  The networking field has recently started to incorporate artificial
intelligence (AI), machine learning (ML), big data analytics combined with
advances in networking (such as software-defined networks, network functions
virtualization, and programmable data planes) in a bid to construct highly
optimized self-driving and self-organizing networks. It is worth remembering
that the modern Internet that interconnects millions of networks is a `complex
adaptive social system', in which interventions not only cause effects but the
effects have further knock-on effects (not all of which are desirable or
anticipated). We believe that self-driving networks will likely raise new
unanticipated challenges (particularly in the human-facing domains of ethics,
privacy, and security). In this paper, we propose the use of insights and tools
from the field of "systems thinking"---a rich discipline developing for more
than half a century, which encompasses qualitative and quantitative nonlinear
models of complex social systems---and highlight their relevance for studying
the long-term effects of network architectural interventions, particularly for
self-driving networks. We show that these tools complement existing simulation
and modeling tools and provide new insights and capabilities. To the best of
our knowledge, this is the first study that has considered the relevance of
formal systems thinking tools for the analysis of self-driving networks.
