Bulletin of Applied Computing and Information Technology

Epistemology and Computing Studies

Dr Brian O. Cusack
Auckland University of Technology, New Zealand
brian.cusack@aut.ac.nz

Abstract

Computing is a field of study that has evolved from traditional disciplines in mathematics and science, and now covers a very large and general array of knowledge. As a field of study Computing is defined by the way objects are included rather than by any that may be excluded. The looseness and ambiguity of the field can lead to vague or unintelligible claims that are not readily transferred in speech or meaning to other contexts. This is a problem that may be mitigated by systematic methods and redressed with an acknowledgement of the limits to knowledge amassed in fields.

In this paper some of the background to the epistemology of Computing is reviewed in an attempt to illustrate possible approaches to making PhD proposals in Computing. No attempt is made to be exhaustive of possibilities but cases are used to illustrate different approaches.

Keywords

Computing knowledge, epistemology, postgraduate research, PhD studies

1. Introduction

I was first introduced to computing in 1968 at high school by the head of the Mathematics Department. She had the foresight to see that computers were to be a future for the coming generations of students and set up a club that met at lunch times to write Fortran 60 programs for computation and text manipulation. We patiently punched out slots on IBM punch cards with unrolled paper clips, placed rubber bands around the stacks of cards, and then had them couriered to Dunedin by the BNZ for processing. The cards and printouts would come back the following week, usually for correction and resubmission. When I went to Auckland University in the early 1970s the epistemology of computing was similar but in a state of transition. Computing started in the Mathematics Department that then separated into Pure and Applied Departments – computing was defined as an applied math. Two years later computing branched out of Applied Mathematics and through a series of iterations became and independent Department. Since then, of course, computing has diversified further and is now represented under pluralistic banners of other Departments (e.g. MIS), Faculties (e.g. Business) and various ad hoc organisational groupings (e.g. HCI, Art & design, and AI).

The map of computing knowledge seen through educational organization offerings mirrors the evolution of a field of knowledge through diversification. Maths and sciences are often referred to as tight knowledge areas and other fields such as the social & human sciences as loose knowledge areas. The field of computing currently contains a wide range of loose and tight knowledge areas and an acceptance that all these different ways of knowing fit the developmental nature of the field. As a consequence the opportunities for research are wide and the acceptance of different approaches welcomed. In practice studies in computer science, for example networking or microprocessor research, are as valid as studies of human computer interaction or end user studies in Information Systems. What is interesting here, is that neither the “what” or the “how” of the field is held tight, but rather the “who” and the “when”.

The generalization and evolution of the computing field has created problems for the training, the certification, and the supervision of participants. The field is characterized by constant change, persistent progress and strong parochial advocacy. For example, in the areas of software development or hardware development change is visible daily, and progress is made every two to three months. The difficulty presented to researchers and people wanting to gain postgraduate qualifications in computing is where to start. Many good ideas, exciting programs, and best-prepared reading come to nothing after a few weeks of inquiry. A general perception arises that everything is in a state of change (including business and university supervisors), and that the tried and true research methods are inadequate or insufficient to deliver other than momentary insights on any topic. At best this paper can give some insight into getting a foothold in the field.

2. Epistemology

Epistemology is a study traditionally undertaken by philosophers into the nature of knowledge. It is defined as “the theory of knowledge” (O’Conner & Carr, 1982, p. vii), and in other studies as “the preferred theory of knowledge”(Quine, 1976, p. 7). Epistemology is one technical division of the academic discipline called philosophy. The other two divisions are ontology (the nature of being or reality) and axiology (the nature of value) (Hodgkinson, 1983, pp. 4-5). A quick search on the Web opens a large definitional resource that takes the scope of epistemology far beyond what I hope to cover here. The central questions for epistemology are: “What do we know ?” and “ Under what conditions do we know?” Two useful examples describing epistemology are:

“Epistemology is the branch of philosophy that studies knowledge. It attempts to answer the basic question: what distinguishes true (adequate) knowledge from false (inadequate) knowledge? Practically, this question translates into issues of scientific methodology: how can one develop theories or models that are better than competing theories? It also forms one of the pillars of the new sciences of cognition, which developed from the information processing approach to psychology, and from artificial intelligence, as an attempt to develop computer programs that mimic a human's capacity to use knowledge in an intelligent way.” (http://pespmc1.vub.ac.be/EPISTEMI.html).

