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Information and Communication within Organizations
Organizat!onal Knowledge Systems: An Investigation on Knowledge Communication Uncertainty Dubravka C E C E Z - K E C M A N O V I C Elektrotehni?ki fakultet Sarajevo, Univerzitet u Sarajevu, 71000 Sarajevo, Lukavica, Jugoslavia
Penetration of new information technologies in organizational working and decision-making processes has increased the number of locations where some kinds of knowledge are acquired, processed and used for local needs and purposes. These local implementations of information technologies--socalled computer-based knowledge systems--usually support production control, maintenance operations, office activities, marketing department, research and development, etc. Unlike global information systems, that are planned and developed to meet organizational needs, these local knowledge systems are
Dubravka (~e~ez-Kecmanovi~ is a Professor of Information Systems and Head of Catedra for Information Systems of the Faculty of Electrical Engineering, University of Sarajevo, Yugoslavia. She is also a president of the Coordination Committee of the Republic Information System. She holds B.Sc. in Engineering from the Faculty of Electrical Engineering, Iv University of Sarajevo, an M.S. in Systems Sciences from the Center of Multidisciplinary Studies, University of Belgrad and a Ph.D. in Computer Sciences and Information Systems from the Faculty of Electrical Engineering, University of Ljubljana. She conducted several research projects in the theory and methodology of information system development and social system of information in Yugoslavia. She is the author of the book Social System of Information: Concept, Models and Technologies (Sarajevo, 1987). Her research has lately concentrated on intelligent information systems and decision support systems with the emphasis on knowledge representation. She has engaged as an advisor to the government of Socialist Republic of Bosna and Hercegovina in the field of legislation and development of social system of information. She has also consulted with many institutions concerning development of their information systems. She is a member of IFIP W.G. 8.3 on Decision Support Systems. North-Holland Computer Networks and ISDN Systems 14 (1987) 221-230
evolving spontaneously, without prior overall planning, often even developed by end-users. Although local knowledge systems can improve performance of their departments, offices, working groups (i.e. work units), their contribution to the overall organizational performance is questionable due to knowledge communication uncertainty. Knowledge communication uncertainty is defined here as the new arising organizational problem. The needs for knowledge communication among different work units are coming from their mutual dependence and necessity to cooperate and coordinate their activities. The required level of knowledge communication is determined by the complexity of their tasks. The four-level model of knowledge communication is developed. The higher knowledge communication level is required the lower knowledge uncertainty is allowed. A three-dimensional framework ("knowledge representation structure" / "problem representation" / "formal organization structure") is suggested for the description of organizational knowledge systems and different aspects of communicating knowledge. Monitoring and analysis of the overall architecture of organizational knowledge systems enable recognition of the needs for improved communications, identification of sources of knowledge uncertainty and hence support organizational change toward higher knowledge communication levels.
Keywords: Organizational Knowledge Systems, Knowledge Based Systems, Decision Support Systems, Knowledge Communication, Knowledge Communication Uncertainty, Multiple-Source Information Fusion.
