[HillRat]

As I understand it (and I may not understand it at all!), the most significant difference is that dataflow programming is deterministic and compositional, whereas actors are nondeterministic and discrete. Intuitively, I tend to compartmentalize dataflow along with maps, filters and comprehensions; and actors with FSMs. This is probably an incorrect generalization, but it works for me.

Somewhere, Hoare gives an example of pipelining the sieve of Eratosthenes through a series of communicating processes, which is a good example of dataflow programming -- you send a list of integers to a process that sieves out even numbers and sends the result to a process that sieves out multiples of 3, and so on. Such a pipeline is deterministic -- given a set of values, you will always get the same output -- and compositional -- the sum of the sieves is greater than the individual parts.

A traditional actor-based model, on the other hand, isn't well-suited for that kind of problem, but is great if your goal is to dispense biscuits and chocolates. State is kept locally, and the actor can change its behavior to external messages based on internal state -- that is to say, actors have discrete identities. The response to any particular message is nondeterministic, because it depends on what's the actor's internal state and decision process may be, and you can't really combine multiple vending machines to compose a new, super-vendor.