The increasing immersion and interaction of computing systems in both physical systems and societal systems, inevitably poses the problem of the nature of computing and its relationship to other scientific disciplines. What is the very nature of computing? How the interplay between different types of systems (physical, computing, biological) can be understood and mastered? To what extent can multi-disciplinary system-centric approaches contribute to a cross-fertilization of other disciplines?
Computing is a scientific discipline in its own right with its own concepts and paradigms. It deals with problems related to the representation, transformation and transmission of information. Information is an entity distinct from matter and energy. It is a resource that can be stored, transformed, transmitted and consumed. It is immaterial but needs media for its representation by using languages characterized by their syntax and semantics
Computing is not merely a branch of mathematics. Just as any other scientific discipline, it seeks validation of its theories on mathematical grounds. But mainly, and most importantly, it develops specific theory intended to explain and predict properties of systems that can be tested experimentally.
The advent of embedded systems brings computing closer to physics. Linking physical systems and computing systems requires a better understanding of differences and points of contact between them. Is it possible to define models of computation encompassing quantities such as physical time, physical memory and energy? Significant differences exist in the approaches and paradigms adopted by the two disciplines.
Physics is based on continuous mathematics while computing is rooted in discrete non-invertible mathematics. Physics studies a given “reality” and tries to discover laws governing physical phenomena while computing systems are human artifacts. Its laws are declarative by their nature. Physical systems are specified by differential equations involving relations between physical quantities. The essence of many physical phenomena can be captured by simple linear laws. They are, to a large extent, deterministic and predictable. Synthesis is the dominant paradigm in physical systems engineering. We know how to build artifacts meeting given requirements (e.g. bridges or circuits), by solving equations describing their behavior. By contrast, state equations of very simple computing systems, such as an RS flip-flop, do not admit linear representations in any finite field. Computing systems are described in executable formalisms such as programs and machines. Their behavior is intrinsically non-deterministic. Non-decidability of their essential properties implies poor predictability.
Computing enriches our knowledge with theory and models enabling a deeper understanding of discrete dynamic systems. It proposes a constructive and operational view of the world which complements the classic declarative approach adopted by physics.
Living organisms intimately combine interacting physical and computational phenomena that have a deep impact on their development and evolution. They share several characteristics with computing systems such as the use of memory, the distinction between hardware and software, and the use of languages. However, some essential differences exist. Computation in living organisms is robust, has built-in mechanisms for adaptivity and, most importantly, it allows the emergence of abstractions and concepts. We believe that these differences delimit a gap that is hard to be filled by actual computing systems.
The Design of Trustworthy Ensembles
The need for rigorous design is sometimes directly or indirectly questioned by developers of large-scale ensembles. These are systems of overwhelming complexity. They consist of an immense number of interconnected components and are built incrementally in an ad hoc manner. Their behavior can be studied only empirically by testing and through controlled experiments. The key issue is to determine tradeoffs between performance and cost by iterative tuning of parameters. Currently, a good deal of research on cloud-based or web-based systems privileges an analytic approach that aims to find laws that generate or explain observed phenomena rather than to investigate design principles for achieving a desired behavior. It is reported in  that “On line companies […] don’t anguish over how to design their Web sites. Instead they conduct controlled experiments by showing different versions to different groups of users until they have iterated to an optimal solution”.
This trend calls for two remarks.
- In contrast to physical sciences, computing is predominantly synthetic. Its main goal is to develop theory, methods and tools for building trustworthy and optimized computing systems. Considering the cyber-universe as a “given reality” driven by its own laws and privileging analytic approaches for their discovery and study, is epistemologically absurd and can have only limited scientific impact. The physical world is the result of a long and well-orchestrated evolution. It is governed by simple laws. To quote Einstein, “the most incomprehensible thing about the world is that it is at all comprehensible”. The trajectory of a projectile under gravity is a parabola which is a very simple and easy to understand law. There is nothing similar about programs or computing systems.
- Ad hoc and experimental approaches can be useful only for optimization purposes. Trustworthiness is a qualitative property and by its nature cannot be achieved by the fine tuning of parameters. Small changes can have a dramatic impact on safety and security properties.
We need theoretical frameworks that are expressive, make use of a minimal set of high level concepts and primitives for system description and that are amenable to formalization and analysis. Is it possible to find a mathematically elegant and still practicable theoretical framework for ensembles? We cannot expect to have theoretical settings as beautiful and as powerful as those for physical systems. One more profound reason is that computing systems are human artifacts while the physical systems are the result of a very long evolution.
In contrast to physical sciences that focus mainly on the discovery of laws, computing should focus mainly on theory for correct-by-construction and predictable system design. Design is central to the discipline. Awareness of its centrality is a chance to reinvigorate research, and build new scientific foundations matching the needs for increasing system integration and new applications.
There already exists a large body of correct-by-construction results such as algorithms, architectures and protocols. Their application allows correctness for (almost) free. How can global properties of ensembles be effectively inferred from the properties of their constituents? This remains an old open problem that urgently needs answers. Failure to bring satisfactory solutions will be a limiting factor for ensemble integration. It would also mean that computing is definitely relegated to second-class status with respect to other scientific disciplines.
 Timo Hannay “The Controlled Experiment” in John Brockman “This will make you smarter”, Happer Perennial.