpace presented here may be considered
by analogy with our own spatio-temporal conception of reality, consisting of a three-dimensional space delineated
(for convenience and visual clarity) by orthogonal axes S,Mand Cwith a fourth temporal dimension able to
be incorporated through the “movement” of assets through the space as characteristics evolve over time.
Figure 14: TokenSpace visual impression
Other constructions with different meta-characteristics, boundaries, scoring methods or dimensionality are
possible. TokenSpace is intended to be a flexible conceptual framework designed to output visually comparable
results. Assets may possess a score or range on each axis between 0 and 1 inclusive giving rise to an object
inhabiting a region of TokenSpace described by the (x,y,z) coordinates which in this case map to the meta-
characteristics (C,M,S). Each asset’s location in TokenSpace is intended to be derived from a weighted
scoring system based upon combinations of taxonomy, typology, intuitive, elicited and / or quantitative methods
depending on the choices and assertions of the user - which may or may not be identical to those proposed in
this work.
3.3.2 Defining Boundaries For the purposes of facile comprehension, each axis is bounded between the
values of zero and one. An asset that is determined - using whichever scoring method is chosen by the researcher
- to exhibit none of the properties that are taken to constitute the meta-characteristic would possess a score of
zero. Conversely an asset constituting an ideal or canonical example in the context of Bailey’s definitions (see
§2.1) of the meta-characteristic in question would possess a score of one. Assets may possess any value between
zero and one, although this somewhat coarse approach does not distinguish between “good” or “bad” assets - for
example a 2017 ICO may constitute a very poor investment analogue of a security, and at present TokenSpace
does not distinguish between this asset’s Securityness from a bona fide security such as a legitimately issued
stock or bond. TokenSpace could be extended to occupy a space between negative one and positive one to reflect
the difference in quality of assets in the future.
3.3.3 Dimensionality & Clarity The choice of three meta-characteristics and hence spatial dimensions in
the example instantiation of a TokenSpace constructed in §4 is partially justified by the analysis of traditional
asset classes in §1.3.3 and the apparent link between securities, commodities and moneys with characteristics
of various cryptographic assets. Depending on the needs of the user, a custom TokenSpace could be created
with any number of dimensions but beyond 3 or 4 the visual clarity afforded by the framework diminishes.
Conversely fewer dimensions could be used, or a bespoke TokenSpace could be constructed by iteratively adding
dimensions with scores derived from the usual menu of options with judgement applied as to the dimensionality
which provides the greatest explanatory power. Alternative graphical approaches such as a “radar” diagram
may be helpful in visually comparing higher dimension TokenSpaces, as seen in Figure 15, though awareness
of the so-called Curse of Dimensionality and in particular implications to statistical analysis of populations in

23
sparsely populated high-dimensional Euclidean space [119]. In particular analytical techniques such as k-means
clustering may produce unreliable results in such scenarios.
Figure 15: A radar graphical approach for a hypothetical TokenSpace
Some discussion of the true “orthogonality” of the meta-characteristics employed here is worthwhile. It is
not the belief of the author that these overarching attributes are completely independent from one another, in-
stead the utilisation of orthogonal axes is a conceptual simplification considered acceptable in order to produce
output which can be easily visually discerned by humans. This is in accordance with the aims of producing
a useful classification framework for the subjective comparison of cryptographic asset properties. For instance
Commodityness could be seen as an analogue of utilityness, but so-called utility tokens also frequently exhibit
security-like characteristics. Likewise, commodity-like assets such as Bitcoin and Ethereum are also partially
usable as pseudo-monetary goods. The taxonomies displayed in Tables 10 and 11 show a great deal of common-
ality in the dimensions between Moneyness and Commodityness, with significant differences in the weightings
of scores.
3.3.4 Categorical & Numerical Characteristics For an ideal instantiation of TokenSpace with maximum
explanatory power, a balance should be struck between thorough utilisation of discriminatory attributes whilst
not encumbering the researcher with a classification which is overly burdensome to apply. For the instantiation
of TokenSpace in §4 the optimal balance was found with hybrids of categorical and phenetic taxonomy types.
This design choice - preferring hybrid supra-taxonomies to simpler categorical taxonomies - is justified by the
desired outcome of numerical scores as the output of the classification execution in order to populate asset
locations in the Euclidean 3D space that TokenSpace creates. A particularly useful tool, made use of widely in
the taxonomy examples in §4 is the indexed dimension. Taking the notion of a range-bound score - determined in
any manner deemed acceptable as part of the experiment design process - as a proxy for one or more categorical
discriminants allows significant simplification of the application of taxonomic systems to objects. A pertinent
example of this in the TS10 TokenSpace example illustrated in §4 is the consolidation of seven multi-layered
dimensions addressing the balance of power and influence between network stakeholders into a single “alignment
index”.
It is important to consider Goodhart’s Law when developing quantitative metrics, as the decentralisation
theatre discussed in §1.3.3 or susceptibility of metrics to Sybil nodes or automated agents mean that many
quantitative metrics cannot be relied upon in an absolute sense. That which can be measured, becomes an
optimisation target and as a result is susceptible to manipulation [120]. Comparative analytical approaches may
be still be valuable, and judgement should be exercised by the researcher with respect to this. Taking this one
step further, the researcher should keep in mind that their classification approach may require constant appraisal
and optimisation. Due to strong incentives for ecosystem participants to portray some assets favourably with
respect to others for a variety of motivations, the very act of publishing a classification approach renders it
vulnerable to perversion through relatively facile means of manipulation. In this respect, though making use of

