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I have two arguments which I want to combine. Depending on the way I do it I get different results.

Argument #1

P1) If a person is A, then it's likely that that person is also B.

P2) This person is A.


C) This person is likely to be B.

Here, "likely" is interpreted as having a probability greater than 0.5. Using conditional probability we can write P1 as

Pr(B|A) > 0.5

This argument is deductively valid. If our premises are true we can confidently say that the person is more likely to be B than not.

Argument #2

P1) If a person is C, then it's likely that that person is also B.

P2) This person is C.


C) This person is likely to be B.

Just like with the first argument we can write P1 as

Pr(B|C) > 0.5

And again, taking this argument in isolation and assuming the premises are true, we can say that the person in question is more likely to be B than not.

Finally, let's assume that we have both arguments and their second premises refer to the same person. Is it likely that that person is B? If I use arguments' conclusions as premises for my third argument then the inference is trivial.

Argument #3

P1) This person is likely to be B. (From argument #1.)

P2) This person is likely to be B. (From argument #2.)


C) This person is likely to be B.

I expect to come to the same conclusion if I combine premises from arguments #1 and #2.

Argument #4

P1) If a person is A, then it's likely that that person is also B.

P2) This person is A.

P3) If a person is C, then it's likely that that person is also B.

P4) This person is C.


C) This person is likely to be B.

However, if I try to prove this using conditional probabilities I can't get a definitive answer. What we need to prove now is that Pr(B|A,C) > 0.5. However, as was shown in Information paradox: the more we know, the less confident we are, premises

P1) Pr(B|A) > 0.5 and

P3) Pr(B|C) > 0.5

do not guarantee that

Pr(B|A,C) > 0.5

There must be a mistake in the way argument #3 or argument #4 is constructed but I don't see it. Or my assumption that the two ways of argument construction are equivalent may be wrong. So why the conclusions are different?

  • You are confusing deductive and inductive reasoning. The is no probability in deductive reasoning whatsoever. Deductive reasoning is absolute and no chance for more than two possible answers. This is not so in math or science. You cannot have a valid inductive argument by definition. Validity only refers to deductive arguments and you have probabilities directly in your premises. Inductive reasoning deals with probabilities. At best you can have a strong or weak inductive argument as written. Science is more inductive than humanities subject. By definition science is induction. – Logikal Apr 2 '18 at 11:14
  • @Logikal, when I asked this question I knew the definitions of inductive and deductive reasoning. Therefore, restating those definitions does not help me to answer the question. If you could apply your knowledge to the arguments I provided and point out where the confusion lies that would be helpful. – Vlad Apr 3 '18 at 21:52
  • @ Vlad, If you knew the correct definition or terminology then you would know your question makes no sense. You can not include probability in deductive logic because probabilities are not absolute. Can you explain why you think that you can do so and everything is alright? Perhaps you have good reason. – Logikal Apr 3 '18 at 23:51
  • If you leave the arguments as they are you will only have probable answers. The highest percent being rounded to a whole number would be 99 percent. This is what science does -- -- not deductive logic – Logikal Apr 4 '18 at 0:00
  • @Logikal Firstly, regarding my knowing that the question makes no sense. The reason why I asked that question is because it makes sense to me and I don't know the answer. Unless you are accusing me of deliberately asking nonsensical questions, your comment contradicts the evidence. Secondly, from your allegation that I knew that my question made no sense I conclude that it does not make sense to you. For me, it is an indication that you might not have understood my question. If you can articulate in which way it does not make sense to you I'll be happy to provide further clarifications. – Vlad Apr 5 '18 at 4:49
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Ven Diagram For Example The apparent difficulties in this argument arise from the way the words and symbols that you are using are working together to create an imprecise discussion regarding the probabilities of picking an individual from a group versus the probabilities of looking at the traits of a certain individual. We get in trouble when we don't think precisely about the difference between these two statements: 1) I am going to pick an individual from a group and the probability they have various traits is X. 2) I have an individual in front of me, the probabilities this person has these trait is X. The symbols that you are using do not sufficient context to make this distinction on their own. The picture below is a Ven diagram depicting a situation where the three conditional statements can all be true. (Those three are underlined.)

I suspect there is a convention using subscripts that could solve the problem. But the following tries to make clear the distinction you would have to capture in the symbols for the symbolic argument not to give you a problem.