“Whether and when subjects know something is whether and when they are justified in believing things, and the justification of beliefs is a standard topic in epistemology. Epistemology also concerns itself with other, closely related concepts. Some examples: When is a subject rational in believing something? When are you certain of something? When do you know for certain that something is the case? When is something doubtful, for a subject, or not? When is something possible (in an epistemic sense of "possible") -- under what conditions is a belief possibly false from its subject's point of view? When is a belief adequately supported by one's evidence? (And what constitutes our evidence for our beliefs, and when does a belief need to be supported by evidence in order to be rational?)” (http://pantheon.yale.edu/~kd47/What-Is-Epistemology.htm).

The scope of epistemology is far reaching for anyone involved in research. It is not enough to simply undertake a study within the requirements of a discipline or field unless the limits of the study are recognized in terms of the epistemic capacity or maturity of the preferred research framework. The questions quoted above may be used to interrogate the content of any research report. Philosophy is hence argued to be a “second order” activity that has the capacity to adjudicate between different claims for knowing (Hirst, 1974, p.1). Philosophical work in epistemology provides frameworks for evaluating theories of knowledge, for differentiating different types of knowledge and guidelines for debating the issues that arise from contested problem areas. An understanding of epistemological methods and the way different philosophers work to build knowledge provides the introduction and conclusion to a study.

It is not by mistake that the highest academic degree is called the Doctor of Philosophy (PhD). Even in this age of applied Doctorates (single frame studies) the Doctor of Philosophy is still seen as a theoretical (multi frame) degree. The traditional belief is that anyone undertaking the extended course of study would arrive in common theoretical grounds, irrespective of their discipline, and philosophize the nature of knowledge. Philosophy has hence been given a place of privilege amongst the academic disciplines (such as Science, Language, Mathematics, and so on) to adjudicate disputes over what constitutes knowledge, its justification, and the distinctions between different types of knowledge (Hirst, 1974, pp.1-2):

“[Philosophy] … is a distinctive type of higher order pursuit, primarily an analytical pursuit, with the ambition of understanding the concepts used in all other forms of lower-order knowledge and awareness. Philosophy, as I see it, is a second-order area of knowledge, concerned above all with the necessary features of our primary forms of understanding and awareness in the sciences, in morals, in history and the like. Philosophical questions are not about, say, particular facts or moral judgments, but about what we mean by facts, what we mean by moral judgments, and how these fundamental elements in our understanding relate to each other…”

3. Epistemology and Computing

Computing presented an interesting dilemma for technology. From the industrial revolution onwards in time, technology had provided mechanical solutions for work and transportation, and had given rise to new managerial classes. Computing from the early 20^th century, however, presented the possibility of information processing machines doing the thinking and decision-making work of the managerial middle classes. Computing hence took technology studies beyond mechanical / technical boundaries and challenged the frameworks of mathematics and science. In math patterning methods had to be developed and in science, recognition of the methodological limits grew. Computing took technology knowledge on a leap from mechanical effects and mathematics / science justifications to a dilemma that to date has only partial solutions. In terms of epistemology computing straddles all of the traditional disciplines (Turban & Aronson, 1998, p. 207) and at best may lay claim to an ambiguous field of study. Attempts to divide the field into, for example computer science, information systems, and so on have done little to make the grounds for adopting true propositions any clearer. I will now briefly review five themes that have been used successfully to develop research in the field of computing and make suggestions as to how a student can use each theme.