1. Introduction
Designing organizations as "human activity systems" [3] includes not only formal structures, roles and responsibilities and communication links, but also knowledge systems structures, the content of knowledge accumulated in different points in organization and knowledge communicated between them. The diffusion of information technologies in organization has increased the number of
0169-7552/88/$3.50 © 1988, Elsevier Science Pubhshers B.V. (North-Holland)
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places where some capacity for information processing is installed and used. There is a tendency to furnish each task or activity with the appropriate work station, more or less connected to each other. Some kinds of knowledge, generated during task or activity performance, are captured and organized within such work stations. The captured knowledge usually supports working and decision-making activities such as office work, production management, experts' work or managerial and executive decision-making. Corresponding "computer-based knowledge systems" serving these working a n d / o r decision-making processes are respectively called: Office Systems, Production Management Information Systems, Expert Systems or Decision Support Systems. These knowledge systems are evolving locally, performing given tasks within the work units. Consequently, their purpose and content are determined by specific local needs, inside the boundaries of the work units. Since these knowledge systems are developed locally, usually by local professionals and managers, they express their "world views", as well as their motives and goals. The knowledge accumulated in "data bases", "knowledge bases" and "model bases" represents their understanding of things and happenings not only in their work unit, but also in their environment--the other units in the organization and outside world, the activities of which are related to theirs. Unlike global organization information systems that are planned and developed systematically, those local implementations of information technologies grow more spontaneously, in evolutionary processes. The resulting local knowledge systems, dispersed throughout the organization, without prior overall design, differ in scope, content, semantics and purposes. In this respect they are open for inconsistencies, overlappings, and incompatibilities among themselves which are not known in advance, and are difficult to discover before the accidental matching. Do they actually meet the organizational needs? The work units cooperate in order to perform complex tasks. Cooperation requires communication of knowledge between the work units involved. Local knowledge systems are, among others, the sources of knowledge relevant for cooperation. This implies that, besides the local needs, knowledge systems should satisfy the re-
quirements coming from cooperation with other units. Moreover, in the organization facing an unstable environment the task execution and cooperative patterns among work units are restructuring in order to respond to changing conditions. Greater flexibility and adaptiveness of the organization place higher requirements on knowledge communication between the work units. The needs for knowledge communication can be specified with low risk for well-structured cooperation between the work units and also for semi-structured repetitive activities (e.g. planning processes with different work units involved). For less structured, nonrepetitive organizational cooperative and coordination activities the needs for knowledge communication are not predictable and hence the requirements for knowledge availability could not be specified in advance. As a consequence, when a new problem has to be solved, by one or many work units, the organization sources have to be searched for relevant knowledge. Availability of relevant knowledge has two aspects: whether the work unit has information about various knowledge sources (such as knowledge systems) in the organization and whether the accessible knowledge has meaning for the users in this particular work unit. In this respect, knowledge from different sources, dispersed in organization, is not usable with certainty: there is uncertainty about the content of knowledge sources and the uncertainty about the meaning of accessible knowledge. Only one aspect of knowledge uncertainty in organizations has been treated so far--integration of information from multiple knowledge sources. This problem, which is also called "information fusion", appeared in the design of more sophisticated computer-based decision-aiding systems, relying on information from diverse sources. Since these knowledge sources may provide inconsistent, even conflicting information, the problem of computer-based interpretation and integration of information is difficult to solve. Current approaches to the problem are based on probability theory [13], possibility theory [12], theory of belief functions [6], information theory [2], etc. (for a survey of approaches, see [2]). The problem of integration of multiple-source information is defined assuming that the characteristics of sources, such as their content and reliability and modes of knowledge representation,
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are known. Although this problem implicitly comprises an aspect of knowledge uncertainty--interpretation of information from different sources-knowledge uncertainty has not been identified as the problem per se. We shall define here the problem of knowledge uncertainty as it appears in the organizational communication. The origin of knowledge uncertainty in communicating knowledge between the work units and its manifestation and consequences on organizational performance will be examined in the next section. The section to follow investigates the needs for knowledge sharing among work units and the requirements for knowledge communication depending on the complexity of tasks. A model of four different levels of knowledge communication is developed. The higher knowledge communication level is required the lower knowledge uncertainty must be. The fourth section addresses the analysis of organizational needs for knowledge communication with the aim to provide a basis for developing an adequate framework for investigation of organizational knowledge systems and knowledge uncertainty. Whether a degree of knowledge uncertainty is dissatisfactory could only be judged with respect to the required knowledge communication level. How to investigate organizational knowledge sources and the needs for decreasing knowledge uncertainty is discussed briefly in the fifth section, after which follows the concluding section.
