- Email: [email protected]
An architecture for knowledge evolution in organisations1
European Journal of Operational Research 109 (1998) 414±427
An architecture for knowledge evolution in organisations FrancËoise Barthelme b
a,b
, Jean-Louis Ermine
b,*
, Camille Rosenthal-Sabroux
1 a
a LAMSADE, Universit e Paris-Dauphine, Place de Mar echal de Lattre de Tassigny, 75150 Paris, France Commissariat a l'Energie Atomique, Direction de l'Information Scienti®que et Technique, Centre d'Etudes de Saclay, 91191 Gif sur Yvette Cedex, France
Received 1 March 1997
Abstract Methodology for Knowledge System Management (MKSM) is a methodology, based on modelling techniques, to support knowledge capitalisation and management. This kind of approach faces a new challenge: knowledge evolution. Indeed, as knowledge of the organisation evolves, it seems necessary to have models and supporting tools to represent this evolution. Biologic evolution theories can oer the basis of a dynamic theory of Knowledge System evolution. We especially propose to adapt Lamarckism's principles and the Cladistic classi®cation to support such a modelling and propose a conceptual architecture for managing knowledge evolution. Ó 1998 Published by Elsevier Science B.V. All rights reserved. Keywords: Knowledge; Evolution; Evolution classi®cation; Knowledge management
«Qui sait les races d'animaux qui nous ont precedes? Qui sait les races d'animaux qui succederont aux n^ otres? Tout change, tout passe, il n'y a que le tout qui reste»
«Who knows the animal races which precede us? Who knows the animal races which will follow ours? Everything change, there is only the whole which remains»
Le r^eve de d'Alembert, Diderot, 1769 1. Knowledge management and knowledge system 1.1. The problem of knowledge management in organisations *
Corresponding author. Fax: 33 1 69 08 26 69; e-mail: [email protected]. 1 This research has been supported by the ``GIS Sciences de la Cognition''.
The problematic of knowledge management evolved strongly in about 15 years and changed several times of approach level. After the strictly computer level of simple information processing,
0377-2217/98/$19.00 Ó 1998 Published by Elsevier Science B.V. All rights reserved. PII S 0 3 7 7 - 2 2 1 7 ( 9 8 ) 0 0 0 6 7 - 8
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
the ®rst level is the one of automated knowledge processing, essentially oriented toward problems of software programming. The second level is the one of a structured approach of knowledge by cognitive, systemic (or other) modelling techniques, essentially for computer architectures design. This last level oriented the problematic towards a more in-depth understanding of knowledge in companies, without however clearing the strategic and organisational aspects of it. The question therefore takes another dimension. The problem is how to manage the knowledge in its totality, and not to solve some punctual problems. It is not to ®nd the adequate innovating technique(s), but to identify and to use all techniques, available or in development, useful to this objective. A new approach is initialised therefore, that boosts knowledge and information technologies. Corporate knowledge in companies is now considered as a heritage (``knowledge asset'') extremely competitive that needs to be managed in order to remain in the top organisations (``knowledge is power''). Knowledge management is therefore a problem whose acuteness equals its suddenness (it seems that our developed societies could not anticipate it!) in every organisation, big or small, public or private. The nuclear industry was one of the ®rst to experience this need of ``capitalisation''. The CEA (Commissariat a l'Energie Atomique, the French Atomic Energy Commission) is in charge of research and development in this domain and in many others (advanced technologies, life sciences, material science, etc.). It is one of the major ``producers of knowledge'' in France and as a result it is quite natural that it should be concerned by the management of its scienti®c and technical memory. In 1994, CEA decided that knowledge management was part of corporate strategy and 1995 they created a new operational division (the seventh division of CEA) aimed at scienti®c and technical information with the responsibility of helping all other units set up their own knowledge management systems. Several programs are at present under way to protect and preserve the very wide corpus of knowledge belonging to CEA. At present many units of CEA are setting up policy guide
415
