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
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.
A Knowledge Map can be be seen as a Generalization of Knowledge graphs on Preferences where its traversal has associated Discrimination and Uncertainty costs and can be built iteratively even from a Brainstorming procedure. #KnowledgeMaps#GKB
A #KnowledgeMap (KM) is a (possibly partial and globally agreed) implementation of a Social Discrimination function in a Social Discrimination Space of n dimensions evaluating Uncertainty and Discrimination.
KM(Absolute Majority)=U<<1 and D>>0 what means that although it’s widely accepted it’s far from non discrimination of other (losing) preferences, candidates and the real losers of the game: Non-Politicians. Namely, you and me (We).
Contrarily, KM(Less-discriminatory Majority) is U<<1 and D<<1. You name it. It should deserve more study (and use) as it could be the first Social Law (de facto, for the time being) in the range of KM(Gravity Law) where U=0 and D=0.
Collaborative Knowledge Evolution can be seen as the Open Standardization and Integration of Open Knowledge, optimized from generation to generation thanks to Human Evolution.
To start with, it might be simulated in Artificial Societies through Inclusive Social Reality implementation, and then after the required (although not precisely the desired) pass of generations it could be implemented in Research and Socio-economic Systems.
“Salud, Consenso e Integración para todos: #SCI-ence4all”.
(“Health, Consensus and Integration for everyone: #SCI-ence4all” Translated from Spanish.)
We might design the (Digital and SCI-ence) Fair Society of the XII Century from now on.