Saves memory.

Faster. Less memory. Maps to physical things well (e.g. a device with memory mapped registers). No garbage collection / object destruction needed. No need to initialize new objects all the time.

So your writing a game. This game has what I’m going to call “entities” which are the dynamic NPCs and such objects. So these objects are most easily conceptualized as mutable things. Why mutable? Well they move around, change states depending on game events ect. If this object is immutable you’d have to tie the in world representation to a new object, constantly just because it moved slightly or something else. This object is mutable not just because it’s easier to understand but there are even efficiency gains due to not needing to constantly create a new version just because it moved a little bit.

In contrast the object which holds the position data (in this case we’ll have 3 doubles x, y, z) makes a lot of sense as an immutable object. This kind object is small making it cheap to replace (it’s just 3 doubles, so 3*64 bits or a total of 24 bytes) and it’s representing something that naturally makes sense as being immutable, it’s a set of 3 numbers.

Now another comparison your typical dynamic array type container (this is your std::vector std::vec ArrayList and friends). These are mutable objects mainly due to efficiency (it’s expensive to copy the contents when adding new values) yet they also are easier to conceptualize when mutable. It’s an object containing a collection of stuff like a box, you can put things in, take stuff out but it’s still the same box, just it’s contents have changed. If these objects are immutable to put something into the box you must first create a brand new box, and create a copy of the old boxes contents, and then put your new item into the box. Every time. Sometimes this kind of thing makes sense but it’s certainly not a common situation.

Some functional languages do have immutable data structures however in reality the compiler usually does some magic and ends up using a mutable type as it’s simply so much more efficient.

Simply put, because you often want to change the state of something without breaking all the references to it.

Wild off the top of my head example: you’re simulating a football game. Everything is represented by objects which hold references to other objects that are relevant. The ball object is held by player object W, player object X is in collision with and holds a reference to player object Y, player Z is forming a plan to pass to player object X (and that plan object holds a reference to player object X) and so on.

You want to be able to change the state of the ball object (its position say) without creating a new object, because that would invalidate how every other existing object relates to the ball.

Because recreating entire object just to make a single change is dumb.

God help you if you’ve already passed the object by reference and have to chase up all the references to point at the new version!

I’m gonna hazard a guess, just cause I’m curious, that you’re coming from JavaScript.

Regardless, the answer’s basically the same across all similar languages where this question makes sense. That is, languages that are largely, if not completely, object-oriented, where memory is managed for you.

Bottom line, object allocation is VERY expensive. Generally, objects are allocated on a heap, so the allocation process itself, in its most basic form, involves walking some portion of a linked list to find an available heap block, updating a header or other info block to track that the block is now in use, maybe sub-dividing the block to avoid wasting space, any making any updates that might be necessary to nodes of the linked list that we traversed.

THEN, we have to run similar operations later for de-allocation. And if we’re talking about a memory-managed language, well, that means running a garbage collector algorithm, periodically, that needs to somehow inspect blocks that are in use to see if they’re still in use, or can be automatically de-allocated. The most common garbage-collector I know of involves tagging all references within other objects, so that the GC can start at the “root” objects and walk the entire tree of references within references, in order to find any that are orphaned, and identify them as collectable.

My bread and butter is C#, so let’s look at an actual example.

public class MyMutableObject
{
    public required ulong Id { get; set; }

    public required string Name { get; set; }
}

public record MyImmutableObject
{
    public required ulong Id { get; init; }

    public required string Name { get; init; }
}

_immutableInstance = new()
{
    Id      = 1,
    Name    = "First"
};

_mutableInstance = new()
{
    Id      = 1,
    Name    = "First"
};

[Benchmark(Baseline = true)]
public MyMutableObject MutableEdit()
{
    _mutableInstance.Name = "Second";

    return _mutableInstance;
}

[Benchmark]
public MyImmutableObject ImmutableEdit()
    => _immutableInstance with
    {
        Name = "Second"
    };

Even for the most basic edit operation, immutable copying is slower by more than 7 times, and (obviously) allocates more memory, which translates to more cost to be spent on garbage collection later.