lines for the management of their corpus of knowledge, especially preserving and capitalising their knowledge asset. As this is a new concept, CEA has developed a speci®c methodology to address that problem, Methodology for Knowledge System Management (MKSM) [1]. MKSM is a global analysis of the knowledge to be managed. This is performed by a succession of elicitation and modelling of knowledge, at dierent levels in order to make the Corporate Knowledge intelligible. The obtained models are prerequisites for the design of any knowledge management system. But, in every organisation (especially in a research centre), capitalising knowledge is not sucient. Knowledge is a dynamic creation in constant move, and any knowledge snapshot has to live and evolve to stay pertinent in the organisation. MKSM integrates the problem of knowledge evolution in its modelling process. But up to now, taking in account knowledge evolution has been reported, in order to focus on knowledge capitalisation. The problem is now mature enough, and urging in CEA, to consider a new framework, and add the evolution features to MKSM. This paper gives a sketch of that framework. 1.2. What is a knowledge system? The ®rst main diculty that appears in a knowledge management problem is to seek where this knowledge is. In parallel with the fact that the knowledge can be of dierent kinds (physical, psychological, cultural, social...), it seems that knowledge is everywhere. To visualise this fact, we can take the classical systemic model of an organisation. Every complex system can be described in terms of three dierent systems: the operating system, the decision system, and the information system. Knowledge is not a property of one of the subsystems, but hence the fact of the whole system. MKSM considers then a fourth subsystem, called the Knowledge System, we also call the Corporate Knowledge Repository (or Corporate Memory). We describe it as an active subsystem that exchanges ¯ows with the other subsystems. All the ¯ows that go towards the knowledge repository
416
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
are called the competence ¯ows, and all the ¯ows that come from the knowledge repository are called the cognition ¯ows [2]. Modelling the organisation in that way gives a representation of the system that transforms the inputs ¯ow into the outputs ¯ow and that contains the four subsystems (including the Corporate Memory) as described above (Fig. 1). We propose to illustrate the concept of Knowledge System by the modelling of an organisation (a university department) whose aim is to teach an Information System Course. We can discriminate the following features: Goal: To teach Information Systems theories. In-¯ows: Students registered in Information System Course. Out-¯ows: Students graduate in Information System theories. Some Agents: (de®ned in the model by their role, consumed and produced information, consumed and produced knowledge): University
managers, Educational program director, Library, Computers network, Teaching teams, Professors, Pedagogic tools... Operating system: Set of agents who perform the transformation of in-¯ows into out-¯ows. In this example, it is mainly the professors and pedagogic tools. Decision system: Set of agents which manage the production of the operating system. In this example persons responsible for the administrative and pedagogic choices. Information system: Set of agents, sources of information which memorise and put them at the organisation members' disposal so as to be used to the organisation's aim. In this example it deals with libraries, school books, educational programs and so on. It is important to notice that one organisation can be represented dierently according to the aim considered in the analysis. Indeed, a dierent goal will draw dierent borders for each system. For instance, if we consider the national
Fig. 1. A de®nition for knowledge system.
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
educational layout, the university in its totality can be considered as an operating system and national authorities as the decision agents. Fig. 2 displays some simple elements of each subsystem. 2. The MKSM macroscope 2.1. Microscope versus macroscope One may become dizzy facing the complexity of corporate knowledge in an organisation and the need to manage it. This complexity comes from numerous origins, either human or not, appearing during the achievement of some goals due to the complexity of their dependencies, to the multitude of the information sources taking place in the
417
process, to the knowledge taking place in the problem solving, to certain procedures, skill necessities, to the tacit or implicit knowledge needed in certain tasks implementation, and so on. It is then clear we are facing complex systems, in a widely spread meaning. In front of this complexity, it is inviting to reduce the problem into a simpler one, and to solve ``at least this one'', more especially as tools exist which may achieve the job pretty well. One decides to build a data base, to produce documents, to set training sessions, to build a knowledge-based system, and so on. All this leads generally to leave apart some knowledge which could unfortunately in turn become strategic. This recalls the metaphor of the optical microscope given by the anthropologist Claude Levi-Strauss saying we only have control over one partial point of
Fig. 2. The knowledge system related to an information system course.
