This research provides an investigation of the phenomenon of mutual shaping between technologies and law, its ontological explanation in the interconnected transdisciplinary abstractions of complex systems (“design work”, “memory”, “attractor”, “shaping channel” and their sub-abstractions), while addressing knowledge gaps in the fields of Socio-Technical Systems, Information Systems and Legal Informatics. The dissertation also provides a computational systems approach, generated bottom up from qualitative data in application of the abstractions for their recursive improvement.
The corresponding approach extensively exploits the properties of the narrative form of qualitative data for its parallel tagging, time-relevant visualization, meaning-based clustering and relationship extraction. This approach consists of nine categories of conditional rules with an open ending for their replication to N number of datasets and enhanced learning from real-world decisions, professional practices and opinions. Technologies and law are conceptualized symmetrically, though processed asymmetrically due to their natural differences and experience of time flow.
The solution explores ongoing and existing ICT design works within the shared legislative environment, where the legal uncertainties shape and are shaped by the responsiveness of technological implications in their diversity and uniqueness. In addition, the computational approach indicates “ex ante” effects of accumulated information across ICT design works. The diversity of expert knowledge, professional affiliation and perspectives is respected in their originality, and the approach may subsequently be designed into an Intelligent Decision Support System within language technologies. The logic of computation, as designed in this research, may potentially be applied for exploration of other qualitatively diverse, but overlapping spheres of life in their co-evolution, because the rules of the approach do not contain any topic-based conditions.
Further research would imply testing steadily emerging hypotheses and sequences gained from findings on the implementation of the rules to the datasets, which would deepen the connections among the inseparable computational abstractions regardless the chosen data contexts, but learning from them in particular. This would enrich the logic of analysis and synthesis while answering the question how technologies and law mutually shape each other: practically, theoretically, visually and computationally.