سیستم های خبره برای مدیریت دانش: عبور از شکاف بین پردازش اطلاعات و ایجاد حس
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|11857||2001||10 صفحه PDF||سفارش دهید||7450 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 20, Issue 1, January 2001, Pages 7–16
Based on insights from research in information systems, information science, business strategy and organization science, this paper develops the bases for advancing the paradigm of AI and expert systems technologies to account for two related issues: (a) dynamic radical discontinuous change impacting organizational performance; and (b) human sense-making processes that can complement the machine learning capabilities for designing and implementing more effective knowledge management systems.
The narrative cited above as an observation by the noted psychologist and computer scientist John Holland was in response to my query to him regarding the possibility of using intelligent information technologies for devising self-adaptive organizations. As meaning seems to be a crucial construct in understanding how humans convert information into action [and consequently performance], it is evident that information-processing based fields of artificial intelligence and expert systems could benefit from understanding how humans translate information into meanings that guide their actions. In essence, this issue is relevant to the design of both human- and machine-based knowledge management systems. Most such systems had been traditionally based on consensus and convergence-oriented information processing systems, often based on mathematical and computation models. Increasing radical discontinuous change (cf. Huber and Glick, 1993 and Nadler et al., 1995) that characterizes business environments of today and tomorrow, however, requires systems that are capable of multiple — complementary and contradictory — interpretations. Despite observations made by Churchman, 1971 and Mason and Mitroff, 1973, the paradigm of information systems, artificial intelligence (AI) and expert systems have yet to address the needs posed by wicked environments that defy the logic of pre-determination, pre-diction and pre-specification of information, control and performance systems (cf. Malhotra, 1997). Wicked business environments — characterized by radical discontinuous change — impose upon organizations the need for capabilities for developing multiple meanings or interpretations and continuously renewing those meanings given the changing dynamics of the environment. Scholars in business strategy have advocated human and social processes such as ‘creative abrasion’ and ‘creative conflict’ (cf. Eisenhardt et al., 1997 and Leonard, 1997) for enabling the interpretive flexibility ( Nonaka & Takeuchi, 1995) of the organization. It is also evident that there is an imperative need for relating the static notion of information captured in databases or processed through computing machinery to the dynamic notion of human sense making. More importantly, our current understanding of information as the [indirect] enabler of performance can immensely benefit from unraveling the intervening processes of human sense making that are more directly related to action (or inaction) and resulting performance outcomes (or lack thereof). Based upon a review of the current state of AI and expert systems research and practice in knowledge management, this article develops the bases for AI and expert systems researchers to develop knowledge management systems for addressing the above needs. Section 2 provides an overview of the state-of-the-art expert systems research and practice issues related to knowledge management highlighting key relationships with the key theses of the article. Section 3 offers a more current understanding of knowledge management as it relates to organizational adaptability and sustainability by drawing upon information systems and business strategy research. Section 4 highlights the contrast between the computational model of information processing and human sense making while recognizing both as valid meaning making processes. Finally, sense-making bases of human action and performance are discussed in Section 6, followed by conclusions and recommendations for future research in Section 8.
نتیجه گیری انگلیسی
The motivation of this article was the need to suggest how AI and expert systems research and practice can improve their relevance for the design of effective knowledge management systems implementations by addressing issues that are critical to business performance. Based upon information science, strategy and organizational science practice and research, this article underscores the need for designing AI and expert systems for knowledge management by accounting for wicked business environments that defy the programmed logic based upon pre-specification, prediction and pre-determination. In addition, the article also develops an in-depth bases of human sense making processes that characterize human meaning making capabilities underlying the translation of information into knowledge and finally into performance. For advancing the state of research and practice on AI and expert systems as related to knowledge management, the article also answered the questions: what's being human? and what is the contrast between human learning and machine learning? The personal constructivist theory was suggested as one foundation for understanding the processes of meaning making in human beings. The related sense-making model of human meaning making is also supported by observations of other scholars who have approached it from other psychological perspectives. For instance, Bruner (1986) has suggested that humans often (pp. 51–52) “suspend disbelief”… in order to construct “multiple perspectives and possible worlds…“ and considers the individual as (p. 3) “one who actively selects information, forms… hypotheses and on occasion distorts the input in the service of reducing surprise and of attaining [understanding]”. In congruence with the observations of the PCT, he is also critical of the existing conceptual split between the constructs of thought, action and emotion currently prevalent in the information processing view. To him the three aspects represent an integrated whole ( Bruner, 1986, pp. 117–118): Emotion is not usefully isolated from the knowledge of the situation that arouses it. Cognition is not a form of pure knowing to which emotion is added… [and] action is a final common path based on what one knows and feels. The three constitute a unified whole… To isolate each is like studying the planes of a crystal separately, losing sight of the crystal that gives them being. He also underscores the importance of linkages “between emotion, arousal, drive on the one side and learning, problem solving, thinking on the other” (1986, p. 113) for developing an understanding of how humans construct meanings. The essence of the discussion is that the world of business is encountering not only unprecedented pace of change but also radical discontinuities in such change that make yesterday's proven rules of behavior and models underlying such behavior increasingly vulnerable. The information processing view, evident in scores of definitions of knowledge management in the trade press and academic texts, has often considered organizational memory of the past as a reliable predictor of the dynamically and discontinuously changing business environment. Most such interpretations have also made simplistic assumptions about storing past knowledge of individuals in the form of routinized programmable logic, rules-of-thumb and archived best practices in data bases for guiding future action. However, as discussed in the article, there are major problems that are attributable to the information-processing view of information systems. The current paradigm of AI and expert systems technologies needs to overcome the constraints of their rule-based and model-based characteristics of Lockean and Leibnitzian systems. Future evolution of these technologies needs to overcome the limitations inherent in the information processing logic. Based on the discussions in the article, three areas of research are recommended to address existing gaps in knowledge and conflicting inter-disciplinary assumptions about knowledge and its management with the aid of new information technologies. First, AI- and expert systems-based knowledge management technologies are often purported to deliver the right information to the right person at the right time. However, new business models marked by radical and discontinuous changes make the task of predicting the right information, the right person or the right time challenging as the notion of “right” keeps shifting. Second, AI and expert systems technologies are often based upon the assumptions of storing human intelligence and experience. However, prior discussion that contrasted the information processing and sense-making views suggests otherwise. Technologies such as databases and groupware applications store static bits and pixels of data, but they cannot store the rich schemas that people possess for making dynamic sense of data bits. Also, the static representations of data in databases, inferential logic of computer programs and computer memories lack inherent dynamic meaning making capabilities that are increasingly relevant for emerging business environments. In contrast, given the dynamic subjective nature of human construction of meaning and the diversity of personal constructions, different meanings could be constructed from the same assemblage of data at the same time by different individuals. Likewise, different meanings could be construed at different times, or by consideration of different contexts by the same person. Hence, storing a static representation of the explicit representation of a person's tacit knowledge in the form of data bits — assuming one has the willingness and the ability to part with it — cannot be considered tantamount to storing human intelligence and experience. Finally, it has been often asserted that AI and expert system technologies can distribute human intelligence. Again, this assumes that companies can predict the right information to distribute and the right people to distribute it to. Even when information is archived in a database or intranet, or it is pushed to individuals’ mailboxes or desktops, it may be ignored as increasingly attention is the scarce resource often overwhelmed with information overload. Moreover, the data archived in technological ‘knowledge repositories’ is rational, static and without context, and such systems cannot account for renewal of existing knowledge and creation of new knowledge.