A Signal Theory approach to Knowledge maps

To reduce Uncertainty, the application of the solutions contained in the Knowledge map will be more resistent to unexepected events as the Knowledge map grows.

Imagine an Analog to Digital Converter (ADC, namely a Researcher) that processes the Signals of the Analog Social Reality and converts them using the appropriate Nyquist rate (2 times the desired Bandwith) into Signals of the Digitial Social Reality (namely a Knowledge map). https://en.wikipedia.org/wiki/Nyquist-Shannon_sampling_theorem

The signal-to-noise and distortion ratio (SINAD) would measure the quality of the Signal. https://en.wikipedia.org/wiki/SINAD

While converting from Analog to Digital, it is commonly know that the SINAD is higher (namely the quality of the Digital Signal is higher, also because it is less error-prone due to it will be computed by a Deterministic Turing Machine with very low Uncertainty).

While converting from Digital to Analog,(namely People voting and acting) the quality of the signal is guaranteed due to the Nyquist rate.

In the hypothetical case of interferences to the Analog Signal might appear there would be pre-established Control Signals in the Knowledge map (namely, sanctions and correction courses of action) to mitigate these interferences and ensure the Analogue Signal arrives correctly to Destination.

Then, the more Signals and Control Signals stored, the wider the range of effectiveness of Knowledge maps. However, Control Signals harnessing Human Autonomy should be limited and finding a trade-off enough for all the Humans to arrive to Destination while carrying the Analogue Signals will be needed.

This trade-off will be found following the Less-discriminatory Majority rule.

Author: Andrés García-Camino

Ph.D. in A.I.

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