3.1 Computable Numbers

Alan Turing (1950) had proposed an algebra for computation and sets of computational algorithms for decision-making – commonly called the Turing Machine(s). This turn in computing history was made the more pointed by the simple question, “Can machines think?”. Turing introduced the link between computing and cognitive science, and opened computing studies to metaphysical (how) questions. Questions relating to the continuity of Man and machine (or otherwise), machine and brain, and personification became legitimate in computing studies. In more recent research (e.g. Churchland, 1989) attempts to rationalize computing epistemology have taken a physicalist's and a naturalist's turns and attention has moved towards artificial intelligence. For a student looking for research topics, the Turing turn allows the entry of psychological, physiological, and philosophical questions, in addition to algorithmic and related work.

3.2 Technology is Different

The distinction drawn between technology and science is an important one for students approaching research in computing. The distinction not only lets a student choose other than a scientific method but also affords alternative grounds for true propositions. In a traditional literature technology was seen as an inferior study concerned with work and experience and opposed to aesthetic studies or the pursuit of truth in science (O’Hear, 1989, p. 216). However, such a view is incomplete when technology is seen as information technology and in the context of computing games and entertainment. In Lyotard’s (1979, p. 45) critical reflection on science and technology, the point is made that science is concerned with truth, others with justice, and technology with efficiency. The argument in this critique is positioned within an empirical framework and the writer sees technology as a study that collects data to confirm or otherwise theoretical hypothesis. Technology by definition hence produces proof and in relation to science, influences the truth criterion of science by increasing the capacity to be right. These views provide a framework in which computing propositions can be justified in terms of the findings collected within the computing field.

The implication for students looking for research questions is that sociological, managerial, legal, and action based work are legitimate. Hence, wide ranging studies into technology effects, performance measurement, legitimating, and critical reflective studies are possible.

3.3 Effects on Being

The diversification of the computing field to include mental effects (Turing, 1950), to include more than science (Lyotard, 1979), and the location of computing in a post mechanical view of technology, have all lead to the development of computing studies that consider technological effects on human beings. The current market leader is studies into the Human Computer Interface. These approaches have evolved from information systems end user studies and developed their own methods and approaches. Other related studies of being take a similar approach and start with the human, human relationships and reflections, and then backward map onto the computer interface (e.g. Computing psycho drama). The theoretic shift presented by this area of computing studies is the conception that computing is not a means but an end. Hence the processes for analysis rest with the human and not the machine.

A more formal and substantial literature into the study of computing effects and being can be located in the collective work of Martin Heidegger (e.g. 1977, etc.). Heidegger’s exploration of technology is a challenge to neutrality. The relationship between technology and being is coercive and partial in its effect. The human is ordered and controlled by information technology, and carries a divided character – in part the gestalt stamped by technology and in part the being stamped by nature. This ever-changing relationship gives rise to a nexus of researchable questions often relating to work systems, human identity, education, and freedoms.

3.4 Artificial Intelligence

Much of what I have put forward as alternative research approaches in computing studies (above) may be explained by reference to Gödel’s incompleteness theorem. A generalization that follows from the theorem, is that there are internal limits within any formalized system (those being: consistency, completeness, decidability, and independence of axioms (Peters, 1989, p.102). The implication of the Theorem is that internal investigation of any formalized system can lead to localization. For example, the challenge to some elements of the formalized system of mathematics by Turing, lead to a new algebra and the entertainment of research questions concerning Man and machine. Similarly, Von Neumann (who was well aware of Gödel’s incompleteness theorem and Turing’s work) advanced notions of integrating self-replicating machines and separating the “program” from the machine. These foundations provide an entry into not just the what (Turing) of machines thinking, but also the how (von Newman). Artificial Intelligence (AI) offers many and diverse opportunities for students to undertake extended research in both applied and theoretical computing. Often this work involves building a functional application or solving a problem in the theory of intelligence.