24
indexed dimensions increases the relative subjectivity of a classification system it also somewhat mitigates the
effects of Goodhart’s Law providing the researcher is sufficiently cautious in the development of their taxonomic
system and its application to populations of objects.
3.3.5 Score Modifiers & Weightings for Taxonomy Characteristics & Dimensions The purpose of
assigning weightings to each dimension and/or characteristic in a meta-characteristic’s taxonomy is to provide
a rational basis from which to derive a quantitative but subjective score for each axis that a TokenSpace is
constructed from. In the instantiation of TokenSpace presented in §4, with three meta-characteristics and indexed
or categorical discrimination the weightings attached to dimensions may be conceived intuitively or by employing
optimisation approaches. So for a dimension with three (mutually-exclusive) characteristics, characteristic A may
increase the overall score by 0.05, characteristic B may increase it by 0.025 and characteristic C leads to no
change. In the instantiation of TokenSpace presented in §4, weightings have been applied in an ad hoc manner
for each meta-characteristic’s taxonomy. In doing so, even when different taxonomies contain significant overlap
of dimensions, categories and characteristics their weightings and overall scores may vary depending on how
important those factors are judged to be in contributing to the meta-characteristic.
Approximate “target” scores for a selection of assets were intuitively reasoned and score modifiers for char-
acteristics adjusted to facilitate loose convergence of conceptual and empirical methods. This approach could be
refined by using statistical and operational research techniques such as cluster analysis of results, algorithmic
optimisation or methods such as Delphi elicited judgement [121].
The approach employed in §4 with TS10 was to assign weightings to the dimensions themselves, so that
the characteristic scores are themselves scored for importance in their overall contribution to each meta-
characteristic score.
The definition of one or more category would be the selection of the dimensions: {ai,i= 1, ..., n},
which together would constitute the taxonomy for a particular meta-characteristic, and the relative weighting
of these attributes: {wi,i= 1, . . . , n,Piwi= 1}.
3.3.6 Time-Dependence Time-dependence of asset characteristics and hence their location in TokenSpace
may be significant in certain instances due to temporal phenomena such as the putative Lindy effect, increased
functionality of a network and hence a token that is required to access its services, and more generally the
dynamic nature of cryptocurrency protocol networks and their native assets, tokens issued atop them and
network fragmentations such as ledger forks. Time-dependent phenomena can be coarsely incorporated into
this framework by evaluating an asset’s location in TokenSpace at different points in time and charting asset
trajectories. A more advanced approach (currently in development) could be to calculate a coordinate at time
tbased upon functions mapping expected or judged variation. Care should be taken to distinguish between
temporal interpolation between past and present, and extrapolation beyond the present as future characteristics
of an asset may not necessarily be easily predicted with a high degree of confidence.
A pertinent example of time-dependence of the Securityness meta-characteristic is the ostensible judgement
of senior SEC official William Hinman to heavily imply in public comments made in summer 2018 that he
deemed the Ethereum network to have become “sufficiently decentralised” for its native token ETH to not be
considered a security, although the token crowdfunding event in 2014 most likely was a securities offering (see
§1.3.3). These comments give rise to the necessity of a time-dependent scoring aspect to allow for changes as the
network and / or asset matures. This may be justified as follows: the evolving characteristics of proliferating
tokenised P2P networks appear to have a significant bearing on the opinions of senior regulators with respect
to the security status of particular assets, and no objective boundary (or “Securityness threshold”) to separate
objects on either side of exists a priori. Another consideration is the prospect of a “grace period” for distributed
networks bearing cryptographic assets and tokens, as invariably these will commence operation as much more
concentrated / centralised systems than the would eventually be envisaged to become.
Whilst these types of comments are indicative of an open-minded approach from legislative officials, some
open questions remain. If future claims of ether on the as-yet-unfunctional Ethereum Frontier mainnet were
a security at time of the crowdfunding, but ETH no longer carries that designation due to an “increase in
decentralisation” then - making the assumption that regulators are rational and logical actors - we can propose
that some regulatory boundary surface / zone in TokenSpace has been advanced through, from is security to is
not security and the primarily underlying reason for this according to Hinman is “sufficient decentralisation”
without making clear what the justification for this opinion was, or indeed decentralistion at which layer of the
network meta-stack and ecosystem (§1.2).
A more general regulatory policy question arises, which has significant implications for assets which were
issued in a state of high Securityness with properties such as pre-network functionality and / or tightly held
supply by insiders. Indeed the long-running cryptoasset exchange Poloniex announced in late 2018 that it was
delisting three assets Gnosis (GNO), Expanse (EXP) and Synereo (AMP) without reasons proffered. However
these assets share to some extent both of the above Securityness increasing properties, lending credence to the

25
notion that the new owner of the business - Goldman Sachs-backed Circle - intends to follow a compliance-first
strategy and are assessing the possible declaration of particular cryptoassets as securities by the SEC at some
point in the future [46].
3.3.7 Boundary Functions Following on from the complexities raised in §3.3.6 regarding the time-dependence
of the location of assets inhabiting TokenSpace, the notion of a regulatory boundary function has been developed
to delineate the change in ETH’s status from is security to is not security based upon Hinman’s summer 2018
comments (see §1.3.3). Naturally this is a subjective assignment and may be affected by factors both endogenous
and exogenous to the networks and assets in question. An example of such a regulatory boundary visualised in
TokenSpace is presented in Figure 16.
This binary approach may be extended to account for edge cases and regulatory grey zones by instead
making a “safe”, “marginal ” or “dangerous” distinction with regard to the likely compliance of assets with
respect to particular regulatory regimes. Careful review of territory-specific regulatory guidance and judicious
consideration of boundary functions is a necessity for this approach to have utility beyond the hypothetical.
3.3.8 Non-point & Anisotropic Asset Locations When there is uncertainty or disagreement regarding
the optimal weighting of a taxonomy characteristic or the categorical assignment of an asset, it may be helpful
to employ a range, error bars or probability density functions (PDFs) to represent the likelihood of the meta-
characteristic instead of precise loci [122]. The space-filling representations of quantum mechanical electron
orbitals displayed in Figure 17 provide a set of well-understood and characterised functions from which to
develop such an extension to TokenSpace and this is the subject of ongoing research.
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Commodityness
Moneyness
Securityness
Figure 16: Regulatory / compliance boundary visualised in TokenSpace. Arbitrary polynomial for illustrative purposes

26
Figure 17: Single electron orbital probability density functions represented in 3D space [122]

Dimension Category Characteristics
Explicit Incentives
(1) Profit-sharing / cost-reduction Dividends, token buybacks or burns Issuer-sanctioned airdrops Service discounts Combinations / other None
A (0.4) B (0.3) C (0.3) D (0 to 1) E (0)
(2) Rights to CFs Masternodes Staking rewards Combinations / other None
A (0.6) B (0.4) C (0 to 1) D (0)
Implicit Incentives
(3) Marketing claims / investor expectations Quantitative variable: Expectations index
(0 to 1)
(4) Network functionality at time t Quantitative variable: Network functionality index (Inverse)
(0 to 1)
(5) Asset utility at time t Quantitative variable: Asset utility index (Inverse)
(0 to 1)
(6) Initial issuance mechanism PoW Costly PoX e.g Proof-of-Burn Snapshot / Fork / Airdrop Costless PoX Arbitrary Mint / Premine / Instamine ICO SAFT/Deferred Distribution
A (0) B (0.2) C (0.4) D (0.55) E (0.7) F (0.85) G (1)
(7) Ongoing issuance mechanism PoW PoX Arbitrary Mint (algorithmic / autonomous) Arbitrary Mint (centrally controlled) None
A (0) B (0.25) C (0.5) D (0.75) E (1)
Stakeholder Alignment
(8) Balance between network participants Quantitative variable: Alignment index (Inverse)
(0 to 1)
Network Governance
(9) Governance Type Masternode Token/ticket Reputation Combination / other None / off-chain
A (0.6) B (0.3) C (0.2) D (0 to 1) E (0)
(10) Token-Holder voting rights Upgrade & treasury Upgrade Treasury Indirect (implicit) None (explicit)
A (1) B (0.5) C (0.5) D (0.2) E (0)
(11) Treasury / foundation supply control Quantitative variable: Insider supply control index
(0 to 1)
(12) Power asymmetry between issuer and investors Quantitative variable: Asymmetry index
(0 to 1)
(13) Human primacy over codebase / assets Quantitative variable: Mutability index
(0 to 1)
Network Topology / Software Properties
(14) Network centrality / distribution Quantitative variable: Centrality index
(0 to 1)
(15) Permissioned / proprietary elements Quantitative variable: Openness index (Inverse)
(0 to 1)
(16) Ease of node operation / software compatibility Quantitative variable: Network compatibility index (Inverse)
(0 to 1)
Table 9: Securityness taxonomy (3rd iteration)
1