418
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
view of a system ``as it is with an optical microscope. It is unable to reveal to the observer the ultimate structure of the material. The observer only sets dierent magnifying factors, each of these produces an organisation level of comparative availability, which excludes, as long as we use it, the perception of other levels''. The question is, of course: can we do something else? The answer is not new either, and can be expressed by another metaphor given by Joel de Rosnay: the macroscope metaphor that is precisely opposed to the microscope metaphor. The microscope idea (we could have spoken about telescope as well) is the one of an instrument which allows to go far beyond the human perception and to reveal organisations, that is structures that cannot be observed, on ®rst thoughts, with the help of classical means. This is then a powerful knowledge capturing instrument which widely proved its eciency. Its metaphoric power is then obvious but it hides another bias. For through the unknown world of the in®nitely small where it can open doors, it hides another ignorance. The ignorance of another dimension, as mysterious and unexplored, the dimension the systemicians such as J. de Rosnay call in®nitely complex. We need then to imagine a new tool, which enables the exploration and the discovery of the systems in a fertile and pertinent way in this new in®nitely complex dimension. A tool that may help us to have a global vision of the systems without any reduction. It is a deep change, because this tool is neither physical nor material, on the contrary: ``the macroscope is not a tool as the others. It is a symbolic instrument, made of methods and techniques inherited from various domains and put together. It does not help to see bigger or further but to observe what is altogether too big and too slow and too complex for human eyes'' [13]. MKSM intends to de®ne a macroscope to observe complex systems and to control them. And above all MKSM intends to state this macroscope in points of views which are relevant for what we work with: knowledge systems. In the next section we set the basic theoretical hypothesis for MKSM which allows to build a macroscope able to work with knowledge systems.
2.2. The dierent points of view of a knowledge system The semiotic hypothesis: The ®rst strong hypothesis is that the knowledge asset of an organisation is an ``object'', a ``phenomena'' perceived by anybody as a global set of elements that may be either virtual, real, conceptual, physical, etc. Even if they are not easy to distinguish and interpret, these elements make sense and give a signi®cance and an intrinsic coherence to the system, even though one does not know anything about and hence cannot give it a name at this very time. This is the semiotic hypothesis: These elements are named with the general term of signs and hence a knowledge system is perceived as a signs system. Let us just give the basis of the sign theory which is generally approved as a reasonable base. Each perceptible phenomena (sign) has to be observed within three levels considered as a whole: the sign (the evidence), the signi®ed (the designation), the signi®cant (the way it takes sense) that can be described as well in terms of three dimensions: syntactic, semantic and pragmatic. To split this conjunction of points of view is a nuisance, so that all system must be described as the conjunction of three points of view tightly linked together. These three points of view have been given a lot of names. We keep in mind, as a terminological convention, the three terms: syntactic, semantic and pragmatic which can be represented in a scheme we call the semiotic triangle scheme shown in Fig. 3. The systemic hypothesis: The second strong hypothesis is that the knowledge asset of an organisation is a system as described in the general system theory [11], put in France in the limelight by Jean-Louis Le Moigne's famous book on General System Theory [3,12]. Let us give the de®nition, ``trivial but easy to remember'', J.-L. Le Moigne wrote in his book in ch. 2: ``...taking a general de®nition of the word object, (a system is de®ned as) an object, at one and the same time: active, steady and moving in an environment for the sake of some goals''. This general system de®nition leads, as for the semiotic, to a triangle scheme as shown in Fig. 4. A system must be observed using three points of
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
419
Fig. 3. The three axis of the semiotic triangle scheme, the S3 conjunction.
view tightly linked together. Here again, there are various names to describe these points of view depending on the concept one wants to enhance. The ®rst point of view is named ontological. It considers the system in its structure, in a sense the system
is described as a set of objects settled together as ``one thing''. The second point of view is named phenomenological (or functional). It considers the system in its function, in a sense the system is perceived as acting, as ``doing things''. The third point
Fig. 4. The three axis of the systemic triangle scheme.
420
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
of view is named genetic. It considers the system in its evolution, in a sense the system is perceived as evolving according to its goal. Here again, by convention, we select the following terminology: structure, function and evolution. The perception, the analysis, the modelling of a system is then done with a weighted choice between these three points of view. The system analysis structure: The last hypothesis comes straight from the ®rst two. It produces the macroscope which enables to globally understand a knowledge asset in an organisation. For a system is perceived both as signs system and a general system, with three points of view each, the global perception we may have on the system is the product of these two kinds of approaches. This leads to have nine possible points of view: a syntactic, semantic and pragmatic point of view, each one having in turn three other points of view: structure, function, evolution. From now on, this nine-points-of-view set will be considered as our macroscope. It will help to approach, even to control the system complexity we will have to analyse and manage.
