Partition Theorem Homework: Finding Probability of Lying

In summary, the probability that a claimant is lying is 0.62, and the probability that a claimant is truthful is 0.36.
  • #1
Philip Wong
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Homework Statement


Assume that it is appropriate to transfer the probabilities IP(F|L) and IP(F|T) from the police context to the insurance context.
Define the following new events for the insurance context:
L = “insurance claimant is lying”;
T = “insurance claimant is truthful”;
F = “insurance claimant failed lie-detector test on phone”;
P = “insurance claimant passed lie-detector test on phone”.
An insurance company finds that a massive 52.5% of claimants fail the liedetector
test on the phone. What is the probability that a claimant is actually
lying?

IP(F) = 0.525 IP(P) = 1-0.525=0.475
IP(F|L)=0.38 IP(F|T)=0.23
IP(P|L)=0.14 IP(P|T)=0.25

Homework Equations


Bayesian TheoremP(B |A) = P(A|B)P(B)/ P(A)
IP(L) = IP((L|F) [itex]\cap[/itex] (L|P))


The Attempt at a Solution


IP(L|F)=(0.38*0.525) / (0.38*0.525+0.23*0.525)=0.1995/0.32025=0.62
IP(L|P)=(0.14*0.475) / (0.14*0.475+0.25*0.475)=0.0665/0.18525 = 0.36

IP(L) = IP((L|F) [itex]\cap[/itex] (L|P))
= IP(L|F) * IP(L|P)
= 0.62 * 0.36 = 0.2232

is my workings right? I'm kind of worried that I used the wrong formula to work out IP(L), so it would be nice if someone could double check that part too
thanks
 
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  • #2
Though I haven't checked numbers, your use of Bayes to get IP(L|F) and IP(L|P) is on the right track, and given the results show someone who fails the test is likely to be lying (62%) or someone who passes the test is unlikely to be lying (36%) is encouraging

I don't understand what you've done in the last step however. I would notice (or assume) that the events P&F span the entire probabilty universe, and use the following:

IP(L) = IP(L|F)IP(F) + IP(L|P)IP(P)
 
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  • #3
lanedance said:
Though I haven't checked numbers, your use of Bayes to get IP(L|F) and IP(L|P) is on the right track, and given the results show someone who fails the test is likely to be lying (62%) or someone who passes the test is unlikely to be lying (36%) is encouraging

I don;t understand what you've done in teh last step however. I would notice (or assume) that the events P&F, span the entire probabilty universe and use the following:

IP(L) = IP(L|F)IP(F) + IP(L|P)IP(P)

you are right, I just figured it out about half an hour ago. I knew I had implied some of the formula wrongly.

Thanks for confirming that with me!
 

1. What is the Partition Theorem and how is it used to find the probability of lying?

The Partition Theorem is a mathematical concept that is used to find the probability of an event occurring given that another event has already occurred. In the context of lying, it can be used to find the probability that a person is lying based on their behavior or statements.

2. How does the Partition Theorem take into account different factors that may affect the probability of lying?

The Partition Theorem takes into account the different factors that may affect the probability of lying by breaking down the event of lying into smaller, more manageable events. These smaller events are then used to calculate the overall probability of lying.

3. Can the Partition Theorem be applied to all situations involving lying?

Yes, the Partition Theorem can be applied to all situations involving lying as long as there is a clear event that can be used as a basis for calculating the probability of lying.

4. How can the Partition Theorem help in understanding patterns of lying behavior?

The Partition Theorem can help in understanding patterns of lying behavior by providing a systematic approach to calculating the probability of lying. By breaking down the event of lying into smaller events, it allows for a more comprehensive analysis of the behavior and potential patterns that may emerge.

5. Are there any limitations to using the Partition Theorem in determining the probability of lying?

While the Partition Theorem can be a useful tool in determining the probability of lying, it does have its limitations. It relies on the accuracy and reliability of the information used to calculate the probability and may not take into account other factors that may affect the behavior of lying. Additionally, it may not work in situations where there is a lack of clear events or data to use for the calculations.

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