Now lets translate your conditional probabilities into words that are less problematic:

The probability that I pick any individual from this set and they have trait B, given they came from set A, is greater than 50%. (51%) The probability that I pick any individual from this set (NOT NECESSARILY THE SAME INDIVIDUAL!) and they have trait B, given they came from set C, is greater than 50%. (52%) The probability that I pick any individual from this set (NOT NECESSARILY THE SAME INDIVIDUAL!) and they have trait B, given they came from a set of individuals that are both A&C, is less than 50%. (20%)

Were we to tell the story of one individual the statements could look like this:

The probability that I pick an individual from this set and they have trait B, given they came from set A, is greater than 50% (51%). The probability that THAT THAT SAME INDIVIDUAL has trait B, given I add the information that they have trait C is less than 50%. (20%). The probability that THAT THAT SAME INDIVIDUAL has trait B, given I redundantly tell you that they have trait A&C is less than 50%. (20%).

OR to make a similar contrast

I have an individual in front of me. I have picked them from this group of 345 people. (see diagram) When I tell you they have trait A, what is the probability that they have trait B? Answer: 51% Forget I told you that they have trait A. When I tell you they have trait C, what is the probability that they have trait B? Answer: 52% Forget I told you that they have trait A or C. When I tell you they have trait A and trait C, what is the probability that they have trait B? Answer: 20%

versus

I have picked them from this group of 345 people. (see diagram) When I tell you they have trait A, what is the probability that they have trait B? Answer: 51% Now don't forget what you already know. When I now tell you they have trait C, what is the probability that they have trait B? Answer: 20% Now don't forget what you already know. When I tell you they have trait A and trait C (WHICH I ALREADY TOLD YOU), what is the probability that they have trait B? Answer: 20%

  • your answer explains the difference between considering two pieces of evidence independently and adding evidence incrementally, as in Simple Principle of Conditionalization. The results of those two approaches are, not surprisingly, the same. However, you do not address construction of Argument #3 which does not use A or C. On the surface it always confirms that the person is likely to be B. I believe the problem is with the way that argument is interpreted and I will explain it in my answer. – Vlad Apr 2 '18 at 0:08
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You're missing information on whether or not A AND C is representative of A and representative of B. I.e. it could be that A AND C represents 49% of A, and 49% of C, and that NONE of them are B.

Let's try an example:

A are Probably B, C are probably B, Therefore (A and C) are probably B

E.g. A = American Females, B = 'White', C = American Politicians

An American Female is probably White, An American Politician is probably White, Therefore A Female American Politician is probably White

Is obviously not necessarily true. Unless you add something like "American female politicians are probably white"

  • Your example, @jeffUK, is correct, and it's exactly the reason for my question. I don't understand why two seemingly valid ways of constructing an argument produce inconsistent results. – Vlad Mar 30 '18 at 0:29
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I'll throw my hat on the ring and say that argument 4 sends problematic, because your are (unnecesarily?) reversing causal relationship between attributes.

Does that make any sense to you?

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    Can you provide more details? I can't see a reversal. Also, the conditional probabilities do not require a causal relationship between a condition and an event. – Vlad Mar 30 '18 at 0:25
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I think the problem is with Argument #3. The reason why that argument seems to always confirm that the person is likely to be B is in the way we interpret it.

Firstly, we need to recognise that the premises are descriptions of two different findings. Although they look exactly the same they do not necessarily represent the result of the same assessment or measurement.

Secondly, the term "likely" is vague. It indicates that a probability of an event is more than 0.5 which gives us a range of possible values. The actual values hidden by the quantifier "likely" may be different in each premise.

Given these two points, let's imagine a situation in which both premises are true and we know the actual probabilities are different in each case. If we had those probabilities we could refine Argument #3 like this

P1) Probability of this person being B is x.

P2) Probability of this person being B is y.


C) This person is likely to be B.

where x>0.5, y>0.5 and x is not equal y.

In such case, the argument would be valid because the conjunction of the premises is always false, but it would not be sound. This situation is possible because we lose information about the person by not mentioning conditions A and C in Argument #3.

In summary, the apparent soundness of Argument #3 is caused by imprecise description of prior findings where specific values are replaced by vague quantifiers.

  • Deductive reasoning has rules in how propositions are used in arguments. You aknowledge the premises are vague and written poorly. The argument cannot be valid for the reasons you state: vagueness and equivocation of the description of the findings. Different propositions can not be written identically and mean something else: That is pure deception to do so. I am guessing that you might be repeating an argument given to you. An invalid argument can yield any conclusion. – Logikal Apr 4 '18 at 0:11

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