It is this nine-points-of-view system split up which stands as a basis. Now, we need to give a speci®c vision about the knowledge systems for this macroscope. Hence, the knowledge macroscope is a methodological tool which enables to de®ne and approach the knowledge by the syntactic, semantic and pragmatic component with three dierent and complementary viewpoints each. 2.3. The knowledge macroscope and the points of view of evolution According to the nine points of view we have seen in the above section, which de®nes a macroscope for the general complex system, we still have to make some hypothesis which leads to the interpretation and the adaptation of this macroscope to knowledge system analysis (see Fig. 5). The ®rst hypothesis is that the syntactic component of the knowledge concerns information. The term information is so widely used, that it is hard to precisely state it without stepping into a certain technicality. Let us say roughly that the informa-
Fig. 5. The knowledge macroscope.
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
tion concerns the obvious part, the form of the knowledge, as orthography or grammar concerns the obvious part of the language. Information is then the point of view that takes care of the form into which the knowledge is translated, the code it uses to take form. The structural aspects of information is given by Shannon's theory which gives a formal, quanti®ed de®nition of the code used by knowledge to appear as informational data (we will shorten that term in data, which has unfortunately a lot of meanings too!). The functional aspect of information deals with information processing (mixed with computer science) which is a very wide scienti®c domain. It describes how the data may be manipulated. Structural and functional aspects of information are perfectly dual. The second hypothesis is that the semantic component of the knowledge concerns the information signi®cation. It is, of course, dierent from its form, as in the language, the meaning of a sentence does not depend (or not only) on its syntax. Accumulating data about a knowledge is not sucient, it is necessary to join, in a way or another, the meaning of these data in the data themselves to achieve a somewhat pertinent whole. It is then this point of view that takes care of the substance regardless of the form of the knowledge, that takes care of the structure the knowledge uses to get meaning. The structural aspect de®nes the nature of meaning. Tough program which requires elements of linguistics, cognitive psychology, anthropology, because meaning is deeply rooted in human nature and culture. This aspect is given by semantic networks (semantic data versus informational data) which are built and stored, according to certain cognitive theories, in mental structures. The functional aspect deals with semantic data processing. The semantic structures do not exist by themselves, they are built to be used for a given goal, in a given action. This action is characterised by a problem to solve, an objective and is described by a strategy built by human mind to solve that problem. That is why the semantic data are used by cognitive tasks (we will say tasks) which are problem solving methods, which are, in turn, built and stored in mental structures. The third hypothesis is that the pragmatic component of the knowledge concerns the context in
421
which the meaning we have been talking about has to be put together. As we may easily guess this meaning strongly in¯uences this component. A knowledge exists not only because of its form or its signi®cation, but also because this form and this signi®cation take place in a speci®c context which gives its ¯avour and its relevance. This is then the point of view that takes care of the system, the environment that uses the knowledge to be put in context. The structural aspect of the context deals with the knowledge domain. To build a model of the domain, one must identify a set of fundamental processes allowing to build a pertinent ontology (or a glossary) of the domain. The functional aspect of the context is a functional analysis of the knowledge system. One must identify or de®ne the activities performed in the system, generally in terms of data ¯ows. A Knowledge System is then perceived, through our macroscope, as information which takes a given signi®cation in a given context. This knowledge macroscope is a fundamental tool to make knowledge system analysis and perform strategic knowledge management. It is a way to face the complexity of the problem of corporate knowledge, to get both a sharp and global view of what are the available components of knowledge, to spot patterns and gaps, to synthesise existing knowledge, to capture tacit knowledge in operational activities. This is the basis of MKSM. Up to now, the stress has been put on six points of view, the structural and the functional ones (in fact for the information point of view, it is classical, as data structures and data processing). Models have been built and widely used to address those points. The three remaining points of view related to evolution are still vacant. When (a part of) the knowledge is modelled and then capitalised, the maintenance problem appears: knowledge must evolve. A classical maintenance system is not sucient. Knowledge evolution seems to be a very complex process, which cannot be supported by elementary systems. There is a need to deeply understand how a Knowledge System evolves, in order to de®ne the architecture of a tool (conceptual, methodological, organisational or software) able to support and follow the evolution
422
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
