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This article is about deterministic models, what they are, why they are useful, and what their limitations are. First, the article emphasizes that determinism is a property of models, not of physical systems. Whether a model is deterministic or not depends on how one defines the inputs and behavior of the model. To define behavior, one has to define an observer. The article compares and contrasts two classes of ways to define an observer, one based on the notion of “state” and another that more flexibly defines the observables. The notion of “state” is shown to be problematic and lead to nondeterminism that is avoided when the observables are defined differently. The article examines determinism in models of the physical world. In what may surprise many readers, it shows that Newtonian physics admits nondeterminism and that quantum physics may be interpreted as a deterministic model. Moreover, it shows that both relativity and quantum physics undermine the notion of “state” and therefore require more flexible ways of defining observables. Finally, the article reviews results showing that sufficiently rich sets of deterministic models are incomplete. Specifically, nondeterminism is inescapable in any system of models rich enough to encompass Newton’s laws. 

Many programming languages and programming frameworks focus on parallel and distributed computing. Several frameworks are based on actors, which provide a more disciplined model for concurrency than threads. The interactions between actors, however, if not constrained, admit nondeterminism. As a consequence, actor programs may exhibit unintended behaviors and are less amenable to rigorous testing. We show that nondeterminism can be handled in a number of ways, surveying dataflow dialects, process networks, synchronous-reactive models, and discrete-event models. These existing approaches, however, tend to require centralized control, pose challenges to modular system design, or introduce a single point of failure. We describe “reactors,” a new coordination model that combines ideas from several of these approaches to enable determinism while preserving much of the style of actors. Reactors promote modularity and allow for distributed execution. By using a logical model of time that can be associated with physical time, reactors also provide control over timing. Reactors also expose parallelism that can be exploited on multicore machines and in distributed configurations without compromising determinacy.