3.5 Intuitional and Professional Knowledge

A substantial and well-developed framework for the epistemology of computing studies is a institutionalized framework. The National Advisory Committee on Computing Qualifications (NACCQ) is one such body that seeks to certify and sanction computing knowledge. In concert with universities, business training schools and others, elements, methods, and frameworks are selected as the preferred basis for knowledge. Any of these objects can be the subject for a sustained research project, and even the ways of communicating knowledge (e.g. education) are legitimate computing studies. These studies can include process analysis, policy evaluation, and systems review.

Furthermore there are many professional organizations that retain, disseminate and define terms for computing knowledge. For example, the New Zealand Computer Society (NZCS), the International Standards Organization (ISO), The Institute of Electrical and Electronic Engineers Inc. (IEEE), and many other organizations. A serious consideration for every research student is the positioning of a proposed study. None of the computing epistemological frameworks introduced in this Section and Sub-sections provide a comprehensive justification for knowledge – although most attempt to be consistent and coherent. Again I would appeal to Gödel’s incompleteness theorem and suggest that there is room to challenge any computing epistemological claim from within the preferred theory of computing knowledge and (as a point I’ll take up in the next Section) inconsistencies between preferred theories of computing knowledge. All these observations bode well for a student wishing to make a proposal for PhD studies.

4. Computer Knowledge

The assertions in this paper to date, support the thesis that computing knowledge is diverse and is accumulated into a field (or many). Some of the consequences for study and entry into the field have been developed. In this section I want to look at the problems for substantiating true statements in fields and how this may influence proposals for study. Some of the more interesting problems, such as “What status do truth claims have in fields that have arisen from other fields?” and “How might an ambiguous claim be tested ?” fall beyond the scope of this paper.

The traditional structuring of knowledge has adopted the divisions known as “disciplines” to make logically distinct different forms of knowledge. These forms are defined as “mathematics, physical sciences, human sciences, history, religion, literature and the fine arts, [and] philosophy” (Hirst, 1974, p. 46). In addition to disciplines, various fields of knowledge are recognized as being different in nature to disciplines and are generally described as being less rigorous for truth requirements and inclusive.

“… there are those organizations (of knowledge) which are not themselves disciplines or sub-divisions of any discipline. They are formed by building together round specific objects, or phenomena, or practical pursuits, knowledge that is characteristically rooted elsewhere in more than one discipline… these organizations (of knowledge) are not concerned, as the disciplines are, to validate any one logically distinct form of expression. They are not concerned with developing a particular structuring of experience. They are held together simply by their subject matter... I see no reason why such organizations (of knowledge), which I shall refer to as “fields”, should not be endlessly constructed according to particular theoretical or practical interests.” (Hirst, 1974, pp. 45-46).

Hirst then goes on to give examples of fields of study such as Geography and Engineering. I would also add Professional fields of knowledge such as Medicine, Law, and Education as valid fields of study. In the traditional sense, these fields of study have been arrived at by the participants reading and reflecting on the disciplines, but then by constructing an inclusive content matter, coherently distinct to the field but not exclusive to any other.

Computing knowledge conforms to the definition of a field organization. It also has professional and particular content elements, interaction with disciplines, and a constituency similar to engineering or sociology. However, a field of knowledge (as Hirst pointed out) has gains and losses. Disciplines are tight, objects are clearly defined and the criteria for true statements expressly prescribed. Fields on the other hand are more expressive and inclusive of generalities, but lack qualities such as transfer, precision, and definition. Ambiguity is a key distinction that affects the capacity to adjudicate claims for knowing in fields. In computing for example, two parties may claim to be correct in achieving a software performance output but the grounds for this claim and the value of the claim may be distinctly different on both counts. Hence it may come down to who shouts the loudest or when the claims were made. The implications for study supervision, who may claim to be an expert, and which content to include in a computing course remain ambiguous.