of the capitalised knowledge. We propose here some directions to ®ll these gaps, and an architecture to manage those points of view. 3. The evolution theories 3.1. Darwinism against Lamarckism A necessary research direction for complex systems evolution is given by biological systems evolution. For those types of systems, evolution theory is a very wide area with an astounding amount of studies, still in debate between pros and cons of Darwin's theory [4,5]. Attempts have been already made to compare biological evolution and complex technical systems [6] (even in 1863 Samuel Butler wrote an essay entitled ``Darwin among the machines''). It seems natural to elaborate models of complex systems evolution based on those considerations. Several main ideas are to be considered. Nevertheless alternative theories exist, as the transformism of Lamarck. In this theory, the variations are not random, but directed, adaptative and intentional. It is clear for example that in a technological system (or a Knowledge System in an organisation) the evolution of a given functionality is not guided by chance, but by intelligence. We must notice that in the case of biological systems, the existence of an ``intelligence'' is a theological problem (this point has initiated the fabulous impact of Darwin's theory). In another way, there are hypotheses that evolution can occur by sudden jump (saltationnist theory). In short, two main models can be used to explain the evolution: the ®rst one is continuous, with gradual evolution, guided by chance and then by a (natural) selection; the second one is either continuous or discontinuous, but teleological, that is to say guided by an intelligence. 3.2. Lamarckism hypothesis for knowledge system evolution The works of Torres-Carbonell and Parets-Lorca [7] propose a model of software systems evolu-
tion. Software systems are analysed as complex systems with an evolutionist approach inspired by Lamarck's theory of evolution. As Parets-Llorca we draw a parallel between an open system evolution and biological evolution theories to model Knowledge System evolution. Indeed, as software system, it is possible to classify the Knowledge System as an open system in Von Bertalany's typology. Contrary to the natural selection proposed by Darwin, Lamarck assumes that traits genetically transmitted are issued from an adaptation process and that inheritance results from a goaloriented behaviour. For corporate Knowledge System, whose objectives are clearly prede®ned, it seems more suitable to use the Lamarckian concepts. This approach can be sustained by the nature of such a system: an arti®cial product which does not issue from a hazard process. We state here the fundamental hypothesis that we can derive from the Lamarckism concepts. Hypothesis 1. Knowledge systems evolve, that is to say they have the capability to integrate new solutions, that keep the characteristics of their ancestors and some new ones. Hypothesis 2. Evolution of a Knowledge System can be explained by new needs or constraints in its environment. Hypothesis 3. The new solutions are integrated as long as their objectives and goals exist. In the other case, they are integrated to the system history. Hypothesis 4. The adaptation of the solutions to their environment will be transmitted either wholly or in part to the next generations and then accumulated as far as this adaptation is adequate. This principle is known as the inheritance of acquired characteristics. Among the features of acquired characteristics, we propose to adopt three to characterise the Knowledge System evolution: · The acquired characteristic is a structure emerging from a new behavioural pattern of the organisation or in the organisation (a new move). · The acquired characteristic can be a direct and involuntary answer to the evolution of its own
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
environment (a new need). · As new characteristics appear, some parts of the system which are no more useful can be reduced or removed (dependence with use or non-use). The hypothesis of adaptation to the environment is justi®ed by the fact that the generations of dierent systems integrate all the adaptations performed during the life of the organisation. The hypothesis of inheritance of acquired characters and the hypothesis of oriented adaptation is justi®ed by the fact that a knowledge is kept in an organisation only if it answers a need or a goal. 3.3. Evolution modes To explain the evolution of arti®cial systems, Parets-Llorca suggests the use of three key concepts issued from Piaget and Simon works. We can then underline three ways of evolution. (1) The accommodation/learning process: In a favourable environment, an agent can adapt itself by learning to use the knowledge system structure in the best way. Therefore knowledge system evolves without performing structural changes. Thus, a Knowledge system, in a stable environment (which includes the stability of the organisation behaviour) is able to answer to the organisation's agents' needs better and better. (2) The assimilation: The cognition ¯ows produced by the Knowledge System modify its environment apprehension and thus they contribute to strengthen its function. (3) The mutation/dierentiation: It is a much more radical process than the previous ones. It occurs in a reluctant environment, from a deep
423
divergence between the system and the needs of the agents. It leads to a new structure, unknown until then by the system. The system structure must be modi®ed in this interaction. These kinds of modi®cations cannot be realised by the system alone and must be managed by another one: the Knowledge evolution system. This last system is the mutation's agent of the Knowledge System's evolution. Thus, an agent in an organisation (whatever the system it belongs to) can generate three evolving functions that we can link to three cognition ¯ows: · Mutation/dierentiation which is issued from the competence ¯ows. · Assimilation which is due to cognition ¯ows. · Accommodation which is the result of auto-referent cognition ¯ows. To sum up we can propose in Fig. 6 the model of agent interactions. 3.4. Evolution hypothesis for knowledge system evolution modelling in the MKSM macroscope All the above hypotheses give the basis of a model for representing the pragmatic evolution in a Knowledge System (one of the missing points of view in the MKSM macroscope). This gives the context in which the evolution takes place. The principles of the model can be stated as follows. · The model represents the objectives or goals that originated the new solutions. · The solutions are represented with links to the objectives. · The links between solutions are evolution links labelled by the concerned mode of evolution.