Dimension Category Characteristics
Longevity
DURABILITY
(1) In/extra-protocol persistence Quantitative variable: Technical / social antifragility index
(0 to 1)
(2) Lindy type time-dependent effects Quantitative variable in the time domain
(0 to 1)
RESILIENCE
(3) Network & protocol robustness Quantitative variable: Robustness index
(0 to 1)
(4) Key entity risk / Layer N risk / Quantitative variable: Externality index (Inverse)
Smart Contract risk / Cryptography risk (0 to 1)
(5) Balance between network participants Quantitative variable: Alignment index (Inverse)
(0 to 1)
Portability
(6) Transactional friction Quantitative variable: Friction index (Inverse)
(0 to 1)
Pervasiveness
(7) Realness / Materiality Quantitative variable: Materiality index
Memeticism / Simulacrisation (0 to 1)
(8) Protocol breadth at time t Quantitative variable: Protocol breadth index
(0 to 1)
(9) Network maturity at time t Quantitative variable: Maturity index
(0 to 1)
(10) Network legitimacy at time t Quantitative variable: Legitimacy index
(0 to 1)
Fungibility / Privacy
(11) In protocol Full transparency Opt-in privacy Private with selective audit Fully private
A (0) B (0.3) C (0.65) D (1)
(12) Extra protocol Coin mixing Sender obfuscation Node level L2 / Sidechain Combinations thereof None
A (0.25) B (0.25) C (0.25) D (0.25) E (0 to 1) F (0)
Supply Dynamics
(13) Initial issuance mechanism PoW Costly PoX e.g Proof-of-Burn Snapshot / Fork / Airdrop Costless PoX Arbitrary Mint / Premine / Instamine ICO SAFT/Deferred Distribution
A (1) B (0.8) C (0.6) D (0.45) E (0.3) F (0.15) G (0)
(14) Ongoing issuance mechanism PoW PoX Arbitrary Mint (algorithmic / autonomous) Arbitrary Mint (centrally controlled) None
A (1) B (0.75) C (0.5) D (0.25) E (0)
(15) Price elasticity of supply Quantitative variable: Price Elasticity of Supply / unforgeable costliness
(0 to 1)
(16) Inflation None Fixed/known Adaptive/variable
A (1) B (0.5) C (0)
(17) Scarcity Hard cap (algorithmically enforced) Cap (social consensus / implied) No cap (promised) No cap (no promise)
A (1) B (0.65) C (0.3) D (0)
(18) Asset supply concentration Quantitative variable: Proportion of supply controlled by insiders/foundation (Inverse)
(0 to 1)
Demand Dynamics
(19) Acceptability as payment method Quantitative variable: Acceptability index
(0 to 1)
(20) Settlement and value flows Quantitative variable: Settlement index
(0 to 1)
(21) Liquidity depth Quantitative variable: Liquidity index
(0 to 1)
(22) Volatility in price Quantitative variable: Volatility index (Inverse)
(0 to 1)
Table 10: Moneyness taxonomy (3rd iteration)
2

Dimension Category Characteristics
Longevity
DURABILITY
(1) In/extra-protocol persistence Quantitative variable: Technical / social antifragility index
(0 to 1)
(2) Lindy type time-dependent effects Quantitative variable in the time domain
(0 to 1)
RESILIENCE
(3) Network & protocol robustness Quantitative variable: Robustness index
(0 to 1)
(4) Key entity risk / Layer N risk / Quantitative variable: Externality index (Inverse)
Smart Contract risk / Cryptography risk (0 to 1)
(5) Balance between network participants Quantitative variable: Alignment index (Inverse)
(0 to 1)
Portability
(6) Transactional friction Quantitative variable: Friction index (Inverse)
(0 to 1)
Pervasiveness
(7) Realness / Materiality Quantitative variable: Materiality index
Memeticism / Simulacrisation (0 to 1)
(8) Protocol breadth at time t Quantitative variable: Protocol breadth index
(0 to 1)
(9) Liquidity depth Quantitative variable: Liquidity index
(0 to 1)
(10) Network maturity at time t Quantitative variable: Maturity index
(0 to 1)
(11) Network legitimacy at time t Quantitative variable: Legitimacy index
(0 to 1)
Fungibility / Privacy
(12) In protocol Full transparency Opt-in privacy Private with selective audit Fully private
A (0) B (0.3) C (0.65) D (1)
(13) Extra protocol Coin mixing Sender obfuscation Node level L2 / Sidechain Combinations thereof None
A (0.25) B (0.25) C (0.25) D (0.25) E (0 to 1) F (0)
Supply Dynamics
(14) Initial issuance mechanism PoW Costly PoX e.g Proof-of-Burn Snapshot / Fork / Airdrop Costless PoX Arbitrary Mint / Premine / Instamine ICO SAFT/Deferred Distribution
A (1) B (0.8) C (0.6) D (0.45) E (0.3) F (0.15) G (0)
(15) Ongoing issuance mechanism PoW PoX Arbitrary Mint (algorithmic / autonomous) Arbitrary Mint (centrally controlled) None
A (1) B (0.75) C (0.5) D (0.25) E (0)
(16) Price elasticity of supply Quantitative variable: Price Elasticity of Supply / unforgeable costliness
(0 to 1)
(17) Inflation None Fixed/known Adaptive/variable
A (1) B (0.5) C (0)
(18) Scarcity Hard cap (algorithmically enforced) Cap (social consensus / implied) No cap (promised) No cap (no promise)
A (1) B (0.65) C (0.3) D (0)
(19) Asset supply concentration Quantitative variable: Proportion of supply controlled by insiders/foundation (Inverse)
(0 to 1)
Demand Dynamics / Usefulness
(20) Asset purpose Access to service Rights to CFs Reward potential / spec. Volatility hedge SoV Issuer-mandated airdrops Payment / MoE Combination / none
A (0.15) B (0.15) C (0.15) D (0.15) E (0.15) F (0.15) G (0.15) H (0 to 1)
(21) Asset utility at time t Quantitative variable: Utility index
(0 to 1)
(22) Settlement and value flows Quantitative variable: Settlement index
(0 to 1)
Table 11: Commodityness taxonomy (3rd iteration)
3