2. Uncertainty of Knowledge Knowledge uncertainty existed even before the application of information technologies in organiz a t i o n s - i n manual systems for knowledge representation. In various f o r m s - - p a p e r files, reports, memos and the like--structured information as well as informal statements were registered. Developed mostly as personal resources, these manual forms of knowledge representation contain personal views, opinions and understanding. Such personal knowledge resources were hardly accessible and are not easily understandable by others than creators (or "resources' owners"), which resuits in high knowledge uncertainty. The formal reporting mechanisms and informal communications increased accessibility and partially resolved
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or attenuated inconsistencies and mismatching in understanding and explanations. Nevertheless, the basic differences and inconsistencies in manual representations of knowledge, coming from people's minds, remained unidentifiable. However, the potential power of manual systems to affect work and decision processes was very limited which might explain why knowledge uncertainty has not been recognized as a significant organizational problem. When personal knowledge, expressing certain views, opinions, values and attitudes, is embedded in a knowledge system based on information technologies, it becomes available for other users in local site, as well as in other places in the organization. The possible power of these knowledge systems to affect people's jobs and roles and work performances, as well as overall organizational processes, is substantially increased. This is why the uncertainty of knowledge availability and communication within the structure of organizational knowledge systems is emerging as a significant organizational uncertainty. In order to examine knowledge uncertainty human activity systems are analyzed in terms of work units which perform a well-determined and bounded set of tasks and activities and produce specific output--material or informational. For example, the production process at shop floor level is organized in a number of production units in which input flows of material and parts are transformed into output flows of products. Similarly, at office floor level, office units accept input documents and information and produce output documents and information. An elementary work unit is the smallest entity organized to perform a specific (material or document) transformation process. The elementary work units are connected to form more complex work units, such as sectors or divisions, that perform more global tasks to achieve higher goals. The requirements for communication of the pieces of knowledge, generated in different work units, are caused by the interdependence of work units; those, performing interconnected transformation processes in a sequential or network like structure, exercise a horizontal type of communication and those performing a complex task or solving a common problem, that are hierarchically structured, require vertical communications. The formal organization structure enables some of the
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necessary communications to take place, by means of information systems reporting and sometimes manual reporting. When required and necessary communications are not formally established, they may or may not be exercised by informal communications among work units. Knowledge necessary for performing an activity in a particular work unit is available partially from its local knowledge system which contains, e.g. description of tasks, standards, methods and rules for tasks executions, checking and validation procedures, plans and schedules for operations, rules for handling exceptions, etc. On the other hand, information about higher organizational goals and constraints as well as knowledge about local goals' contributions to these higher goals are not usually found in the local knowledge system of the work unit. This is also the case with the knowledge about changes of plans, available resources and different experiences in other work units, which might be useful for a specific activity or problem solving in a particular work unit. These kinds of knowledge, external to the work unit, should be available from other work u n i t s - - o f t e n from their local knowledge systems. However, for any work unit, in general, there is uncertainty as to the availability of relevant knowledge throughout the distributed knowledge systems in the organization. First, there is uncertainty about the content of knowledge sources in the organization, which we shall name structural knowledge uncertainty. The degree of structural knowledge uncertainty is defined for each work unit as its measure of sureness about the content of the whole structure of knowledge systems in the organization. Second, in a particular work unit for any piece of knowledge, available from other knowledge systems, there is uncertainty that it will be properly communicated, i.e. without distortion of meaning, in a compatible mode of presentation, consistent with already possessed knowledge. This semantic knowledge uncertainty is specific for each user (or for a work unit, if we assume all the users in a work unit to possess similar semantic structures) and for each knowledge system. In a particular work unit the degree of semantic knowledge uncertainty for its own local knowledge system is equal to zero, and for all other knowledge systems between zero and one. A failure to (properly) communicate knowledge
to a certain work unit has numerous dysfunctional consequences, such as the inability of the work unit to detect emerging problem situations and to understand and predict their effects, and also negligence of some alternatives and solutions and suboptimization in problem solving. More generally, inadequate sharing of organizational knowledge has the effect that the organization as a whole lacks coordination and cooperation in its responses to its environment. Although, these independently evolving local knowledge systems can improve performance of local work units and aid to the attainment of local goals, a loosely coupled structure of organizational knowledge systems may effect a decrease of the overall organizational performance, due to high degree of knowledge uncertainty. This does not, however, require organizational knowledge systems to be totally integrated. What it implies is the necessity for an overall systems view of existing needs for knowledge sharing and communication throughout the organization. This systems view will not only give the basis for the investigation of the structure of knowledge systems and their inherent knowledge uncertainty, but will also increase the awareness of the benefits of improved knowledge communication.
