Exploring the Probability of the Last Passenger Getting Their Assigned Seat in Python

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💡 Problem Formulation: Imagine the scenario where an airplane with 100 seats has passengers boarding randomly, except for the last person. This person always boards last and has a specific assigned seat. We want to calculate the probability of this last passenger ending up in their assigned seat after all preceding passengers might have taken random seats, potentially initiating a chain of seat shuffling. What we seek is a Python program that accurately computes this probability given the chaos in boarding.

Method 1: Simulation with Iteration

This method involves simulating the boarding process multiple times to approximate the probability. Each simulation randomly assigns seats to all passengers except the last one and checks whether the last passenger can sit in their assigned seat. After a large number of iterations, the method provides a reliable estimate of the probability.

Here’s an example:

import random

def simulate_airplane_seating(passenger_count, simulations):
    last_seat_prob = 0
    for i in range(simulations):
        seats_taken = set()
        for j in range(passenger_count - 1):
            seat = random.choice([s for s in range(1, passenger_count + 1) if s not in seats_taken])
            seats_taken.add(seat)
        if passenger_count not in seats_taken:
            last_seat_prob += 1
    return last_seat_prob / simulations

# Example with 100 passengers and 10,000 simulations
print(simulate_airplane_seating(100, 10000))

The output will be a floating-point number close to 0.5 (for large values of simulations), representing the estimated probability.

This code snippet conducts numerous boarding simulations to calculate the probability using randomness and iteration. The simulate_airplane_seating() function takes in the passenger count and the number of simulations to run, iterating through the passengers and assigning them seats. If the last seat (intended for the last passenger) is not taken, the occasion is counted towards the final calculation of probability.

Method 2: Analytical Approach

The analytical method solves the problem by using combinatorics and logical reasoning to derive the probability mathematically, instead of relying on simulations. This approach provides an exact answer, rather than an estimate based on repeated trials.

Here’s an example:

def calculate_exact_probability(passenger_count):
    if passenger_count == 1:
        return 1
    return 1/passenger_count + (passenger_count - 2)/passenger_count * calculate_exact_probability(passenger_count - 1)

# Example with 100 passengers
print(calculate_exact_probability(100))

The output will be the exact probability, which should be 0.5 for any passenger count greater than 1.

This snippet contains a recursive function calculate_exact_probability() that uses an analytical formula to compute the exact probability. It is based on the insight that the last passenger can either sit in their own seat, the first passenger’s seat, or recursion is needed if a different seat is taken. This method ensures precision as it does not rely on random trials.

Method 3: Dynamic Programming

Dynamic programming can be used to improve the efficiency of the analytical approach. Instead of computing values recursively, dynamic programming employs a bottom-up approach and stores results of sub-problems to avoid redundant calculations.

Here’s an example:

def dynamic_probability(passenger_count):
    dp = [0] * (passenger_count + 1)
    dp[1] = 1
    for i in range(2, passenger_count + 1):
        dp[i] = 1/i + (i - 2)/i * dp[i - 1]
    return dp[passenger_count]

# Example with 100 passengers
print(dynamic_probability(100))

The output will be the exact probability, 0.5 as expected.

In this snippet, a dynamic array dp holds the probabilities for subproblems. The loop iterates over passengers and utilizes the previously stored results to compute the current probability. This method provides an exact result with greater algorithmic efficiency compared to plain recursion.

Method 4: Probabilistic Reasoning

This method simplifies the problem by using logic and the base properties of probability, without simulations or intense mathematical computation. It analyzes the problem to realize that the probability in question is inherently symmetrical, resulting in a straightforward answer.

Here’s an example:

def probabilistic_reasoning(passenger_count):
    return 1/2 if passenger_count > 1 else 1

# Example with 100 passengers
print(probabilistic_reasoning(100))

The output will straightforwardly be 0.5 for any passenger count above 1.

This code shows a simple function that leverages logic to derive the probability. It understands that for any passenger count above 1, the symmetry in the seat selection chaos leads to a 50% chance of the last passenger finding their seat unoccupied. This method is the simplest and requires the least computing resources.

Bonus One-Liner Method 5: Lambda Function

For those who love concise code, a one-liner lambda function encapsulates the solution. This is effectively a reduction of the probabilistic reasoning to its bare minimum expression in Python.

Here’s an example:

probability_last_seat = lambda passenger_count: 1/2 if passenger_count > 1 else 1

# Example with 100 passengers
print(probability_last_seat(100))

The output is the same as before: 0.5 for passenger counts greater than 1.

This snippet creates a lambda function probability_last_seat that instantly returns the 50% probability when called with the passenger count. It’s the epitome of Pythonic elegance, presenting the simplest approach with the least amount of code possible.

Summary/Discussion

  • Method 1: Simulation with Iteration. Strengths: Intuitive and closely models real-world randomness. Weaknesses: Computationally expensive and less precise with fewer iterations.
  • Method 2: Analytical Approach. Strengths: Provides an exact probability. Weaknesses: Inefficiency due to recursive calls and stack overflow risk for large passenger counts.
  • Method 3: Dynamic Programming. Strengths: Optimizes the analytical approach by storing intermediate results. Weaknesses: Slightly more complex code than the one-liner.
  • Method 4: Probabilistic Reasoning. Strengths: No need for computation; provides instant answer. Weaknesses: Does not extend to more complicated probabilistic scenarios.
  • Method 5: Lambda Function. Strengths: Extremely concise code. Weaknesses: Might be less readable to newcomers not familiar with lambda functions.