wt / BTC ETH XRP EOS LTC BCH USDT XLM TRX BNB
1 Profit share / cost red 0.025 0 0 0 0.3 0 0 0 0.3 0.3 0.3
2 Rights to CFs 0.025 0 0 0 0.4 0 0 0 0 0.4 0
3 Marketing Claims / Investor Expectations 0.05 0 0.7 0.85 1 0.2 0.3 0.3 0.7 1 0.8
4 Network Function 0.15 0.1 0.5 0.8 0.9 0.3 0.15 0.2 0.8 0.6 0.8
5 Asset Utility 0.1 0.3 0.45 0.8 0.9 0.4 0.5 0.2 0.7 0.85 0.85
6 Initial Issuance Mech 0.1 0 0.85 0.7 0.85 0 0.4 0.2 0.7 0.85 0.85
7 Ongoing Issuance Mech 0.05 0 0 1 0.55 0 0 0.75 1 0.25 1
8 Stakeholder Balance 0.1 0.2 0.65 0.9 0.8 0.5 0.6 0.7 0.9 1 1
9 Governance Type 0.025 0 0.2 0.2 0.6 0 0.2 0.2 0.2 0.6 0.3
10 Token-Holder Vote Rights 0.025 0 0 0 0.5 0 0 0 0 0.5 0.2
11 Treasury Supply Control 0.05 0 0.4 0.85 0.5 0.2 0 1 0.9 0.9 0.9
12 Power Asymm 0.1 0.2 0.6 0.9 1 0.3 0.4 0.8 0.9 0.9 0.9
13 Primacy Algo/Human Code & Assets 0.1 0 0.75 0.9 1 0.3 0.3 1 0.9 0.9 0.9
14 Network Centrality / Distn 0.05 0 0.2 0.7 0.8 0.4 0.3 1 0.9 0.9 0.9
15 Permissioned/Propietary Elements 0.025 0 0 0.6 0 0 0 1 0.6 0 0.6
16 Ease of Node Operation / Compatability 0.025 0.1 0.3 0.6 0.9 0.1 0.2 1 0.6 0.9 1
TS10 SCORE 0.09 0.48 0.75 0.80 0.24 0.28 0.53 0.75 0.76 0.81
Table 12: Securityness category assignments and scores as of April 2019
wt / BTC ETH XRP EOS LTC BCH USDT XLM TRX BNB
1 In/extra-protocol persistence 0.025 0.6 0.1 0.05 0.1 0.25 0.2 0.3 0.05 0.05 0.05
2 Lindy type time-dependent effects 0.025 0.3 0.15 0.05 0 0.2 0.05 0.1 0.05 0 0
3 Network robustness 0.05 0.4 0.2 0 0 0.15 0.05 0.05 0 0 0
4 Externality risk 0.05 0.3 0.1 0.05 0.025 0.15 0.05 0 0.05 0 0
5 Stakeholder alignment 0.05 0.65 0.8 0.1 0.1 0.3 0.1 0 0.05 0.025 0.025
6 Transactional friction 0.025 0.3 0.3 0.05 0.05 0.2 0.2 0.2 0.1 0.1 0.025
7 Realness / materiality 0.05 0.75 0.15 0.05 0.05 0.025 0.05 0.1 0.05 0.05 0
8 Protocol breadth 0.1 0.2 0.1 0.05 0.025 0.075 0.05 0.1 0.05 0.025 0
9 Network maturity 0.05 0.3 0.1 0.1 0.025 0.15 0.05 0.1 0.1 0.05 0.025
10 Network legit 0.025 0.6 0.4 0.1 0.2 0.05 0.1 0.1 0.1 0.05 0.025
11 Fung/priv in-prot 0.1 0 0 0 0 0 0 0 0 0 0
12 Fung/priv extra-prot 0.025 0.6 0.2 0 0 0.2 0.2 0 0 0 0
13 Initial issuance 0.025 1 1 0.3 0.15 1 1 0.8 0.3 0.15 0.15
14 Ongoing issuance 0.025 1 1 0 0.75 1 1 0.25 0 0.75 0.75
15 Price inelasticity 0.025 0.8 0.5 0.1 0.1 0.35 0.8 1 0.1 0.1 0.1
16 Inflation 0.025 0.5 0 1 0 0.5 0.5 0 1 0.5 0.5
17 Scarcity 0.025 1 0.3 1 0 1 1 0 1 0.3 0.3
18 Asset supply conc 0.05 0.5 0.3 0.1 0.15 0.2 0.2 0.2 0.05 0.05 0.05
19 Acceptability paym 0.1 0.3 0.15 0.05 0.05 0.1 0.1 0.1 0.05 0.025 0.025
20 Settlement/valflows 0.05 0.4 0.1 0.05 0.025 0.01 0.05 0.15 0.05 0.025 0.025
21 Liquidity depth 0.05 0.25 0.1 0.025 0.01 0.01 0.025 0.5 0.025 0.025 0.025
22 Volatility 0.05 0.3 0.1 0.05 0.05 0.05 0.05 1 0.05 0.05 0.05
TS10 SCORE 0.41 0.22 0.10 0.06 0.19 0.17 0.19 0.10 0.07 0.06
Table 13: Moneyness category assignments and scores as of April 2019
wt / BTC ETH XRP EOS LTC BCH USDT XLM TRX BNB
1 In/extra-protocol persistence 0.025 0.6 0.1 0.05 0.1 0.25 0.2 0.3 0.05 0.05 0.05
2 Lindy type time-dependent effects 0.025 0.3 0.15 0.05 0 0.2 0.05 0.1 0.05 0 0
3 Network robustness 0.025 0.4 0.2 0 0 0.15 0.05 0.05 0 0 0
4 Externality risk 0.025 0.3 0.1 0.05 0.025 0.15 0.05 0 0.05 0 0
5 Stakeholder alignment 0.025 0.8 0.25 0.1 0.1 0.3 0.1 0 0.05 0.025 0.025
6 Transactional friction 0.025 0.3 0.3 0.05 0.05 0.2 0.2 0.2 0.1 0.1 0.025
7 Realness / materiality 0.05 0.75 0.15 0.05 0.05 0.025 0.05 0.1 0.05 0.05 0
8 Protocol breadth 0.025 0.2 0.1 0.05 0.025 0.075 0.05 0.1 0.05 0.025 0
9 Liquidity depth 0.025 0.25 0.1 0.025 0.01 0.01 0.025 0.5 0.025 0.025 0.025
10 Network maturity 0.025 0.3 0.1 0.1 0.025 0.15 0.05 0.1 0.1 0.05 0.025
11 Network legit 0.025 0.6 0.4 0.1 0.2 0.05 0.1 0.1 0.1 0.05 0.025
12 Fung/priv in-prot 0.025 0 0 0 0 0 0 0 0 0 0
13 Fung/priv extra-prot 0.025 0.6 0.2 0 0 0.2 0.2 0 0 0 0
14 Initial issuance 0.025 1 1 0.3 0.15 1 1 0.8 0.3 0.15 0.15
15 Ongoing issuance 0.025 1 1 0 0.75 1 1 0.25 0 0.75 0.75
16 Price inelasticity 0.05 0.8 0.5 0.1 0.1 0.35 0.8 1 0.1 0.1 0.1
17 Inflation 0.025 0.5 0 1 0 0.5 0.5 0 1 0.5 0.5
18 Scarcity 0.025 1 0.3 1 0 1 1 0 1 0.3 0.3
19 Asset supply conc 0.025 0.5 0.3 0.1 0.15 0.2 0.2 0.2 0.05 0.05 0.05
20 Asset purpose 0.05 0.6 0.75 0.45 0.6 0.6 0.6 0.45 0.6 0.6 0.6
21 Asset utility 0.375 0.9 0.6 0.2 0.1 0.4 0.3 0.3 0.3 0.1 0.1
22 Settlement/val flows 0.05 0.4 0.1 0.05 0.025 0.01 0.05 0.15 0.05 0.025 0.025
TS10 SCORE 0.68 0.42 0.18 0.12 0.34 0.31 0.27 0.23 0.13 0.12
Table 14: Commodityness category assignments and scores as of April 2019
4

31
4 Creating a TokenSpace: TS10
4.1 Iterative Construction of Taxonomies, Indices & Score Modifiers
Based on the asset characteristics discussed in §1.3 and the methodologies employed by Bailey, Nickerson and
Prat in §2 and §3, the author has constructed a TokenSpace named TS10 and three hybrid categorical and
quantitative taxonomies with weighted scores based on the meta-characteristics, dimensions and characteristics
discussed in §3.2 paying heed to the design considerations outlined in §3.3. These taxonomies are shown in Tables
9, 10 and 11. In applying the Nickerson methodology (§3.2.1) some judgement was used to reduce the complexity
of the putative taxonomies by consolidating a number of similar and / or overlapping categories into “indexed”
ranged score modifiers as discussed in §3.3.4. Care was taken to ensure the correct “polarity” of outputted scores -
for example a network perceived to have poor stakeholder balance would lead to an increase in Securityness but a
decrease in Moneyness. This is consistent with the design goals of being useful, straightforward to apply and min-
imising arbitrary elements. It is a potential goal for extensions of this work to more thoroughly delineate the im-
pact of each of these elements in finer granularity and assign appropriate score modifiers and / or branch weight-
ings to them.
TS10 Taxonomy Development Iterations:
1) Progression from intuitively reasoned shortlists in §3.3 to categorical & indexed dimensions.
2) Assigned unstandardised characteristic score modifiers (weightings incorporated), reduced number of dimen-
sions, some categorical dimensions consolidated into index form.
3) Standardised characteristic score modifiers to separately apply weightings, further reduction of dimensions,
collapsing some categoricals further into indices for ease of application - at possible expense of increased sub-
jectivity.
4.2 Placing Assets in TS10
Having produced these proto-taxonomies, the Nickerson method was applied and selected major cryptographic
assets were “classified” with meta-characteristic scores for Securityness (S), Moneyness (M) and Commodity-
ness (C) in order to populate this TS10 instantiation of TokenSpace. Assets were selected by their perceived
importance via the admittedly coarse heuristic of market capitalisation at time of writing. Assets included:
Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), EOS (EOS), Litecoin (LTC), Bitcoin ABC (BCH), Tether
(USDT), Stellar (XLM), Tron (TRX) and Binance Coin (BNB). TS10 full breakdowns of scores and weightings
for each meta-characteristic’s taxonomy are presented in Tables 12, 13 and 14. Overall score values are presented
in Table 15 and definitions of dimensions in Table 16. Statistical analytical results are visually represented in
Figures 18 & 19, and the TS10 TokenSpace is visually represented in Figure 20.
DISCLAIMER: A reminder that the decisions of category selection, dimension weighting and / or index values have been made in an ad hoc,
approximate and subjective manner and do not necessarily correlate to an objective representation of reality. The author is not a lawyer, regulator or legal professional
and has no definitive opinion on the regulatory or compliance status or consequences of assets being classified with particular assignations by any territorial or jurisdictional legislature. By
reading this document you agree that the author accepts no liability or responsibility for the results outlined below or any discussions arising thereof. TS10, TSL7 and TSTDX TokenSpace scores
are provided for intel lectual purposes and the aforementioned TokenSpaces are an abstract and hypothetical representation based upon the methodologies developed in this work.
Asset (S) (M) (C)Notes
BTC 0.09 0.41 0.68
ETH 0.48 0.22 0.42
XRP 0.75 0.10 0.18
EOS 0.80 0.06 0.12
LTC 0.24 0.19 0.34
BCH 0.28 0.17 0.31
USDT 0.53 0.19 0.27
XLM 0.75 0.10 0.23
TRX 0.76 0.07 0.13
BNB 0.81 0.06 0.12
Mean 0.55 0.16 0.28
Std. Dev. 0.27 0.11 0.17
Table 15: TS10 scores listed to two decimal places

