Introducing assignment leads to difficult conceptual issues, such as the difficulty of defining sameness. But, viewing systems as collections of objects with local state is a powerful technique for maintaining a modular design, separating the internal logic of a sub-procedure from the enclosing procedure. A concrete example is the random number generator.
Once we admit assignment into our programming language, the substitution model introduced is no longer adequate; and this is the motivation of introducing the environment model.
During the evaluation of procedures, new environments are created containing a frame that binds the parameter to the values of arguments; each environment has a pointer leading to the enclosing environment.
Scheme provides us with mutators of pairs, thus we could build powerful mutable list structure upon them.
A pair could serve as a “wrap” around an object, behaving similar to a head pointer of a linked list. For example, if we want a mutable list and use it like this…
(define ml (make-mutable-list))
(insert! 'new-value ml)
…we could make make-mutable-list returning a pair with a “dummy record” and insert! operating on the cdr of the mutable list…
(define (make-mutable-list)
(cons '*mutable-list* '()))
(define (insert! value ml)
(set-cdr! ml (cons value (cdr ml))))
…thus avoiding the need to return the mutated list itself when inserting a new value.
The presence of local state is critical in the implementation of the digital circuit simulator and the constraint system described in the book; both of the two examples store values in “wires” and use a event-driven simulation technique, keeping a listener list in each wire and inform them on value changes.
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