Reasoning(Inductive, Deductive, and Abductive)

This reasoning topic is very important to understand and it also acts as a base for some AI(Artificial Intelligence) applications. It is a part of aptitude as well as AI domain.

Basically, there are 3 parameters for reasoning, and they are:

1. Rule
2. Cause
3. Effect

Inductive Reasoning:

It refers to the formation of rules, when we have cause and its effect. It can also be referred to providing a conclusion to a statement generally, after having some specific observations. For example, consider these two statements:

1. Basket contains Apples.
2. Apples are Tasty.

By using inductive reasoning, we can conclude from that Basket contains tasty apples. But, this will not hold in all the situations. There might be an apple in the basket which is not tasty.

Another example, consider the statement “If john plays, john sleeps”. Here the cause is “john plays” and the effect is “John Sleeps”. Now, we can form a rule from here that “whenever John plays, he Sleeps”. But, this will not hold all of the situations, because it might be possible that John has been doing another work and then he sleeps, or it is also possible that John is lazy.

So, this is also a drawback of inductive reasoning that it is always not true.

Deductive Reasoning:

It refers to the identification of effect, when we have a rule and a cause. It can also be referred to providing a specific statement when we have general statement. For example, consider these two statements:

1. Basket contains Apples.
2. All Apples are Tasty.

By using deductive reasoning, we can conclude from here that Basket contains tasty apples. This time our conclusion holds in every situation. There can not be any situation now in which our statement concluded (the effect we recognise) will fail.

Another example, consider the statement “John only likes eating Apples”. Here the cause is “John is eating Apple” and the rule is “John only likes eating Apples”. Now, we can easily come to the effect that “John will feel Happy/Good”, because as John likes eating apples only, and he is eating the same. So, he will feel happy.

Deductive reasoning has this advantage that it never fails in any situation, or it is valid for each and every situation.

Abductive Reasoning:

It refers to the identification of cause from a rule and effect. It can also be referred to providing a cause of the rule when we have the effect and rule.

For example, consider the rule “John is studying because he has to pass in exam”. Here , the effect is John is studying. From the rule, we can easily conclude the cause, which is “John has to pass in exam”. But, it also do not hold in every situation. There might be a case John started liking some subject and gained interest in it, due to this John will study.

So, abductive reasoning has also same disadvantage as inductive reasoning has, i.e. not true in every situation.

Combination of all the above 3 reasoning:

We can see the best example of combination in Data Science. For Example, we have data, we use abductive reasoning to find out the cause of the data, or to give explanation of data. Then, we use inductive reasoning for mapping inside data or generating some rules inside data. At last we use deductive reasoning for prediction.

This is all I have for the explanation of the concepts. I hope you guys liked it and understood well.

For more content on this please visit https://medium.com/@harshitdawar/reasoning-inductive-deductive-and-abductive-fc6c4eb30fec