32
SMC Balance between network participants:* The extent of egality and balance of influence among the
various network stakeholders.
SMC Initial issuance mechanism: The token distribution method employed by asset issuers at first.
SMC Ongoing issuance mechanism: The token distribution method employed by asset issuers after genesis.
SC Asset utility at time t:* How functional the asset is at a point in time.
MC In / extra-protocol persistence:* The extent to which the social and protocol layers of the network
resist influence to change the transaction record.
MC Lindy type time-dependent effects:* A variable in the time domain to reflect the gradual increase in
desirability of an asset with reference to the putative Lindy effect.
MC Network & protocol robustness:* Security and honesty assumptions / requirements for optimal network
function.
MC Key entity risk / Layer N risk / Smart Contract risk / Cryptography risk:* Long-tail risks and systemic
frailties.
MC Transactional friction:* How facile transacting with the asset is, on base or secondary layers.
MC Realness / Materiality / Memeticism / Simulacrisation:* The ”realness” or ”mimetic stickiness” of
an asset.
MC Protocol breadth at time t:* How widely the network and protocol are distributed at a point in time.
MC Network maturity at time t:* How mature the network is at a point in time.
MC Network legitimacy at time t:* How legitimate the network is considered to be in terms of functionality
at a point in time.
MC Privacy In-protocol: Privacy of transactions and participants using the network in a naive manner.
MC Privacy Extra protocol: Privacy of transactions and participants using additional tools or techniques.
MC Price elasticity of supply:* The extent to which additional supply of an asset can be brought to
production / market in response to an increase in demand.
MC Inflation: The inflationary properties of the asset supply.
MC Scarcity: Whether the asset has fixed or implied supply limits.
MC Asset supply concentration:* How widely distributed is the asset. As Gini-type measurements are
highly gameable an indexed approach of researcher perceptions based on publically available information
has instead been employed.
MC Liquidity depth:* The extent to which the asset’s market can absorb suppply and demand changes.
MC Settlement and value flows:* The extent to which the asset is used for payments and settlement.
SProfit-sharing / cost-reduction: Whether network / asset profits are shared with token-holder, or if
holding the asset gives rights to preferential treatment.
SRights to CFs: Does holding the asset confer rights to on-chain cashflows such as masternodes or staking
rewards?
SMarketing claims / investor expectations:* The extent to which network / asset proponents engaged in
marketing with speculative or profit-oriented themes.
SNetwork functionality at time t:* How functional the network is at a point in time.
SGovernance Type: What governance mechanism (if any) doe the network utilise?
SToken-Holder voting rights: Do the token-holders have ”voting rights” in network governance and how
is this exercised?
STreasury / foundation supply control:* The extent of the asset supply under control by a central treasury
or foundation.
SPower asymmetry between issuer and investors:* The extent to which the network / asset creators are
advantaged over token-holders in terms of implicit and / or explicit influence.
SHuman primacy over codebase / assets:* The extent to which the network / asset creators can make
changes to the network function, codebase, asset distribution or transactions.
SNetwork centrality / distribution:* How widely dispersed the network nodes are. As node number and
distribution are highly gameable an indexed dimension of the author’s perceptions based on publically
available information has instead been employed.
SPermissioned / proprietary elements:* Are there permissioned, closed-source or patented elements in
the network?
SEase of node operation / software compatibility:* How straightforward it is to run a validating node in
the network.
MAcceptability as payment method:* How widely the asset is accepted in return for goods and services.
MVolatility in price:* The extent of variation in the market pricing of the asset (relative to USD).
CAsset purpose: What is the intended purpose for holding and / or using the asset?
*Indicates indexed dimension, others are categorical.
S, M, C indicate which meta-characteristic taxonomies the dimension is included in.
Table 16: Definitions and rationale for categorical and indexed dimensions for TS10

33
Sbar
0.0 0.2 0.4 0.6 0.8 1.0
0 1 2 3 4
Mbar
0.1 0.2 0.3 0.4
0 1 2 3 4
Cbar
0.1 0.2 0.3 0.4 0.5 0.6 0.7
0 1 2 3 4
log10Sbar
0.00 0.10 0.20 0.30
0.0 0.5 1.0 1.5 2.0 2.5 3.0
log10Mbar
0.02 0.06 0.10 0.14
0.0 0.5 1.0 1.5 2.0 2.5 3.0
log10Cbar
0.00 0.05 0.10 0.15 0.20 0.25
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Figure 18: TS10 meta-characteristic score histograms
0.2 0.4 0.6 0.8
Sbar
0.05 0.15 0.25 0.35
Mbar
0.1 0.2 0.3 0.4 0.5 0.6 0.7
Cbar
0.05 0.10 0.15 0.20 0.25
log10Sbar
0.04 0.08 0.12
log10Mbar
0.05 0.10 0.15 0.20
log10Cbar
Figure 19: TS10 meta-characteristic score boxplots