Fig. 6. The evolution modes.
424
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
Such models are in progress in dierent knowledge management projects, but the graphical items and conventions are not still stabilised (Fig. 7). 4. Modelling evolution with genetic classi®cations To ®ll another gap in the evolution points of view in the MKSM macroscope (the semantic point of view, after, as above, the contextual point of view), there is a natural candidate given by a classi®cation that addresses the problem of evolution: a genetic classi®cation, built exclusively by using evolution criteria. The problem is well known from a biological point of view, and has been largely developed in a domain called cladism. This idea has been deeply studied by historians to build the story of techniques or the evolution of technological objects (see [8]). We give here some hints to use those techniques to describe the evolution of knowledge, in terms of concept (or solution) classi®cation. In spite of theoretical dierences, biologists have elaborated classi®cations for a long time. The classi®cation concerns living systems or species. There exists two types of classi®cations. The ®rst one, called phenetic, de®nes and builds groups according to any chosen similarity criteria. The second one, known as phylogenetic classi®cation,
Fig. 7. The pragmatic evolution point of view.
is based on evolutive relationships, which constitute a basis for evolution theory. This last type of classi®cation is the subject of a scienti®c discipline: Cladism. If phenetic classi®cation is built from direct observation, phylogenetic classi®cation is a reconstruction of evolution relationships. We propose to use the taxonomies elaborated by evolutionists, phylogenetic classi®cations or cladograms, for modelling system's evolution. We consider now, the possibility of classifying the knowledge evolution in the knowledge system not according to similarities of criteria but according to criteria of evolution. The cladistic approach gives us the possibility to gather species (for us technological concepts) according to their evolution. As technological concepts are subject to an evolution, the parallel between knowledge classi®cation and species classi®cation based on evolution criteria seems applicable. Cladistic method known also as the Phylogenetic systematics was born in 1950 proposed by Hennig. The phylogeny consists on founding a degree of relationship (of kinship) between species (or group species) not according to their resemblance but according to the evolution of common characteristics. Thus two species A and B are more similar than another species C if a common A and B ancestor bears the common characteristic and is less old than the common ancestor of the three species. A representation of such relations is a cladogram or cladistic tree. Biologists distinguish three types of similarity: (1) The super®cial similarity which stems from a convergent phenomena of the evolution. For instance the bird wings and the bat wings come both from the functionality ``¯ying'' with dierent modalities. This ®rst type, not natural, is polyphyletic because the convergent character is not inherited from a common ancestor. This kind of relation is homological, we have indeed two dierent structures (the wings) which perform the same functionality. It is possible to draw a parallel because concepts can evolve and develop dierent structures while their functions remain. (2) The similarity which stems from existence of the same characters in all the evolved species. For instance the carnassial for cats and dogs. This group is called monophyletic because the carnassial character is inherited from the common
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
ancestor of the two groups and this character is exclusive in this relationship. Thus two technological concept holders of a speci®c characteristic which does not exist in other concepts are the only heir of the same initial concepts. (3) The similarity which stems from the common character which is unchanged in relation to the initial ancestor's. This group is paraphyletic (the tortoise, the salamender, the human ®ve ®ngers rear limb). This character is inherited from an ancestor who does not belong to the group. Other animals have this character out of the group. The tortoise and human common ancestor is also the horse ancestor. The horse has only one ®nger, and the human is more similar to the horse than the tortoise, indeed both are mammals. Thus some characteristics exist in concepts throughout their evolution, identically to the characteristic of the ®rst generation. The classi®cation proposed by Hennig is based on the three above types of similarity in terms of characteristics. We propose to use the taxonomies elaborated by evolutionnists, phylogenetic classi®cations or cladograms, for modelling system's evolution. Thus, the idea is to classify knowledge regarding their common evolved characteristics in a structure which could constitute a track of the Knowledge System evolution. Moreover, the