3. Understanding the Needs for Knowledge Sharing The need for knowledge exchange between the work units is determined by their mutual interdependence in performing their functions and managing their activities. For example, in a production work unit, where a production can be started upon receipt of the material flow from a preceding production unit (as in Fig. l(a)), there is a need for information about the completion of the preceding unit's activities. Many work units are connected in such a way that starting the process or function in a work unit requires some other processes or functions to be finished. According to the information about the completion of the preceding operations or activities, the particular work unit function can be operationally planned and performed (at shop floor or office floor level). The exchange of information enables successful coupling of work units Work units can be interconnected not only by
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SHOP FLOUR
a) E X H A N G E OF I N F O R M A T I O N C O U P L I N G OF WORK U N I T S
ENABLES
'..@/ b}
E X C H A N G E OF KNOWLEDGE IS R E Q U I R E D FOR C O O R D I N A T I O N A N D P L A N N I N G A C T I V I T I E S
C) USAGE OF KNOWLEDGE OF O T H E R S E N A B L E S PRODUCT IMPROVEMENT AND INNOVATION
d)
C R E A T I O N OF NEW IDEAS A N D S T R A T E G I C A C T I O N S R E Q U I R E S KNOWLEDGE I N T E R A C T I O N AMO NG D I F F E R E N T I N T E R E S T E D GROUPS
Fig. 1. Requirements for knowledge sharing dependent on the complexity of tasks.
material or information flows but also by more abstract activities. The output of a complex work unit composed of two or more elementary units depends on the quality of each sub-unit's performance, as well as on the planning and coordination of all work units involved (see Fig. l(b)). The exchange of just information between work units does not satisfy the requirements in these complex activities. Apart from information about the activities of other units, knowledge about resources
management, c o m m o n goals to be attained in a planning period, and constraints imposed locally as well as from outside, are required in order to discover feasible alternatives and predict likely outcomes. The exchange of knowledge in such interconnected work units is vital for their successful performance. The complex work units, globally dependent on each other in the long run, can plan and manage their overall performance not only by means of
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might assume that organizational knowledge systems and their connections should provide for any required communication level. Once the requirements for knowledge communication levels among work units are defined, one might expect that the only thing missing is an adequate knowledge systems structure. However, increasing knowledge communication level, i.e. climbing the "staircase" in Fig. 2., assumes decrease of knowledge uncertainty and hence is a kind of learning process. It requires that in order to attain higher communication levels, one must first succeed to communicate at lower ones. This is why we cannot start with the specifications for knowledge exchange and immediately build the knowledge structure to fulfill them. We should rather identify knowledge resources in the organization and develop a model of a structure of organizational knowledge systems which will support learning processes and climbing the staircase. The recognition of the knowledge systems structure should help the creation of the "knowledge environment" in work units that will enable the investigation of possibilities for getting and using relevant knowledge from others and support understanding of the needs for knowledge sharing. If an organization seeks for improvement of the knowledge communication, investigation of knowledge uncertainty seems inevitable. A model for representing organizational knowledge systems
knowledge exchange between the work units, but also by using and relying upon each other's knowledge. For example Fig. l(c) shows a research and development department that has to use specific knowledge from production management, marketing and product servicing departments in order to be able to improve products according to market demands and experiences in production and product servicing. Still greater requirements for knowledge interaction among different managers and professionals appear in management processes involving creativity and innovation, such as strategic decision-making shown in Fig. l(d). Knowledge interaction for the creation of new ideas and innovation characterizes the highest level of communication. The four levels of knowledge communication among work units described above are distinguished (following Aamodt's model of interpersonal communication levels, [1] and [10]) as a function of the complexity of activities (see Fig. 2). The emphasis here is on knowledge communication among work units throughout the organization. The required level of knowledge communication is determined by the complexity of functions and activities and the corresponding needs for internalization of knowledge of others. Following common approaches to the development of information systems (see e.g. [5]), one
KNOWLEDGE COMMUNICATION LEVELS KNOWLEDGE INTERACTION
KNOWLEDGE USAGE
EXCHANGE OF K N O W L E D G E
EXCHANGE OF INFORMATION
! I ! ! !
I I I I
I I I I
I
I
I I
I
COMPLEXITY OF ACTIVITIES
Fig. 2. Stages of knowledge sharing (adapted Aamodt's model).
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would enable a systematic research of knowledge uncertainty. In the next section we shall propose a framework for knowledge systems representation.