34
Considering first the scores realised by the TS10 TokenSpace in Table 15, it is clear that there is significant
variation between the values obtained for S,Mand Cmeta-characteristics across the 10 cryptoassets with the
highest nominal market capitalisations.
Securityness in TS10 exhibits a minimum of 0.088 for BTC, a maximum of 0.81 for BNB, a mean of 0.55 and
standard deviation of 0.27. The range of values is wide at 0.72, as some cryptoassets possess very high values
whilst others possess low or moderate values. There is also considerable skew in the distribution of these values,
as the maximum value is 0.96 standard deviations from the mean whilst the minimum value is 1.7 standard
deviations away.
Moneyness in TS10 exhibits a minimum of 0.06 for BNB, a maximum of 0.41 for BTC, a mean of 0.16
and standard deviation of 0.11. The range of values is comparatively compact at 0.35, a consequence of all
cryptoassets possessing modest or low scores. Significant skew is also present in the distribution of these values,
as the maximum value is 2.3 standard deviations from the mean whilst the minimum value is 0.91 standard
deviations away.
Commodityness in TS10 exhibits a minimum of 0.12 for EOS and BNB, a maximum of 0.68 for BTC, a
mean of 0.28 and standard deviation of 0.17. The range of values is significant at 0.56. There is also considerable
skew in the distribution of these values, as the maximum value is 2.35 standard deviations from the mean whilst
the minimum value is 0.94 standard deviations away.
Figures 18 and 19 depict histograms and boxplots for each of the three meta-characteristics in linear and
logarithmic scales to visually display the distibution of scores. Interestingly the statistical computing software
employed here (R) initially treated the Mand Cvalues of BTC as outliers due to their significant difference
to the other cryptoassets considered. In the author’s opinion this is a reflection of Bitcoin’s unique status as a
leaderless and permissionless pseudo-monetary commodified good with a paucity of attributes in common with
other cryptoassets. In other words, Bitcoin is sui generis among other cryptoassets, which themselves are sui
generis to lesser and varying degrees in comparison to legacy assets.
Figure 20 contains a three-dimensional view of the TS10 TokenSpace with included assets occupying the
coordinates arising from their scores. As intended in the design of the TokenSpace methodology, this affords
ready visual comparison of asset locations. From this visual representation, it becomes more readily apparent
that there are several sub-populations of cryptoassets within TS10. BTC occupies a domain of its own, as do
ETH and USDT to a lesser extent. This is unsurprising as these three cryptoassets are significantly differentiated:
Bitcoin as a highly decentralised P2P commodity money, Ethereum as a scripting platform and Tether as a
collateralised stablecoin. Ethereum’s Securityness largely arises from its initial issuance mechanism - its token
crowdfunding was the largest “ICO” at time of launch - and the presence of a powerful foundation and leadership
class who largely influence and fund the course of action of the network [123]. BCH and LTC occupy locations
close to each other, as minor analogues of Bitcoin they exhibit some similarities to BTC but have much weaker
value propositions as monetary or commodity goods due to inferior network security and the presence of single
points of failure such as prominent leadership. However like Bitcoin they did not issue assets via a token sale
and solely rely on proof-of-work, keeping their Securityness fairly low. XRP and XLM appear close together
which is unsurprising as they share many of the same characteristics, with XLM originating as a codebase
fork of XRP by the same progenitor (Jed McCaleb), with both networks having a heavy concentration of asset
supply residing with insiders and / or foundations. Network nodes of both XRP and XLM are challenging to
run permissionlessly with high concentrations of validating nodes in the respective federations being controlled
by Ripple Labs and IBM / Stellar Foundation respectively [124, 125]. XRP transactions have been prevented
from occurring due to disputes between network controllers and estranged insiders, and a major historical covert
supply inflation event was recently uncovered in XLM which allowed an attacker to create several billion tokens
[126]. For these reasons and others, XRP and XLM are poor monetary or commodity assets but do display a
fairly high degree of Securityness.
EOS, TRX and BNB are the final subset of cryptoassets in TS10. All three were intially issued via ICOs,
and exhibit a high degree of centralisation in monetary, network, architecture and / or stakeholder influence
with limited asset and / or network utility at time of analysis in early 2019. These cryptoasets exhibit high
Securityness, low Moneyness and low Commodityness, making them possible targets for regulators looking for
high profile cases to investigate. It is somewhat apparent from the operations of these projects that this has
been considered to be a risk, with EOS creator Block.One locating themselves in the British Virgin Islands and
offering non-functional tokens in their ICO with no promise to launch a network. Binance Coin is issued by
the sprawling exchange group Binance, which is commencing exchange operations in new jurisdictions faster
than regulators can react having nominally relocated to Malta last year. The token burning and exchange fee
discounts for token-holders give BNB a very high degree of likeness to a classical securitised asset. Tron is a
project which seems to be mostly focused on marketing with a constant stream of giveaways and partnership
announcements, a rather consistent record of questionable veracity of claims, initially uncredited re-use of code
(EtherumJ ), whitepaper (IPFS / Filecoin), dubious claims and explicit promotion of the speculative potential
of the asset [127].

35
0.1 0.2 0.3 0.4 0.5 0.6 0.7
0.0 0.2 0.4 0.6 0.8 1.0
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
Commodityness
Moneyness
Securityness
BTC
ETH
XRP
EOS
LTC
BCH
USDT
XLM
TRX
BNB
Figure 20: Assets placed in TS10 TokenSpace
4.3 Cluster Analysis & Correlations
Two statistical analysis methods were employed to further understand the anisotropy of the asset locations in
TS10,k-means and agglomerative hierarchical clustering [128, 129]. Each approaches the dataset in different
ways to reach a set of ending conditions, in a manner not unlike taxonomies themselves. The k-means algorithm
creates knumber of cluster centroids (with kbeing adjustable or optimisable) before iteratively assigning values
to the closest centroid and adjusting the updated position of the centroid until no further changes take places
between iterations. One key assumption made by the algorithm is that data is isotropic (or spherical), which may
render it ineffective for advanced TokenSpace studies employing higher dimensionalities and / or anisotropic
PDFs as discussed in §3.3.8.
Agglomerative hierarchical clustering in contrast incrementally builds clusters, producting a dendrogram.
The algorithm first assigns each sample to its own cluster, with each step incorporating a merge of the two most
similar clusters until all have been merged. When perceived in reverse, the dendrogram resembles a stepwise
sorting machine, sub-dividing the population of objects on the basis of similarity. No centroid parameter is
required.
For k-means clustering of TS10, it was found that values of kof 3 or above produced acceptable levels of
extrinsic variation between the clusters versus intrinsic variation within the sum of squares of clusters (Figure
21). To correlate with the visually-derived groupings above, 6 clusters were judged to be optimal but the analysis
could also be conducted using 4 or 5. Results from k-means clustering with values of k= 3, 4, 5 and 6 are
displayed in Figure 22.
Hierarchical complete-link clustering was conducted with the dendrogram produced exhibited in Figure 23.
Complete-link refers to the clustering method, whereby in each iteration, merging occurs between two clusters
which possess the smallest maximum pairwise distance. In contrast single-link clustering merges the two clusters
with the smallest minimum pairwise distance. In this case, both techniques were employed and the complete-link
approach resulted in the greater similarity between clusters as measured by agglomerative coefficient, with 0.84
versus 0.77 for single-link [130]. As discussed above, when the clustering process is considered in reverse, it takes
on some of the properties of a “sorting machine”, allowing the degrees of similarities and differences between
assets to be readily parsed. Largely corroborating previous results, the process found BTC to be unique among
the TS10 basket of cryptoassets, with further clusters ETH and USDT, LTC and BCH, XRP and XLM and

36
2 4 6 8
0 20 40 60 80 100
k
Percentage of variability between clusters
Figure 21: Choosing kfor k-means clustering of TS10
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
0.00.10.20.30.40.5
Cbar
Mbar
Sbar
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
0.00.10.20.30.40.5
Cbar
Mbar
Sbar
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
0.00.10.20.30.40.5
Cbar
Mbar
Sbar
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
0.00.10.20.30.40.5
Cbar
Mbar
Sbar
Figure 22: TS10 k-means clustering with k= 3,4,5,6
EOS, BNB and TRX. The principal difference in results between this approach and the 6 cluster k-means is
that ETH and USDT are not differentiated here, making it more similar to the 5 cluster k-means results.

