A critical survey of some attempts to define ‘computer’, beginning with some informal ones, then critically evaluating those of three philosophers, and concluding with an examination of whether the brain and the universe are computers.

This article presents the evolution of philosophical and methodological considerations concerning empiricism in computer/computing science. In this study, we trace the most important current events in the history of reflection on computing. The forerunners of Artificial Intelligence H.A. Simon and A. Newell in their paper Computer Science As Empirical Inquiry started these considerations. Later the concept of empirical computer science was developed by S.S. Shapiro, P. Wegner, A.H. Eden and P.J. Denning. They showed various empirical aspects of computing. This led (...) to a view of the science of computing - the science of general scope. Some interesting contemporary ways towards a generalized perspective on computations were also shown. (shrink)

This paper argues that scientific studies distinguish themselves from other studies by a combination of their processes, their (knowledge) elements and the roles of these elements. This is supported by constructing a process model. An illustrative example based on Newtonian mechanics shows how scientific knowledge is structured according to the process model. To distinguish scientific studies from research and scientific research, two additional process models are built for such processes. We apply these process models: (1) to argue that scientific progress (...) should emphasize both the process of change and the content of change; (2) to chart the major stages of scientific study development; and (3) to define “science”. (shrink)

It is relatively easy to state that information retrieval is a scientific discipline but it is rather difficult to understand why it is science because what is science is still under debate in the philosophy of science. To be able to convince others that IR is science, our ability to explain why is crucial. To explain why IR is a scientific discipline, we use a theory and a model of scientific study, which were proposed recently. The explanation involves mapping the (...) knowledge structure of IR to that of well-known scientific disciplines like physics. In addition, the explanation involves identifying the common aim, principles and assumptions in IR and in well-known scientific disciplines like physics, so that they constrain the scientific investigation in IR in a similar way as in physics. Therefore, there are strong similarities in terms of the knowledge structure and the constraints of the scientific investigations between IR and scientific disciplines like physics. Based on such similarities, IR is considered a scientific discipline. (shrink)

Recently, Luk tried to establish a model and a theory of scientific studies. He focused on articulating the theory and the model, but he did not emphasize relating them to some issues in philosophy of science. In addition, they might explain some of the issues in philosophy of science, but such explanation is not articulated in his papers. This paper explores the implications and extensions of Luk’s work in philosophy of science or science in general.

This paper presents a theory of scientific study which is regarded as a social learning process of scientific knowledge creation, revision, application, monitoring and dissemination with the aim of securing good quality, general, objective, testable and complete scientific knowledge of the domain. The theory stipulates the aim of scientific study that forms the basis of its principles. It also makes seven assumptions about scientific study and defines the major participating entities. It extends a recent process model of scientific study into (...) a detailed interaction model as this process model already addresses many issues of philosophy of science. The detailed interaction model of scientific study provides a common template of scientific activities for developing logical models in different scientific disciplines, or alternatively for developing ontologies of different scientific disciplines. Differences between research and scientific studies are discussed, and a possible way to develop a scientific theory of scientific study is described. (shrink)

We examine the philosophical disputes among computer scientists concerning methodological, ontological, and epistemological questions: Is computer science a branch of mathematics, an engineering discipline, or a natural science? Should knowledge about the behaviour of programs proceed deductively or empirically? Are computer programs on a par with mathematical objects, with mere data, or with mental processes? We conclude that distinct positions taken in regard to these questions emanate from distinct sets of received beliefs or paradigms within the discipline: – The rationalist (...) paradigm, which was common among theoretical computer scientists, defines computer science as a branch of mathematics, treats programs on a par with mathematical objects, and seeks certain, a priori knowledge about their ‘correctness’ by means of deductive reasoning. – The technocratic paradigm, promulgated mainly by software engineers and has come to dominate much of the discipline, defines computer science as an engineering discipline, treats programs as mere data, and seeks probable, a posteriori knowledge about their reliability empirically using testing suites. – The scientific paradigm, prevalent in the branches of artificial intelligence, defines computer science as a natural (empirical) science, takes programs to be entities on a par with mental processes, and seeks a priori and a posteriori knowledge about them by combining formal deduction and scientific experimentation. We demonstrate evidence corroborating the tenets of the scientific paradigm, in particular the claim that program-processes are on a par with mental processes. We conclude with a discussion in the influence that the technocratic paradigm has been having over computer science. (shrink)

Church's Thesis asserts that the only numeric functions that can be calculated by effective means are the recursive ones, which are the same, extensionally, as the Turing-computable numeric functions. The Abstract State Machine Theorem states that every classical algorithm is behaviorally equivalent to an abstract state machine. This theorem presupposes three natural postulates about algorithmic computation. Here, we show that augmenting those postulates with an additional requirement regarding basic operations gives a natural axiomatization of computability and a proof of Church's (...) Thesis, as Gödel and others suggested may be possible. In a similar way, but with a different set of basic operations, one can prove Turing's Thesis, characterizing the effective string functions, and--in particular--the effectively-computable functions on string representations of numbers. (shrink)

This paper is concerned with scientific reasoning in the engineering sciences. Engineering sciences aim at explaining, predicting and describing physical phenomena occurring in technological devices. The focus of this paper is on mathematical description. These mathematical descriptions are important to computer-aided engineering or design programs (CAE and CAD). The first part of this paper explains why a traditional view, according to which scientific laws explain and predict phenomena and processes, is problematic. In the second part, the reasons of these methodological (...) difficulties are analyzed. Ludwig Prandtl’s method of integrating a theoretical and empirical approach is used as an example of good scientific practice. Based on this analysis, a distinction is made between different types of laws that play a role in constructing mathematical descriptions of phenomena. A central assumption in understanding research methodology is that, instead of scientific laws, knowledge of capacities and mechanisms are primary in the engineering sciences. Another important aspect in methodology of the engineering sciences is that in explaining a phenomenon or process spatial regions are distinguished in which distinct physical behaviour occur. The mechanisms in distinct spatial regions are represented in a so-called diagrammatic model. The construction of a mathematical description of the phenomenon or process is based on this diagrammatic model. (shrink)

Continuing his exploration of the organization of complexity and the science of design, this new edition of Herbert Simon's classic work on artificial ...