As CRDTs must resolve conflicts arising from concurrent updates, understanding how to optimize conflict resolution becomes vital. It's like when you and your friend are trying to pick a common movie to watch. If you both independently pick a movie, it's possible that you might end up with two separate choices. The decision making process here can be considered analogous to a CRDT operation, with conflict resolution represented by how you and your friend discern which movie to watch.

Let's dive into the Python script below, where we simulate this scenario with two 'replicas' - you and your friend. In our script, 'update_cr_dt' is the function responsible for conflict resolution. When you both independently update your movie choice, the conflict is resolved by appending both choices to a list against the same key. This way, neither choice is lost and you could decide to watch both. The conflict resolution here is optimized, as we don't need to call an external method or roll back operations, it handles the conflict 'on the fly'. This is a basic representation of a key feature in CRDTs - conflict resolution optimization in replicating data across nodes.

This understanding will be useful when we start talking about application design and how these optimized processes can noticeably improve system speed and reliability.

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if __name__ == '__main__':

    CRDT_dict = {}

    me = 'me'

    friend = 'friend'

    my_movie = 'Interstellar'

    friend_movie = 'Inception'

    def update_cr_dt(replica, movie):

        if 'movie' not in CRDT_dict:

            CRDT_dict['movie'] = []

        CRDT_dict['movie'].append((replica, movie))

    update_cr_dt(me, my_movie)

    update_cr_dt(friend, friend_movie)

    print(CRDT_dict)