This is the second in a series of pieces focussing on TokenSpace, a novel conceptual classification framework for cryptographic assets. This Q&A provides some additional background. If you need more answers than these two pieces provide, get in touch to be a lucky proof-reader of the manuscript.

TokenSpace 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 Sbar, Mbar and Cbar. 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 ) co-ordinates (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.

Sbar, Mbar and Cbar correspond to intuitively reasoned assignments of subjective classificatory meta-characteristics Securityness, Moneyness and Commodityness which together form the basis of TokenSpace classification
methods currently in development. 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.

Definitions of the proposed meta-characteristics:
Sbar — Securityness. The extent to which an item or instrument qualifies as or 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.
Mbar — Moneyness. The extent to which an item or instrument qualifies as or exhibits characteristics of a monetary asset.
Cbar — Commodityness. The extent to which an item or instrument qualifies as or exhibits characteristics of a commoditised asset.

Example scores for a range of assets are outlined in the tables below with visual depiction in Figure 2. Ideal types are postulated canonical examples of particular asset types and are discussed in Section 2 of the manuscript. 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 3. Parallel Industries is developing 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.