4. Representing Organizational Knowledge Systems
There are several aspects of knowledge acquisition, usage, and communication which might be of interest: - what kinds of knowledge are acquired and for what purpose, -where, at which locations, knowledge is acquired, - how the pieces of knowledge at each location are represented and structured (what concepts and relationships are used, what are their meanings), - what problem space is supported by knowledge organized within each location, - to whom the knowledge is intended and how it is actually used, - what kinds of knowledge are communicated to other locations and when, - what are the conditions for knowledge communication and how is this knowledge, communicated to other locations, integrated into its local knowledge structure. Different structures are required to represent all these aspects of knowledge systems in organizations. In the first place we need an adequate structure to represent the content of organizational knowledge systems, which will allow the discrimination of kinds of knowledge, representation of concepts and their interrelationships contained in knowledge systems, as well as their meaning within the limits of related comprehension space. An organization systems morphology model is developed for modelling organization knowledge structures and for analysis of content, semantics and possible interconnections of different knowledge systems [4]. It also enables consistency checking and discovering of incompatibilities among different knowledge systems. Representing the problem space in an organization requires another modelling scheme to be applied. The focus is on problem specification and classification determined by modes of the cognitive activity of managers. The multi-level problem
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representation scheme, described in [7] and [8], is a candidate problem structuring framework. The problem space is classified according to five qualitatively different levels of abstraction, related to those proposed by Jacques [9], and also consistent with Piaget's stages in cognitive development [11]. The knowledge content of the decision problem at the particular level of abstraction can be described in two dimensions: problem structure and knowledge representation structure. These define a two-dimensional plane, that allows investigation of problem structuring languages in which problem domains can be specified (see Fig. 3(a)). Knowledge systems are located in certain departments, offices or more abstractly, in work units, as we discussed above. The most appropriate way to locate a knowledge system is by a formal organization structure, which defines division of work and assignment of authority and responsibility to each position. Locations within the formal organization structure are not only hierarchically interconnected (by manager-subordinate finks) but are also laterally interrelated (by advisory and coordinating relations). This aspect of formal organizational structure along with knowledge representation structure create another two-dimensional plane whereby local knowledge systems and the content of communication between these systems can be described (as shown in Fig. 3(a)). The third plane is determined by the dimensions of the formal organization structure and the problem space structure, where each location is related to a problem domain, at a certain abstraction level. This " p r o b l e m / l o c a t i o n " plane allows examination of role descriptions and role boundaries. However, locations are occupied by managers, with particular personal characteristics, goals, motives and ambitions, participating in certain informal communications. The boundaries of the actual, socially defined roles, that do not necessarily coincide with those formally defined, can also be described and examined with the " p r o b l e m / l o c a t i o n " plane. This plane is relevant since it enables analysis of the usage and purpose of intended and actual knowledge systems. Local knowledge systems can overlap because of overlapping roles and fuzzy boundaries. They may serve antagonistic ends and be incompatible due to unclear or disturbed role relationships.
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KNOWLEDGE REPRESENTATION STRUCTURE
2
DESCRIPTION OF LOCAL KNOWLEDGE STRUCTURES AND COMMUNICATION
FORMAL ORGANIZATION STRUCTURE DELIMITATION OF LOCAL PROBLEM DOMAINS (problem/location, unit boundaries, role boundaries )
STRUCTURE a)
THREE-DIMENSIONAL SPACE OF K N O W L E D G E S Y S T E M S
FOR
DESCRIPTION
KNOWLEDGE REPRESENTATION STRUCTURE
!iii!!ii!!i FORMAL I~ ORGANIZATION STRUCTURE
STRUCTURE b) LOCAL KNOWLEDGE SYSTEM OCCUPIES A REGION IN THREE-DIMENSIONAL SPACE
Fig. 3. The frame for the analysis of organizational knowledge systems.
The three planes are integrated as suggested in Fig. 3. Each plane represents a specific projection of knowledge systems. The integral view of the knowledge system is generated over three orthogonal dimensions--knowledge representation structure, problem space structure and formal organization structure. A particular work unit's local
knowledge system can be described as a specific region in this space (Fig. 3(b)). The three-dimensional scheme provides a frame complex enough to describe organizational knowledge systems and to discriminate manifold aspects of knowledge communication. On the other hand, the frame itself consists of reasonably simple
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structures, which, combined together, enables creation of the complex picture.
5. How
to Investigate
Knowledge
the local knowledge system (Cycle 3) and of the overall structure of organizational knowledge systems (Cycle 4).