37
BTC
ETH
USDT
LTC
BCH
XRP
XLM
EOS
BNB
TRX
0.0 0.2 0.4 0.6 0.8 1.0
Complete Link
Agglomerative Coefficient = 0.84
z.f
Figure 23: TS10 Hierarchical sorting process (complete-link)
Correlations between the three meta-characteristics were also explored pairwise and are listed in Table 17.
Pearson’s correlation coefficient (Rho) can take values between positive one and negative one, representing
perfect positive and negative correlation of two datasets respectively. As would be expected from the prior
findings, in TS10 there is a high degree of positive correlation between Moneyness and Commodityness with
a Pearson’s Rho of 0.99, indicating that indeed Moneyness and Commodityness are highly related attributes -
base dupon the empirical data and subjective inputs to TS10 at least. Correlation of Securityness to Moneyness
and Commodityness are similar and strongly negative with values of -0.88 and -0.87 which is to be expected for
meta-characteristics which required dimensions to be inverted when used across taxonomies.
Meta-char S M C
S1 -0.878 -0.872
M-0.878 1 0.985
C-0.872 0.985 1
Table 17: TS10 meta-characteristic correlations
4.4 TSL7 : Using TokenSpace to Compare Cryptographic & Legacy Assets
A separate TokenSpace construction to TS10 will now be presented: TSL7, with the goal of exemplifying the
ability of TokenSpace to compare and contrast legacy assets alongside cryptoassets. No taxonomies have been
developed for this TokenSpace, instead relying on the TS10 data for cryptoassets and intuitively reasoned scores
for legacy assets. Tables for overall scores and each meta-characteristic are shown in Tables 18, 19, 20 and 21
with visual representation in Figure 24.
Apple stock (AAPL) represents an ideal type of a securitised asset with negligible monetary or commodity
attributes, with soy beans (SOY) and gold metal (GOLD) being canonical examples of consumable and non-

38
consumable commodities respectively. The US Dollar (USD) still possesses a strong Moneyness being the de
facto world reserve currency, though as is seen in §4.5 its Moneyness and Commodityness have been falling
due to governmental and central bank policy choices. One interesting observation is the near-equivalence in
TSL7 of the Moneyness of gold metal and Bitcoin (BTC). There appears to be a reversal of the perceived
premier commodity monetary good as human society continues to engage in technological and particularly
digital advances.
The “Lindy type time-dependence” indexed dimension values were considered to be universally low for all
cryptographic assets due to the lack of ecosystem maturity, with a “Lindy index” of 1 for gold, which has been
considered valuable by humans for no less than several thousand years [131].
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Commodityness
Moneyness
Securityness
BTC
ETH
XRP
SOY
GOLD
USD
AAPL
Figure 24: Example of legacy assets and cryptoassets inhabiting TSL7 TokenSpace
Asset (S) (M) (C)Notes
BTC 0.09 0.41 0.68
ETH 0.48 0.22 0.42
XRP 0.75 0.10 0.18
SOY (beans) 0.00 0.05 1.00
GOLD (metal) 0.00 0.40 0.95
USD 0.20 0.70 0.25
AAPL 1.00 0.05 0.00
Table 18: TSL7 scores listed to two decimal places

39
Securityness
Asset SNotes
BTC 0.09 Leaderless, permissionless
ETH* 0.48 “sufficiently decentralised”
XRP 0.75 Supply concentration & nodes insider-skewed, no validation reward,
missing ledger history
SOY (beans)** 0.00 Ideal type for non-securitised asset
GOLD (metal)** 0.00 Ideal type for non-securitised asset
USD 0.20 Reliance on faith in fiscal prudence of US Government & Federal Re-
serve
AAPL** 1.00 Ideal type for securitised asset
*see Hinman §1.3.3, **see Bailey §2.1
Table 19: Securityness scores for TSL7 listed to two decimal places
Moneyness
Asset MNotes
BTC 0.41* Post-bootstrap uncertainty
ETH 0.22** Not intended to be a monetary asset but has become an MoE and UofA
in some circumstances
XRP 0.10 Used as regulatory arbitrage vehicle and speculative asset with limited
utility. Central parties can censor
SOY (beans) 0.05 MoE restricted to barter, consumption or use as underlying for a deriva-
tive instrument.
GOLD (metal) 0.40 Non-standardised, prone to dilution, necessitates verification of mass
and purity
USD 0.70 Inflationary, with supply debasement (Triffin dilemma).
AAPL 0.05 Approaching ideal type of non-monetary asset, limited utility as MoE
*Uncertainty of post-bootstrap phase network security incentives
**Uncertain monetary policy, central influence, technical debt & future uncertainty
Table 20: Moneyness scores for TSL7 listed to two decimal places
Commodityness
Asset CNotes
BTC* 0.68 Ideal type of a digital commoditised asset
ETH 0.42 Used as a digital utility for token sales and persistent scripts
XRP 0.18 Censorability of payments and supply concentration among insiders too
great to be freely tradeable
SOY (beans)* 1.00 Ideal type of a material commoditised consumable asset
GOLD (metal)* 0.95 Ideal type of a material commoditised non-consumable asset
USD 0.25 Loss of gold peg, debasement of supply
AAPL* 0.00 Ideal type of a non-commoditised asset
*see Bailey §2.1
Table 21: Commodityness scores for TSL7 listed to two decimal places

40
4.5 TSTDX : Time-Dependence of Selected Assets
A third TokenSpace construction will now be presented. TSTDX has been constructed with the goal of exem-
plifying the ability of TokenSpace to map time-dependence of asset attributes. As with TSL7 no taxonomies
have been developed for this TokenSpace, instead relying on the TS10 data for cryptoassets in 2019 and in-
tuitively reasoned scores for present day scores of legacy assets and all historical values. Tabulation of overall
meta-characteristic scores are shown in Table 22 with visual representation in Figure 25. Intuitive judgement
was applied to give an indicative depiction of how a time-dependence TokenSpace such as TSTDX may be more
rigorously constructed in future.
A number of interesting observations can be made in TSTDX. It is apparent that monetary metals such
as gold (GOLD) and silver (SILVER) are decreasing in Moneyness with time as Bitcoin’s (BTC) increases
- ostensibly as the digitalisation of human society corresponds to favouring similarly digital (“simulacrised”)
money such as Bitcoin over specie.
The loss of gold and silver backing on moneys such as the British Pound (GBP) and the US Dollar (USD)
leading to loss of Commodityness, Moneyness and an increase in Securityness may also be rationalised as
derealisation - a loss of mimetic gravitas in addition to simulacrum-related societal sentiment. Related is the
loss of Moneyness of gold and silver over time, as these two metals have long been on a path to demonetisation.
In this respect, silver is some way ahead of gold, being largely a commodity rather than a commodity-money
in the present day.
The time-dependence of cryptographic assets generally shows a trend of decreasing Securityness as the net-
works mature and assets become more adopted, distributed, widely held, useful and used. In concert Moneyness
and Commodityness also tend to increase as more reasons to use, hold and transact with the assets emerge.
Ethereum (ETH) is particularly remarkable as - in tandem with Hinman’s summer 2018 sentiments as discussed
in §1.3.3 - what started as a securities offering of a centralised asset reliant on the efforts of a team of others
for speculative gain has become (to some extent) more widely used, useful, held and distributed hence leading
to a decrease in Securityness and increases in Moneyness and Commodityness. It could perhaps be said that
Ethereum in particular is well on the path to desecuritisation, or indeed may have arrived at that destina-
tion depending on where boundaries are perceived to lie in TokenSpace. The US Dollar (USD) still possesses
a strong Moneyness being the de facto world reserve currency, though as is seen in §4.5 its Moneyness and
Commodityness have been declining since the abandonment of gold-backing and the rise of the petrodollar
system.

