Higher order functions
Acton supports higher order functions, which means you can pass a function as an argument to another function and choose behavior at the call site.
For collection transformations, use higher order functions,
comprehensions, or generator expressions.
Generator expressions describe lazy iteration, and builtins such as
map, filter, and flatmap accept functions to transform iterables.
A function can be treated like any other value. That makes it easy to reuse one loop, one validation path, or one calculation with different behaviors plugged in.
def apply_twice(fun, value):
return fun(fun(value))
def double(n):
return 2 * n
def square(n):
return n * n
actor main(env):
print(apply_twice(double, 3))
print(apply_twice(square, 2))
env.exit(0)
This prints:
12
16
Use this pattern when the operation is the thing that changes and the overall shape of the work stays the same.
Higher order functions work best when the varying behavior is small, stateless, and easy to describe as "apply this operation here". That makes them a good fit for callbacks, adapters, validation hooks, and small reusable transformation steps. Once the behavior needs evolving state across calls, the design question changes: you are no longer just passing behavior, you are passing behavior plus state.
At that point, an actor or a small object is often a better home than trying to simulate closure-heavy code by threading more and more helper arguments through every call. The same applies to collection work: if a transformation is local and easy to read, a comprehension or short loop usually expresses it more clearly than a stack of tiny callback-style helpers. Reach for higher order functions when they clarify the reusable variation, not just because the language allows them.