This is a major point, as it is less loss in fidelity than a gain in awareness of uncertainty. Any location, even a theoretical point, comes with uncertainty when established in a real world context (e.g. based on any observation based data). In that respect all vector data have limited spatial fidelity as well. Unfortunately their uncertainty remains often hidden by 'false' precision of their location data. A realistic estimation of the inherent (spatial) uncertainty of input data and required (spatial) uncertainty of output data, both expressed in respective choices of cell width would largely benefit spatial sciences. Inherently enforcing this is a major advantage of DGGS, not at all a downside!
I have changed "some loss of fidelity" to "some apparent loss of fidelity" to emphasise your point, which I heartily agree with. I have changed one other statements in the same paragraph.
This is a major point, as it is less loss in fidelity than a gain in awareness of uncertainty. Any location, even a theoretical point, comes with uncertainty when established in a real world context (e.g. based on any observation based data). In that respect all vector data have limited spatial fidelity as well. Unfortunately their uncertainty remains often hidden by 'false' precision of their location data. A realistic estimation of the inherent (spatial) uncertainty of input data and required (spatial) uncertainty of output data, both expressed in respective choices of cell width would largely benefit spatial sciences. Inherently enforcing this is a major advantage of DGGS, not at all a downside!