41
Asset (S) (M) (C) Notes
BTC (2019) 0.09 0.41 0.68
BTC (2017) 0.13 0.33 0.60
BTC (2015) 0.19 0.25 0.50
BTC (2012) 0.24 0.15 0.35
BTC (2009) 0.30 0.07 0.20
ETH (2019) 0.48 0.22 0.42
ETH (2018) 0.55 0.20 0.32
ETH (2017) 0.62 0.18 0.23
ETH (2016) 0.70 0.13 0.15
ETH (2015) 0.75 0.03 0.10
XRP (2019) 0.75 0.10 0.18
XRP (2017) 0.77 0.08 0.15
XRP (2015) 0.79 0.07 0.12
XRP (2014) 0.81 0.06 0.11
XRP (2013) 0.85 0.05 0.10
LTC (2019) 0.24 0.19 0.34
LTC (2017) 0.29 0.17 0.30
LTC (2015) 0.35 0.14 0.25
LTC (2013) 0.40 0.11 0.17
LTC (2011) 0.45 0.08 0.10
GOLD (2019) 0.00 0.40 0.95
GOLD (1920) 0.00 0.50 0.92
GOLD (1820) 0.00 0.60 0.88
GOLD (1720) 0.00 0.70 0.85
GOLD (1620) 0.00 0.80 0.82
SILVER (2019) 0.00 0.20 0.75
SILVER (1920) 0.00 0.25 0.70
SILVER (1820) 0.00 0.35 0.65
SILVER (1720) 0.00 0.45 0.60
SILVER (1620) 0.00 0.60 0.55
USD (2019) 0.20 0.70 0.25
USD (1970) 0.15 0.75 0.33
USD (1920) 0.00 0.80 0.41
USD (1870) 0.00 0.43 0.44
USD (1792) 0.00 0.10 0.20
GBP (2019) 0.20 0.50 0.15
GBP (1900) 0.05 0.70 0.35
GBP (1800) 0.00 0.75 0.45
GBP (1700) 0.00 0.70 0.48
GBP (1600) 0.00 0.75 0.55
Table 22: TSTDX Time-dependent meta-characteristic scores,
listed to two decimal places

0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
Commodityness
Moneyness
Securityness
BTC2019
BTC2017
BTC2015
BTC2012
BTC2009
ETH2019
ETH2018
ETH2017
ETH2016
ETH2015
XRP2019
XRP2017
XRP2015
XRP2014
XRP2013
LTC2019
LTC2017
LTC2015
LTC2013
LTC2011
GOLD2019
GOLD1920
GOLD1820
GOLD1720
GOLD1620
SILVER2019
SILVER1920
SILVER1820
SILVER1720
SILVER1620
USD2019
USD1970
USD1920
USD1870
USD1792
GBP2019
GBP1900
GBP1800
GBP1700
GBP1600
Figure 25: Graphical representation of TSTDX TokenSpace
;

43
5 Discussion & Considerations Informing Further Development of TokenSpace
It is non-trivial to arrive at a robust classification framework employing categorical discrimination in tandem
with judiciously chosen and carefully optimised formulae for quantitative characteristics and this will be a focus
of future work, as will time-dependent scoring mechanisms and scoring ranges where there may be significant
disagreement or uncertainty over the optimal location of an asset in TokenSpace. Often token issuers engi-
neer their assets to have a perceived value proposition by apportioning future cash flows to the holders, via
mechanisms such as token burns, staking or masternodes which are coarsely analogous to share buybacks but
with critical and significant differences to the downside [132, 133]. Likewise masternodes, staking rewards or
issuer-sanctioned airdrops map coarsely to dividends in legacy finance. However, if a token is deemed to be too
security-like then exchanges may be reluctant to list for fear of future liability or compliance issues.
It is important to explicitly discuss the limitations of TokenSpace. For the purposes of a subjective classi-
fication system such as TokenSpace, as many attributes of cryptographic networks and assets are continuous,
exhibit subtle variations and / or edge cases, a mixture of categorical and numerical discrimination is most
likely the optimal approach. Therefore, the instantiations of TokenSpace which will demonstrate the most ex-
planatory power will be hybrids of traditional and phenetic taxonomy types. This design choice is justified by
the desired outcome of numerical scores as the output of the classification execution in order to populate asset
locations in the Euclidean 3D space that TokenSpace creates. Conversely in the interests of pragmatism, a great
deal of insight may still be derived from a primarily categorical classification approach with some range-bound
indices and if this meets the needs of the user then it is an acceptable and valid design choice. Further it min-
imises over-reliance on measurable attributes which may be subject to manipulation for motivations related to
decentralisation theatre.
As with all information systems, the principle of GIGO (Garbage In, Garbage Out) applies. A number of
potential pitfalls are as follows, and the author does not exclude oneself from susceptibility to any or all of these.
The use of misinformed judgement, lack of methodological rigour in taxonomy construction, over-estimation of
the researcher’s knowledge of the field or competence in applying taxonomic methodology, latent biases, poor
quality / misleading data sources or judgements and a lack of appreciation of edge cases or category overlap
may severely limit the usefulness of the TokenSpace produced and therefore its explanatory power.
It must be re-iterated yet again that TokenSpace affords a subjective conceptual framework for the compara-
tive analysis of assets. The meta-characteristic definitions and choices, dimensions, categories and characteristics
employed, score modifiers and / or weightings are all subjective and depend on the choices of the researcher
which derive from intended purpose. It is entirely realistic that an asset issuer may tailor their taxonomies, score
modifiers, regulatory boundary functions or a combination of the above to present a favourable assessment with
respect to their biases or motivations. Additionally, considering the changing nature of regulatory and compli-
ance landscape may have a large bearing on what can be considered to be acceptable asset characteristics in
compliance terms and may necessitate a re-evaluation of weightings and / or score modifiers [51].
As discussed in §3.3.2 with particular reference to the 2017 vintage of regulatory arbitrage mechanism of token
sales for so-called utility tokens, some distinction between “good” and “bad” securities, moneys or commodities
in an area of particular interest. Extending TokenSpace to occupy a region between -1 and +1 could provide a
coarse mechanism to do this, though the way that dimension scores and weightings are determined would have
to be adjusted and naive methods such as taking moduli do not sufficiently discriminate as to the quality of an
asset.
Future planned developments include the construction of TokenSpaces with higher dimensionality as dis-
cussed in §3.3.3, and alternative taxonomies for different meta-characteristics with intended purposes other than
increasing regulatory clarification. The scoring mechanisms as discussed in §3.3.4 and §3.3.5 including categor-
ical and indexed dimensions, score modifiers and weightings may also be further refined and extended. Other
approaches to generating asset coordinates for TokenSpaces will also be explored, with plans in place to form
“digital round tables” with broad subsets of stakeholders to arrive at asset scores or ranges.
Work is underway with collaborators to extend TokenSpace into DAOSpace in order to characterise similar-
ities and differences of “Decentralised Autonomous Organisations” as opposed to assets. One interesting nexus
of DAOSpace and TokenSpace is attempting to disentangle the design choices and properties of decentralised
organisations (and their native assets) with respect to Securityness in particular. As discussed in §1.3.3, the
SEC has already made it clear that TheDAO tokens (DAO) would be classified as securities and therefore profit-
oriented tokenised DAOs must be designed carefully with this in mind should they intend to be compliant with
existing regulations. Interestingly Malta has passed laws giving DAOs legal personality, meaning that another
cycle of jurisdictional arbitrage may be underway, this time with organisations as well as or instead of assets
(§1.1.1).
Likewise stablecoins with certain properties especially related to asset issuance may also be problematic from
a compliance perspective (§1.3.3) so a potential extension of this work towards a StablecoinSpace classification
framework for pegged assets is an avenue being explored currently.

44
A future goal of TokenSpace is the development of an environment which may be updated in real-time from
various information feeds from market, semantic / linguistic and / or network data in order to provide dynamic
information as to evolving asset characteristics as well as historical trends at varying points in time. This may
facilitate the goal of descriptive, explanatory and even predictive techniques for understanding, rationalising or
foreseeing trends, issues and opportunities relating to assets and networks before they become readily apparent
from na¨ıve analysis.

45
6 Acknowledgements
The author would like to extend sincere appreciation and thanks to Professor Sir Martyn Poliakoff, Professor
Boris Mamlyuk, Yuval Kogman and Nic Carter for helpful discussions and feedback during the preparation of
this manuscript.

46
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