We develop domain theory in constructive and predicative univalent foundations (also known as homotopy type theory). That we work predicatively means that we do not assume Voevodsky's propositional resizing axioms. Our work is constructive in the sense that we do not rely on excluded middle or the axiom of (countable) choice. Domain theory studies so-called directed complete posets (dcpos) and Scott continuous maps between them and has applications in programming language semantics, higher-type computability and topology. A common approach to deal with size issues in a predicative foundation is to work with information systems, abstract bases or formal topologies rather than dcpos, and approximable relations rather than Scott continuous functions. In our type-theoretic approach, we instead accept that dcpos may be large and work with type universes to account for this. A priori one might expect that complex constructions of dcpos result in a need for ever-increasing universes and are predicatively impossible. We show that such constructions can be carried out in a predicative setting. We illustrate the development with applications in the semantics of programming languages: the soundness and computational adequacy of the Scott model of PCF and Scott's $D_\infty$ model of the untyped $\lambda$-calculus. We also give a predicative account of continuous and algebraic dcpos, and of the related notions of a small basis and its rounded ideal completion. The fact that nontrivial dcpos have large carriers is in fact unavoidable and characteristic of our predicative setting, as we explain in a complementary chapter on the constructive and predicative limitations of univalent foundations. Our account of domain theory in univalent foundations is fully formalised with only a few minor exceptions. The ability of the proof assistant Agda to infer universe levels has been invaluable for our purposes.
翻译:我们在构造性和预测性单价基础(也称为同伦类型理论)中开发了域论。我们的预测性意味着我们不假设 Voevodsky 的命题调整公理。我们的工作在构造上不依赖于排中律或(可数)选择公理。域论研究所谓的有向完备偏序集(dcpos)及其之间的 Scott 连续映射,并在编程语言语义学、高类型可计算性和拓扑学中应用。在预测性基础中用于处理大小问题的常见方法是使用信息系统、抽象基础或形式拓扑而不是 dcpos,并且使用可逼近关系而不是 Scott 连续函数。在我们的类型论方法中,我们接受 dcpos 可能很大,并使用类型宇宙来解决这个问题。先验地,复杂的 dcpos 结构似乎需要不断增加的宇宙,并且在预测性基础中是不可能的。我们展示了这样的构造可以在预测性环境中进行。我们举例说明了在编程语言语义学中的应用:PCF 的 Scott 模型的合理性和计算适应性以及无类型 $\lambda$-演算的 Scott 的 $D_\infty$ 模型。我们还给出了关于连续和代数 dcpos、小基础及其圆滑理想完成的预测性解释。非平凡 dcpos 有大的承载者这一事实实际上是不可避免的,并且是我们预测性环境的特征,如我们所解释的关于单价基础的构造和预测性限制的补充章节所述。我们在单价基础中的域论账户被完全形式化,只有几个小的例外。鉴于 Agda 证明助手推断宇宙级别的能力,在我们的目的中是不可或缺的。