Example where Neutrosophic Logic works, but Fuzzy Logic does not work

Example where Neutrosophic Logic works, but Fuzzy Logic does not work

Neutrosophic logic is an extension of fuzzy logic. In fuzzy logic a proposition is t% true and f% false, t + f = 1. In neutrosophic logic a proposition is t% true, i% indeterminacy (neutral, i.e. neither true nor false), and f% false. For example, in games based on 3 possibilities (win, tie, loose) you can use the neutrosophic logic better than the fuzzy logic. Let’s consider the proposition P about a future scheduled soccer game between USA and

Argentina: "USA will win against Argentina". The experts can predict that P has the following truthvalues: (0.5, 0.1, 0.4) meaning that the chance that USA wins is 50%, the chance the USA has tie game with

Argentina is 10%, while the chance that USA loses against Argentina is 40%. We cannot characterize this game better in fuzzy logic, since in fuzzy logic you do not have Indeterminacy (=

Neutrality, i.e. tie game). The word "neutrosophic" comes from this middle component Indeterminacy (or Neutrality), meaning neither true/winning nor false/loosing, which is not used in fuzzy logic (not even in intuitionistic fuzzy logic it is defined, only what remains may be…).