The computing field (as with other fields of study) can adopt the metaphor of the Wild West. Standards are at best the adopted law of the computing Wild West but even standards change and are open to the buying power of those who shout the loudest. Innovation and change also characterize the field, whereas progress is harder to find. The implications of these assertions are several fold for a student attempting to get started on a post-graduate study. First there is no best supervisor other than the one who likes your proposal and is prepared to work with you. Computing knowledge is generally available and the Web gives global access to best practice and expert knowledge. Second the choice of a best University / Polytechnic / College to study at may come down to the availability of resources, access, and life style. A big part of post-graduate study is personal growth and this may be best achieved in a way of your choosing. Third and a contentious point at present, a student need not enroll in a computing department / school / faculty to gain a computing degree. Choose the organizational division that best suits your aspirations and talent. Fifth, choose a topic that motivates you but be prepared to adapt and change the original scope to accommodate the nature of the field. And as a final pointer, beware of the noisy promotional push of the Wild West. Much of computing makes for good entertainment but often detracts from serious productivity. The key to success in post-graduate computer studies is locating a niche, getting enough peace and quiet to drill down into epistemology, and then making enough noise to out shout others.

5. Potential Investigations

The range of potential research topics for a PhD in computing is wide. To narrow this discussion I will concentrate on eight proposals for study made by students over the last 18 months to illustrate the diversity of possible ways of approaching PhD Some of the proposals succeed in gaining acceptance at the four levels of review the University applies and others failed or went on to other Universities for various reasons. I’ll also comment on the strengths and weaknesses of each suggestion and the reasons for where the proposal went.

5.1 Internet Cafés Sociology & Cyboid Culture.

This study proposal saw potential in the observation and analysis of human behaviour in Internet Cafés. The working hypothesis concerned the growth of what is termed “Cyboid Culture” – where humans become locked into a cyber-world trance-like state - and proposed personal and organizational effects. This proposal had the potential to follow through some well-grounded literature and into data collection in the bustling Internet Café context in the Auckland Central Business District (CBD). It was a strong proposal having a good literature and an identifiable hypothesis and data field. The student unfortunately withdrew the proposal on account of the competing opportunity to earn an income in business but hopes to return to PhD in a year or two.

5.2 Multi Processor and Beowulf Networking.

This proposal was directed at measuring processing capacities and comparing different microprocessor configurations and their performance. The approach was to postulate different designs and configurations and then to test them. The experimental design was to be multi-factorial and the outcome a set of benchmarks for forecasting capacity, performance and workloads. The study was neatly positioned with in the New Zealand I2 development and International interest in grid computing interests and had great potential. This proposal had more strengths than weaknesses and had the potential to be carried forward as a contribution to worldwide grid computing developments (future proofing). The student, however, chose to enroll using a different topic and to use a hybrid research method.

5.3 Technologies and Public Policy

This study was positioned in the Public Policy domain and sought to follow through literature on the link between techno-logics, computerization and frameworks for decision-making. The student was employed in Government and intended to follow the development and implementation of health policy. The proposal was a strong one based on the student’s two honour’s degrees, access to a data field, and personal motivation to do the research. The student enrolled using the proposal in a leading US University.

5.4 eBusiness Adaptive Work Surfaces

This study was to build on the research I started several years ago measuring and forecasting eBusiness performances. The student was specifically interested in customer profiling and altering the customer work surface and business opportunity according to stored profile information, streaming data, and neural networking extrapolation. The student was well qualified but failed the English entry test and had difficulty raising a literature to support the proposal. It is a study I would like to see happening in the future and would support further proposals in this area.

5.5 Economics and Information Technology – Cyboid Culture

This proposed study had a strong philosophical foundations and literature that had come through from the students masters degree. The proposal was to explore the linkages of cyboid-culture (see explanation above) and decision-making, and to build models to explain current business dependency on computerized information. The hypothesis was that better economic management can come from knowing cyboid culture. The student was again lost to the work place but hopes to return to PhD in the future.

