  More than 1000 distributed ledger technology (DLT) systems raising $600
billion in investment in 2016 feature the unprecedented and disruptive
potential of blockchain technology. A systematic and data-driven analysis,
comparison and rigorous evaluation of the different design choices of
distributed ledgers and their implications is a challenge. The rapidly evolving
nature of the blockchain landscape hinders reaching a common understanding of
the techno-socio-economic design space of distributed ledgers and the
cryptoeconomies they support. To fill this gap, this paper makes the following
contributions: (i) A conceptual architecture of DLT systems with which (ii) a
taxonomy is designed and (iii) a rigorous classification of DLT systems is made
using real-world data and wisdom of the crowd. (iv) A DLT design guideline is
the end result of applying machine learning methodologies on the classification
data. Compared to related work and as defined in earlier taxonomy theory, the
proposed taxonomy is highly comprehensive, robust, explanatory and extensible.
The findings of this paper can provide new insights and better understanding of
the key design choices evolving the modeling complexity of DLT systems, while
identifying opportunities for new research contributions and business
innovation.
