UPSC MainsMANAGEMENT-PAPER-II20164 Marks
Q2.

Question 2

The probabilities of X, Y and Z becoming managers are 4/9, 2/9 and 1/9 respectively. The probabilities that Bonus Scheme will be introduced if X, Y and Z become managers are 3/10, 2/5 and 1/2 respectively. What is the probability that Bonus Scheme will be introduced ? And if Bonus Scheme has been introduced, what is the probability that the manager appointed was Y?

How to Approach

This question tests the application of basic probability concepts in a managerial context. The approach should involve calculating the overall probability of the Bonus Scheme being introduced using the law of total probability. Subsequently, Bayes' theorem should be applied to determine the conditional probability of Y being the manager given that the Bonus Scheme has been introduced. A clear, step-by-step calculation is crucial, along with a concise explanation of the underlying principles.

Model Answer

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Introduction

Probability theory is a fundamental tool in decision-making under uncertainty, widely used in management and risk assessment. This question presents a scenario where the introduction of a Bonus Scheme is contingent upon the appointment of a specific manager – X, Y, or Z. Understanding the probabilities associated with each manager’s appointment and their respective likelihood of introducing the scheme allows for a comprehensive evaluation of the overall probability of the scheme’s implementation. Furthermore, it explores the reverse probability: given the scheme is implemented, what is the probability that Y was the manager responsible?

Part 1: Probability of Bonus Scheme Introduction

Let B be the event that the Bonus Scheme is introduced. We are given the following probabilities:

  • P(X) = 4/9 (Probability of X becoming manager)
  • P(Y) = 2/9 (Probability of Y becoming manager)
  • P(Z) = 1/9 (Probability of Z becoming manager)
  • P(B|X) = 3/10 (Probability of Bonus Scheme given X is manager)
  • P(B|Y) = 2/5 (Probability of Bonus Scheme given Y is manager)
  • P(B|Z) = 1/2 (Probability of Bonus Scheme given Z is manager)

We can use the law of total probability to find P(B):

P(B) = P(B|X)P(X) + P(B|Y)P(Y) + P(B|Z)P(Z)

P(B) = (3/10)(4/9) + (2/5)(2/9) + (1/2)(1/9)

P(B) = 12/90 + 4/45 + 1/18

P(B) = 12/90 + 8/90 + 5/90 = 25/90 = 5/18

Therefore, the probability that the Bonus Scheme will be introduced is 5/18.

Part 2: Probability that Manager was Y, given Bonus Scheme was Introduced

We need to find P(Y|B), the probability that Y was the manager given that the Bonus Scheme was introduced. We can use Bayes' theorem:

P(Y|B) = [P(B|Y)P(Y)] / P(B)

We already know:

  • P(B|Y) = 2/5
  • P(Y) = 2/9
  • P(B) = 5/18

So:

P(Y|B) = [(2/5)(2/9)] / (5/18)

P(Y|B) = (4/45) / (5/18)

P(Y|B) = (4/45) * (18/5)

P(Y|B) = (4 * 18) / (45 * 5) = 72 / 225 = 8/25

Therefore, if the Bonus Scheme has been introduced, the probability that the manager appointed was Y is 8/25.

Conclusion

In conclusion, the probability of the Bonus Scheme being introduced is 5/18, and given its introduction, the probability that manager Y was responsible is 8/25. This demonstrates the application of fundamental probability principles – the law of total probability and Bayes’ theorem – in a practical managerial scenario. These calculations provide valuable insights for decision-making and risk assessment within organizations.

Answer Length

This is a comprehensive model answer for learning purposes and may exceed the word limit. In the exam, always adhere to the prescribed word count.

Additional Resources

Key Definitions

Law of Total Probability
The law of total probability states that if a set of mutually exclusive and exhaustive events (A1, A2, ..., An) exists, then the probability of an event B can be calculated as the sum of the probabilities of B occurring with each of the events Ai: P(B) = Σ P(B|Ai)P(Ai).
Bayes' Theorem
Bayes' Theorem describes how to update the probability of a hypothesis based on new evidence. It is expressed as: P(A|B) = [P(B|A)P(A)] / P(B), where P(A|B) is the posterior probability, P(B|A) is the likelihood, P(A) is the prior probability, and P(B) is the marginal likelihood.

Key Statistics

According to a 2022 report by Deloitte, 79% of organizations are using data analytics to improve decision-making, highlighting the increasing importance of probabilistic reasoning in business.

Source: Deloitte, "The State of Analytics 2022"

A study by McKinsey found that companies that embrace data-driven decision-making are 23 times more likely to acquire customers and 6 times more likely to retain them (as of 2018).

Source: McKinsey, "The next frontier for data-driven value"

Examples

Medical Diagnosis

In medical diagnosis, the probability of a patient having a disease (event B) can be calculated using the law of total probability, considering the probabilities of having certain symptoms (events A1, A2, ...) and the probability of the disease given each symptom.

Frequently Asked Questions

What if the events (X, Y, Z becoming managers) were not mutually exclusive?

If the events were not mutually exclusive, the law of total probability would need to be adjusted to account for the intersections between the events. The calculation would become significantly more complex, requiring information about the joint probabilities of the events.