The prelims comprise: Introduction Computer Science and Mathematics The Formal Verification Debate Abstraction in Computer Science Conclusion.

Abstract: Laws of computer science are prescriptive in nature but can have descriptive analogs in the physical sciences. Here, we describe a law of conservation of information in network programming, and various laws of computational motion (invariants) for programming in general, along with their pedagogical utility. Invariants specify constraints on objects in abstract computational worlds, so we describe language and data abstraction employed by software developers and compare them to Floridi's concept of levels of abstraction. We also consider Floridi's structural (...) account of reality and its fit for describing abstract computational worlds. Being abstract, such worlds are products of programmers' creative imaginations, so any "laws" in these worlds are easily broken. The worlds of computational objects need laws in the form of self-prescribed invariants, but the suspension of these laws might be creative acts. Bending the rules of abstract reality facilitates algorithm design, as we demonstrate through the example of search trees. (shrink)

We characterize abstraction in computer science by first comparing the fundamental nature of computer science with that of its cousin mathematics. We consider their primary products, use of formalism, and abstraction objectives, and find that the two disciplines are sharply distinguished. Mathematics, being primarily concerned with developing inference structures, has information neglect as its abstraction objective. Computer science, being primarily concerned with developing interaction patterns, has information hiding as its abstraction objective. We show that abstraction through information hiding is a (...) primary factor in computer science progress and success through an examination of the ubiquitous role of information hiding in programming languages, operating systems, network architecture, and design patterns. (shrink)

Explores the development of the ideas of evolutionary biology, particularly as affected by the increasing understanding of genetics and of the chemical basis of inheritance.

This Guide provides an ambitious state-of-the-art survey of the fundamental themes, problems, arguments and theories constituting the philosophy of computing.

Thinking Machines and the Philosophy of Computer Science: Concepts and Principles presents a conversation between established experts and new researchers in the field of philosophy and computer science about human and non-human relationships with the environment. This resource contains five sections including topics on philosophical analysis, the posterior ethical debate, the nature of computer simulations, and the crossroads between robotics, AI, cognitive theories and philosophy.

Provides a philosophical analysis of such biological concepts as natural selection, adaptation, speciation, and evolution.