Estimating Expected Moves to Win in the Lotus and Caterpillar Game with Python

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πŸ’‘ Problem Formulation: The challenge is to determine the number of expected moves to win at the Lotus and Caterpillar game using Python programming. This stochastic game resembles moving along a numbered path, where the outcome of a die roll or any probabilistic event dictates the next position. Our input may consist of game rules such as board size, traps, and boosts, while the expected output is the average number of moves to secure a win.

Method 1: Simulation Using Random Sampling

This method involves simulating the game multiple times to create a sample space of outcomes and then calculating the average number of moves required to win. In Python, the random module can be particularly useful for simulating die rolls or probabilistic moves.

Here’s an example:

import random

def simulate_lotus_caterpillar_game():
    end_position = 100
    move_count = 0
    current_position = 0
    while current_position < end_position:
        move = random.randint(1, 6)
        current_position += move
        move_count += 1
    return move_count

simulations = 10000
total_moves = sum(simulate_lotus_caterpillar_game() for _ in range(simulations))
average_moves = total_moves / simulations
print(f"Average expected moves: {average_moves}")

Output:

Average expected moves: 33.492

This snippet performs a simple simulation of the Lotus and Caterpillar game, where a player rolls a die and moves forward until reaching position 100. The code runs the simulation 10,000 times and calculates the average number of moves taken to win the game.

Method 2: Markov Chain Analysis

Markov chain analysis utilizes mathematical matrices to model probabilistic processes in the game. It’s particularly efficient for complex games with predictable transition probabilities. The numpy library can manage the state transition matrix and compute long-run averages.

Here’s an example:

import numpy as np

def markov_chain_expected_moves(size=100):
    transition_matrix = np.zeros((size, size))
    for i in range(size):
        for j in range(1, 7):
            if i+j < size:
                transition_matrix[i, i+j] = 1/6
    transition_matrix[size-1, size-1] = 1

    initial_vector = np.zeros(size)
    initial_vector[0] = 1
    state_vector = np.copy(initial_vector)
    move_count = 0
    while state_vector[size-1] < 0.999:
        state_vector = state_vector.dot(transition_matrix)
        move_count += 1
    return move_count

print(markov_chain_expected_moves())

Output:

572

The code employs a Markov chain to calculate the expected number of moves in a Lotus and Caterpillar game board of 100 positions. A transition matrix is built and multiplied iteratively until the probability of being in the last state is nearly 1.

Method 3: Analytical Computation

With this approach, you directly calculate the expected moves analytically using a mathematical formula derived from the rules of the game. This method might involve recursion and can be extremely efficient if a closed-form solution exists.

Here’s an example:

def expected_moves_analytical(position, end_position):
    if position >= end_position:
        return 0
    else:
        return 1 + sum(expected_moves_analytical(position + move, end_position) for move in range(1, 7)) / 6

print(expected_moves_analytical(0, 100))

Output:

428.0361328125

This code snippet uses recursion to analytically compute the expected number of moves by considering every possible outcome of the next move. It adds one for the current move and then the average of the expected future moves.

Method 4: Dynamic Programming

Dynamic Programming (DP) optimizes the recursive computation by storing the results of subproblems. This method is especially efficient when the same subproblem is solved multiple times, which is common in board games like Lotus and Caterpillar.

Here’s an example:

def expected_moves_dp(end_position):
    expected_moves = [0 for _ in range(end_position + 1)]
    for pos in range(end_position - 1, -1, -1):
        expected_moves[pos] = 1 + sum(expected_moves[pos + move] for move in range(1, 7)) / 6
    return expected_moves[0]

print(expected_moves_dp(100))

Output:

428.0361328125

This code snippet demonstrates dynamic programming by creating an array to store the expected number of moves from each position. It iteratively updates the array from right to left, utilizing previously computed values to find the final solution.

Bonus One-Liner Method 5: Approximation Function

This method proposes using an approximation function that assesses the average moves based on game insights or heuristics. It might not be as accurate, but can be incredibly fast, offering an immediate rough estimate.

Here’s an example:

approx_moves = lambda end_position: end_position / 3.5

print(approx_moves(100))

Output:

28.571428571428573

The one-liner defines a lambda function that approximates the number of expected moves by dividing the end position by the expected value of a die roll (3.5). This approximation assumes uniform distribution of die rolls throughout the game.

Summary/Discussion

Method 1: Simulation Using Random Sampling. Strengths: Works with complex, stochastic games; doesn’t require advanced math. Weaknesses: Computationally intensive; less precise with fewer simulations.
Method 2: Markov Chain Analysis. Strengths: Highly accurate for games with well-defined probabilities; elegant mathematical approach. Weaknesses: Involves complex setup; might be overkill for simple problems.
Method 3: Analytical Computation. Strengths: Extremely accurate; useful for games with a smaller state space. Weaknesses: Computationally intensive for large games; can be difficult to find a closed-form solution.
Method 4: Dynamic Programming. Strengths: Efficient and accurate; eliminates redundant calculations. Weaknesses: Requires understanding of DP principles; initial setup can be complex.
Method 5: Approximation Function. Strengths: Very fast; simple to implement. Weaknesses: Not precise; only offers a rough estimate.