Deductive logic is a process whereby general principles may be derived from specific claims. For example- all fish swim in water. All trout are fish. Therefore, all trout swim in water. However, there are difficulties with this method of analysis. It does not permit the analysis or drawing of a conclusion where the specific claims cannot necessarily be drawn; and further, where those claims are of dubious veracity, the accuracy of the general principles drawn is debatable.
Hence the principle of inductive logic, which is based on two essential principles (that expand deductive logic, essentially); firstly, that an attribute of a specific object can be applied to all objects in a specific class (this ice is cold; all ice is therefore cold); and that specific behavior occurs in patterns which will continue to occur (we eat dinner at 6:00 today; we will eat dinner at 6:00 tomorrow.) Obviously, there are problems with inductive logic. Take for example the following inductive claim: “All observed apples are red. Therefore, all apples are red.” Unless one can positively ensure that all apples are red, the claim may actually be false.
Synthesis is the combination of two claims or pieces of information in order to establish new elements. For example, the deductive logical claim “Water freezes below zero degrees. It is below zero degrees. Therefore, water will freeze.”, when added to the claim “There is water here” when synthesized leads to the conclusion that “This water is frozen.”
The problem with a reliance on strict deduction should be obvious at this point, as David Hume argued; it is an inductive logical principle that bread will continue to nourish us from day to day because it has in the past. Deductive logic relies upon the fact that one has total knowledge of the datasets relied upon.
However, in the real world, strict deductionism is a dangerous ploy. The principles of inductive logic and synthesis can be applied to a situation for the purposes of prediction and risk analysis, whereas deduction requires either the narrowing of the dataset to an extreme extent or expansion of knowledge appropriately.
For example, take the argument that “If I take cyanide, I will die.”
In order for deductive logic to agree with this conclusion, one must have total data in this respect about the dataset; one can either accomplish that by leaving the dataset unlimited and increasing knowledge, or by narrowing the dataset and thereby narrowing the amount of knowledge required. Specifically, one could attempt to poison every person with cyanide and thereby claim that “Cyanide kills all humans. I am a human. Therefore, if I take cyanide, I will die.” Obviously, this is not possible. In the alternate, one could take cyanide, and if one dies, the logical issue will be a tautology- taking cyanide will have resulted in one’s death.
For inductive logic to agree with this conclusion, however, the issue is much simpler. One could argue, instead, that “All observed takers of cyanide are dead. Therefore, cyanide kills and humans.” Obviously, there are flaws in the above argument; namely, all people in the past have died. This does not, however, necessarily, establish that people will continue to die, and further, it does not draw the distinction that those dead may have died from something other than cyanide. Indeed, there is a requirement to knowledge the inductive statements to be as specific as possible to ensure accuracy.
The difference, however, is that inductive logic allows for the prediction of behavior of a class without actually measuring the entirety of the class; and therefore allows one to predict the results of an action without actually taking that action. Those who rely on strict deductionism for their conclusions are either lying or disturbed. However, it is not uncommon for individuals to rely on inductive logic for their own behavior and everyday lives and yet to require deductive proof of claims they disagree with when presented to them- an ironically hypocritical point of view.