Quasi-Quanta Logic 2
Parker Emmerson · Zenodo (CERN European Organization for Nuclear Research) · 2023
We consider 7 potential reasons why a parameter might not quite be able to be quantified: 1. Insufficient data – There may be insufficient data to provide enough information or insight into the parameter. 2. Lack of previous research – If a certain field of study in which a parameter is to be studied is relatively new or not well researched, it will be difficult to define how the parameter should be quantified. 3. Complexity of parameter – In some cases, the parameter or topic being studied may be too complex to fit into a set criteria or qualification. 4. Diversity of sources – A single parameter may be difficult to quantify if there are multiple sources providing unique information about the topic. 5. Paradoxical nature – In some cases, the parameter in question may actu- ally be paradoxical in nature, making it more difficult to quantify. 6. Subjectivity and bias – If the parameter being studied can be biased or subjective, it can be difficult to quantify it in an objective way. 7. In some cases, a parameter may be considered to be away from infinity, meaning that the parameter has a starting point or an end point but no finite value between the two points. In this case, it can be difficult to quantify because a specific number can’t be assigned to this parameter. 1. x, P(x)N (where N represents the number of available data points) 2. x, P(x)Y (where Y represents the number of researches in the field) 3. x, P(x)C (where C represents a complexity that cannot be represented in numerical form) 4. x, P(x)S (where S represents a set of standards under which the parameter is to be evaluated) 5. x, P(x)P (where P represents a paradoxical nature that cannot be quantified) 6. x, P(x)O (where O represents the subjective or biased opinion of the individual studying the parameter). 7. x, P(x) ¿ (where indicates the parameter has a starting or end point but no finite value between the two points).