Let’s scale it up to a slightly-more realistic immutable data structure.

public class MyMutableParentObject
{
    public required ulong Id { get; set; }

    public required string Name { get; set; }

    public required MyMutableChildObject Child { get; set; }
}

public class MyMutableChildObject
{
    public required ulong Id { get; set; }

    public required string Name { get; set; }

    public required MyMutableGrandchildObject FirstGrandchild { get; set; }
            
    public required MyMutableGrandchildObject SecondGrandchild { get; set; }
            
    public required MyMutableGrandchildObject ThirdGrandchild { get; set; }
}

public class MyMutableGrandchildObject
{
    public required ulong Id { get; set; }

    public required string Name { get; set; }
}

public record MyImmutableParentObject
{
    public required ulong Id { get; set; }

    public required string Name { get; set; }

    public required MyImmutableChildObject Child { get; set; }
}

public record MyImmutableChildObject
{
    public required ulong Id { get; set; }

    public required string Name { get; set; }

    public required MyImmutableGrandchildObject FirstGrandchild { get; set; }
            
    public required MyImmutableGrandchildObject SecondGrandchild { get; set; }
            
    public required MyImmutableGrandchildObject ThirdGrandchild { get; set; }
}

public record MyImmutableGrandchildObject
{
    public required ulong Id { get; set; }

    public required string Name { get; set; }
}

_immutableTree = new()
{
    Id      = 1,
    Name    = "Parent",
    Child   = new()
    {
        Id                  = 2,
        Name                = "Child",
        FirstGrandchild     = new()
        {
            Id      = 3,
            Name    = "First Grandchild"
        },
        SecondGrandchild    = new()
        {
            Id      = 4,
            Name    = "Second Grandchild"
        },
        ThirdGrandchild     = new()
        {
            Id      = 5,
            Name    = "Third Grandchild"
        },
    }
};

_mutableTree = new()
{
    Id      = 1,
    Name    = "Parent",
    Child   = new()
    {
        Id                  = 2,
        Name                = "Child",
        FirstGrandchild     = new()
        {
            Id      = 3,
            Name    = "First Grandchild"
        },
        SecondGrandchild    = new()
        {
            Id      = 4,
            Name    = "Second Grandchild"
        },
        ThirdGrandchild     = new()
        {
            Id      = 5,
            Name    = "Third Grandchild"
        },
    }
};

[Benchmark(Baseline = true)]
public MyMutableParentObject MutableEdit()
{
    _mutableTree.Child.SecondGrandchild.Name = "Second Grandchild Edited";

    return _mutableTree;
}

[Benchmark]
public MyImmutableParentObject ImmutableEdit()
    => _immutableTree with
    {
        Child = _immutableTree.Child with
        {
            SecondGrandchild = _immutableTree.Child.SecondGrandchild with
            {
                Name = "Second Grandchild Edited"
            }
        }
    };

Not only is performance worse, but it drops off exponentially, as you scale out the size of your immutable structures.

Now, all this being said, I myself use the immutable object pattern FREQUENTLY, in both C# and JavaScript. There’s a lot of problems you encounter in business logic that it solves really well, and it’s basically the ideal type of data structure for use in reactive programming, which is extremely effective for building GUIs. In other words, I use immutable objects a ton when I’m building out the business layer of a UI, where data is king. If I were writing code within any of the frameworks I use to BUILD those UIs (.NET, WPF, ReactiveExtensions) you can bet I’d be using immutable objects way more sparingly.

For all the people in this thread talking about the inefficiencies of immutability, they may find this talk by Rich Hickey (the creator of clojure) interesting. Not so much as it shows that they’re wrong, but more so that it’s a good lecture explaining how we can build immutable data structures that address the limitations immutability in a way that reduces the overhead.