Uncertainty 6. Concluding
In order to diagnose the degree of knowledge uncertainty and initiate organizational change to decrease it, a systematic analysis of the whole human activity system and its knowledge resources is necessary. This analysis should take, at least, the following cognitive directions: Cycle 1.
- identification of "local problem space" (nominal and factual) and analysis of perceived needs for relevant knowledge, - a n a l y s i s of available knowledge from local knowledge systems and knowledge communicated from other systems, - -evaluation of available knowledge; Cycle 2.
problem space analysis and experimental representation in a problem-structuring language, -analysis of knowledge structures that characterize the problem type, - e v a l u a t i o n of knowledge needs perceived through learning;
-
Cycle 3. -
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specification of requirements for (changes of) local knowledge system and also for knowledge structures to be communicated from other systems, development/evaluation of knowledge system and integration of knowledge communicated from other systems, according to specifications;
Cycle 4. -
-
-
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monitoring and evaluation of overall architecture of knowledge systems in organization, recording of requirements for knowledge communication and monitoring of their fulfillment, diagnosis of the degree of knowledge uncertainty.
The three-dimensional scheme for knowledge systems representatiorr offers a frame for such analysis. The analysis is cyclic and iterates within a cycle and between the cycles. At first, the analysis is going along the planes and then reaches the integrated view of the three-dimensional model of
Remarks
Knowledge systems will continue to evolve in different organization locations. Apart from being intended to fulfill local needs, knowledge systems could be valuable elsewhere in an organization. However, the uncertainty of knowledge assembled in a number of organizational locations prevents attainment of the required knowledge communication level. Knowledge uncertainty is an essential cause for poor knowledge communication between different work units. Knowledge uncertainty has many sources and is increasing with more widespread knowledge systems distribution. Knowledge uncertainty is not obvious. On the contrary, it is implicitly included in decision-making situations and is hidden by other uncertainties. A starting point in the analysis might be just to discover its existence and help to increase managers' awareness of its effects. We have described the frame in which knowledge systems can be modelled and examined. This frame allows exercising and experimenting with the problem domain knowledge and thus can support managers' learning. Depending on the managers' ambitions and abilities, the knowledge uncertainty can be decreased and organizational change toward higher levels of communication initiated.
References
[1] M. Aamodt, Forutsetningerfor a ta till onskede forandringer i en daglingsamarbeidssituasion,Proc. Nordic Conference on Management Research, Oslo (1977). [2] D.E. Brown and B.G. Duren, Conflicting Information Integration For Decision Support, Decision Support Systems 2 (1986) 321-329. [3] P. Checkland, Systems Thinking Systems Practice (Wiley, Chicester, 1981). [4] D. Ce~ez-Kecmanovig,Organization System Morphology Approach to KnowledgeRepresentation,Internal Publication LASA-86-1,Faculty for Electrical Engineering, University of Sarajevo, Sarajevo, 1986. [5] G.B. Davis, Strategies for Information Requirements Determination, IBM Systems Journal 21 (1982) 4-30.
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[6] T.D. Garvey, J.D. Lowrance and M.A. Fischler, An Inference Technique for Integrating Knowledge from Disparate Sources, Proc. International Joint Conference on Artificial Intelligence, Vancouver (1981). [7] P. Humphreys and D. Berkeley, Problem Structuring Calculi and Levels of Knowledge Representation in Decision Making, in: R.W. Scholz (ed.), Decision Making Under Uncertainty (North-Holland, Amsterdam, 1983). [8] P. Humphreys, Levels of Representation in Structuring Decision Problems, Journal of Applied System Analysis 11 (1984) 3-22. [9] E. Jacques, Free Enterprise Fair Employment (Heinemann, London, 1982).
[10] M. Lundeberg and A. Sorsveen, Clarifying the Needs for Improvement of Your Systems Development Function, R and D 10 (1978) 51-58. [11] J. Piaget, The Principles of Genetic Epistemology (Routledge and Kegan Paul, London, 1972). [12] H.E. Rauch, Probability Concepts for an Expert System Used for Data Fusion, Artificial Intelligence (1984) 53-60. [13] R.L. Winkler, Combining Probability Distribution from Dependent Information Sources, Management Science 27 (1981) 479-488.