Explanation:

To determine which sorting algorithm has the least dependency on the initial ordering of the input, let's analyze each algorithm's behavior.

Merge Sort:
Merge Sort is a recursive sorting algorithm that divides the input into smaller subproblems until it reaches a base case of a single element or an empty array. It then merges these smaller sorted subarrays back together to obtain the final sorted array. Merge Sort has a consistent running time of O(n log n) regardless of the initial ordering of the input. This is because it always divides the input into equal halves and then merges them in a sorted manner.

Insertion Sort:
Insertion Sort is an iterative sorting algorithm that builds a sorted array by repeatedly inserting elements from the unsorted part into the sorted part of the array. The running time of Insertion Sort is highly dependent on the initial ordering of the input. If the input is already sorted or nearly sorted, Insertion Sort performs efficiently with a best-case time complexity of O(n). However, if the input is in reverse order or randomly ordered, the worst-case time complexity can reach O(n^2).

Selection Sort:
Selection Sort is an iterative sorting algorithm that repeatedly selects the smallest element from the unsorted part of the array and swaps it with the element at the beginning of the unsorted part. The running time of Selection Sort is also highly dependent on the initial ordering of the input. It always performs the same number of swaps regardless of the input, but the number of comparisons varies. Selection Sort has a worst-case time complexity of O(n^2) regardless of the initial ordering.

Quick Sort:
Quick Sort is a recursive sorting algorithm that partitions the input array into two subarrays based on a chosen pivot element. It then recursively sorts the subarrays. The choice of the pivot can greatly affect the running time of Quick Sort. In the worst-case scenario where the pivot is always chosen poorly (e.g., the smallest or largest element), Quick Sort can have a time complexity of O(n^2). However, on average, Quick Sort has a time complexity of O(n log n), making it one of the fastest sorting algorithms.

Conclusion:
Among the given sorting algorithms, Merge Sort has the least dependency on the initial ordering of the input. It consistently performs with a time complexity of O(n log n) regardless of how the input is arranged. This makes Merge Sort a reliable choice for sorting large datasets or when the initial ordering of the input is unknown or arbitrary.