5.6 Mental Mapping and Systems Alignment

This was a well-developed proposal from a mature researcher. It had a strong literature background, a research question, a research instrument, information systems field positioning, and forecasted outcomes. All the researcher had to do was to formalize the methodology, collect data, and write up the thesis. The student enrolled and is well on the way to completion.

5.7 Airline Systems Competitive Advantage

This proposal arose from the student’s master’s studies and work place observations. The proposal is a recent one and will be presented in 2004 for consideration. The hypothesis is that there has been no structural change in airline information systems since their inception in the 1930s. The student is currently gathering a sufficient literature on information systems in airlines and seeking to position the research in the literature.

5.8 Logics for Data Base Design

This proposal was a carefully thought out approach to solving some of the many problems that arise from current data base design and insensitive treatment of social data. The student’s critical reflection and motivation was to seek solutions for data storage of Maori land claims and rights. This is a hot topic centred on issues arising from culture and technology. The formal research starting date is late 2003.

6. Conclusion

Writing a PhD is a business not a hobby. The resources and constraints involved cost any candidate dearly in terms of time, personal stress and sacrifice, finance, and opportunity loss on the many other alternative things that could be accomplished. One of the best metaphors is that of a project. All costs need to be estimated, everything calculated and planned for – activity-by-activity and milestone-by-milestone. In this paper I have concentrated on the academic side of the project and hopefully identified the bigger picture and ways of working into the bigger picture by understanding the context of computing knowledge.

A big part of PhD study is personal growth. The challenge, the opportunity and the exposure to the best researchers in the world, is a vigorous environment in which to grow. Access to many of the network opportunities comes through the supervisor and with the immediate access to information and researchers over the Web there is no reason a PhD candidate in any institution cannot be in touch with the best. Part of the learning curve is to grasp all the different forms of communication used in the field to promote and deliver knowledge value. In addition to various publication forms and formats (all have a ranking) there are copious opportunities to communicate in forums, seminars, symposiums, conferences, guest lectures, and so on and so on. All these occurrences are part of the greater personal development expectation and knowledge management capacity intended for those contributing to computing knowledge growth at this level.

The key to a successful PhD is differentiation. The standard processes and experiences a candidate goes through are all designed to differentiate the work from others and to make the findings clearly different from others who have been involved in a similar area of study. Hopefully here by raising the importance of reflecting on the broader context of study – the epistemological foundations of the field, some ideas have been hatched as to how to enter studies, to develop a convincing proposal, and to mature a project. The combination of personal motivation, research capacity, and persistence in the face of difficulties over an extended period of time can lead to a defensible theory of knowledge.

References

Churchland, P. (1989). A Neuro-computational perspective: The Nature of Mind and the Structure of Science. Cambridge: MIT Press.

Heidegger, M. (1977). The Question concerning technology and other essays. Translated by Lovitt, W. (1977). New York: Harper and Row.

Hirst, P. (1974). Knowledge and the curriculum. London: Routledge & Kegan Paul.

Hodgkinson, C. (1983). The Philosophy of leadership. Oxford: Basil Blackwell.

Lyotard, J. (1979). The Postmodern condition: a report on knowledge. Translated by Bennington, G., & Massumi, B. (1984), Minneapolis: University Press.

O’Conner, D., & Carr, B. (1982). Introduction to the theory of knowledge. Sussex: Harvester Press.

O’Hear, A. (1989). An Introduction to the philosophy of science. Oxford: Oxford University Press.

Peters, M. (1989). Techno-Science, rationality, and the university: Lyotard on the post-modern condition. Educational Theory,. 39 (2), 93-105.

Quine, W. (1976). Mind and language. Oxford: Oxford University Press.

Turban, E. & Aronson, J. (1998). Decision support systems and intelligent systems. New York: Prentice-Hall.

Turing, A. (1950). Computing machinery and intelligence. Mind, 51, 433-460.

Bulletin of Applied Computing and Information Technology Vol 1, Issue 2 (December 2003). ISSN 1176-4120.

Copyright © 2003, Brian O. Cusack
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