Knowledge dependencies in fuzzy information systems evaluation

Experience and research within the field of Information Systems Evaluation (ISE), has traditionally centered on providing tools and techniques for investment justification and appraisal, based upon explicit knowledge which encodes financial and other direct situational factors (such as accounting, costing and risk metrics). However, such approaches tend not to include additional causal interdependencies that are based upon tacit knowledge and are inherent within such a decision-making task. The authors show the results of applying a cognitive mapping approach, in the guise of a Fuzzy Cognitive Mapping (FCM) simulation, i.e. Fuzzy Information Systems Evaluation (F-ISE), in order to highlight the usefulness of applying such a technique. The authors highlight those contingent and necessary knowledge dependencies, in an exploratory sense, which relate to the investment appraisal decision-making task, in terms of the interplay between tacit and explicit knowledge, in this regard

What’s Wrong with Contemporary Economics?

It is argued that in educating economists we should sacrifice some of the more technical aspects of economics (which can be learned later), in favour of the compulsory inclusion of (a) philosophy, (b) political science and (c) economic history. Three reasons for interdisciplinary studies are given. In the discussion of the place of mathematics in economics fuzziness enters when the symbols a, b, c are identified with individuals, firms, or farms. The identification of the precise symbol with the often ambiguous and fuzzy reality, invites lack of precision and blurs the concepts. If the social sciences, including economics, are regarded as a “soft” technology compared with the “hard” technology of the natural sciences, development studies have been regarded as the soft underbelly of “economic science”. In development economics the important question is: what are the springs of development? We must confess that we cannot answer this question, that we do not know what causes successful development.

A gap in competencies or in capabilities?: the role of regional universities in developing scientific and technological skills in Campania

A gap in competencies or in capabilities?: the role of regional universities in developing scientific and technological skills in Campania The paper assesses the role of universities in resolving the STEM (Science, Technology, Engineering and Mathematics) skills gap in the Campania region of Southern Italy. The results are shown to hinge on a doubled supply/demand model, involving a first upstream stage (logically if not chronologically) of derived demands for and supplies of STEM-based skill development within universities, and a second downstream stage of the usage of these skills in industrial firms. The main objective of this work is to re-examine the role of conventional ‘knowledge capital’ arguments for the role of universities in development processes in catching-up regions of the EU – i.e. human capital and R&D capital, or what will be identified here as ‘competencies’ – as against what we refer to as ‘capabilities’ arguments, reflected here in better ways in which universities might adapt to the actual needs of industry for highly skilled workers and research outcomes. The results suggest that the STEM skills gap is not clearly a deficiency just in capabilities, but more so in the links between capabilities and competencies. Moreover, the STEM universities are trying to feed the interaction with industry, however it is still left mostly to the personal relationships of the professors or their administrative counterparts, e.g. head of the T&T office, and/or to placement. Key words: Derived demand and supply, STEM subjects, Mezzogiorno region, skills gap, competencies and capabilities.

QUALITATIVE ANSWERING SURVEYS AND SOFT COMPUTING

In this work, we reflect on some questions about the measurement problem in economics and, especially, their relationship with the scientific method. Statistical sources frequently used by economists contain qualitative information obtained from verbal expressions of individuals by means of surveys, and we discuss the reasons why it would be more adequately analyzed with soft methods than with traditional ones. Some comments on the most commonly applied techniques in the analysis of these types of data with verbal answers are followed by our proposal to compute with words. In our view, an alternative use of the well known Income Evaluation Question seems especially suggestive for a computing with words approach, since it would facilitate an empirical estimation of the corresponding linguistic variable adjectives. A new treatment of the information contained in such surveys would avoid some questions incorporated in the so called Leyden approach that do not fit to the actual world.Computing with words, Leyden approach, qualitative answering surveys, fuzzy logic

Advancing the state of the art in the modelling and simulation of information systems evaluation

It is widely accepted that Information Systems Evaluation (ISE) is a powerful and useful technique that can be used to assess IT/IS investments in an a-priori or a-posteriori sense. Traditional approaches to ISE have tended to centre upon financial and management accounting frameworks, seeking to reconcile tangible and intangible costs, benefits, risks and value factors. Such techniques, however, do not provide the IS researcher or practitioner with further insight or appreciation of any inherent and implicit inter-relationships, in the investment justification process. Thus, this paper outlines and discusses via a taxonomy and resulting classification, alternative and complementary approaches that can be applied to ISE from the fields of Artificial Intelligence (AI), Operational Research (OR) and Management Science (MS). The paper subsequently concludes that such approaches can be potentially used by researchers and practitioners in the field, as a basis for carrying out further research in the field of applied ISE

New perspectives on realism, tractability, and complexity in economics

Fuzzy logic and genetic algorithms are used to rework more realistic (and more complex) models of competitive markets. The resulting equilibria are significantly different from the ones predicted from the usual static analysis; the methodology solves the Walrasian problem of how markets can reach equilibrium, starting with firms trading at disparate prices. The modified equilibria found in these complex market models involve some mutual self-restraint on the part of the agents involved, relative to economically rational behaviour. Research (using similar techniques) into the evolution of collaborative behaviours in economics, and of altruism generally, is summarized; and the joint significance of these two bodies of work for public policy is reviewed. The possible extension of the fuzzy/ genetic methodology to other technical aspects of economics (including international trade theory, and development) is also discussed, as are the limitations to the usefulness of any type of theory in political domains. For the latter purpose, a more differentiated concept of rationality, appropriate to ill-structured choices, is developed. The philosophical case for laissez-faire policies is considered briefly; and the prospects for change in the way we ‘do economics’ are analysed

"The connection between distortion risk measures and ordered weighted averaging operators"

Distortion risk measures summarize the risk of a loss distribution by means of a single value. In fuzzy systems, the Ordered Weighted Averaging (OWA) and Weighted Ordered Weighted Averaging (WOWA) operators are used to aggregate a large number of fuzzy rules into a single value. We show that these concepts can be derived from the Choquet integral, and then the mathematical relationship between distortion risk measures and the OWA and WOWA operators for discrete and nite random variables is presented. This connection oers a new interpretation of distortion risk measures and, in particular, Value-at-Risk and Tail Value-at-Risk can be understood from an aggregation operator perspective. The theoretical results are illustrated in an example and the degree of orness concept is discussed.Fuzzy systems; Degree of orness; Risk quantification; Discrete random variable JEL classification:C02,C60