cladistic trees seem to be shorter than the trees resulting from other types of classi®cation. One of the main interests in this theory is that the introduction of a new species (for instance in our point of view a new technological concept) can lead to a reconsideration of the whole previous classi®cation. This analysis could improve the present model of evolutionary technologies' trees which allows to represent evolution of technological concepts regarding the historic de®nition of technological objects, presently used in the CEA. An evolutionary technologies' tree links a general concept (a generic species) and the technological concepts which stems from it by the ®xation of speci®c objectives. The analysis of each technological concept in terms of disadvantages and advantages gives the opportunity of a further development of this technology. Then new objectives can be assigned and other technologies developed. Those models can
425
be also used to improve the Design Rationale approach [9]. 5. An architecture for evolution management Knowledge System evolution can be partly modelled by the new points of view in the MKSM macroscope: the context de®ned by history of mutations and accomodations, the semantic representation given by genetic classi®cations (evolution trees). Those models complete the others available in MKSM. A fundamental problem is then to observe the Knowledge System in an organisation, in order to control its evolution. In arti®cial systems, evolution is not random and is partly the consequence of some decisions, especially the decision to incorporate new knowledge in the corporate repository. We discuss here a conceptual architecture to perform that kind of evolution management, and, in particular, a system design able to give some decision support in the organisation to make it evolve in a coherent way. If we suppose now that dierent points of view are able to insure evolution of knowledge in the organisation, some of them will have an eect on the own structure of the Knowledge System. ParetsLlorca et al. works proposed to manage this evolution thanks to a ``metasystem'' which is able to analyse and perform the changes [7]. Following Parets-Llorca and the previous works of Simon and Piaget we propose the existence of a ®fth system: the Knowledge Evolution System which is in charge to manage the evolution. Three elements can be pointed in such a system: an observing system, an analysing and deciding system and a performing and capitalising system. (1) The Observing System (OS) observes and analyses the necessary adaptations which do not imply genetic changes. The accommodation process is controlled by this system. (2) The Decision System (DS) takes the decisions concerning the acquired characteristics which are transmitted by inheritance and thus is able to modify the knowledge structure. Through this system it is possible, in co-ordination with the agents of the organisation to decide what
426
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
new pieces of knowledge can be introduced or on the contrary what technological concepts are no more relevant. (3) The Capitalisation System (CS) performs the evolution transformations. It is implied in the mutation as in the adaptation process. Such a structure allows us to represent the dierent ways of evolution and gives some tools able to maintain a Knowledge System. But this model which is relevant to explain how the Knowledge System evolves is still insucient to describe the evolution itself. We propose to draw the tracks of this evolution by using a phylogenetic classi®cation of the knowledge. The integration of the above theories suggests us to propose the model of Knowledge System evolution described in Fig. 8. Our analysis suggests that three ¯ows are the basis of knowledge evolution process: The ®rst
one appears in the OID system between the dierent agents linked by formal or informal networks. This interaction is the source of the knowledge production (1). The second and the third ¯ows exist between the Evolution System and · the agents of decision system for decision support (2), · the knowledge system to perform evolutionary actions and create evolution trees (3).
6. Conclusion This work is an ongoing research. Once the Knowledge System is analysed in an organisation, we consider its evolution.
Fig. 8. The evolution management system (DSS: Decision Support System, OS: Observing System, CS: Capitalisation System).
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
The MKSM methodology used to analyse knowledge system, incorporates ``naturally'' three evolution points of view answering the following questions: in what context the knowledge has evolved (or evolves), what makes sense in the evolution of the knowledge (the concepts or the solutions), and what information is linked to the evolution? However, up to now, MKSM gives no models to answer these questions. We have addressed the ®rst two questions, and given hints to build models for the two corresponding points of view. We choose to describe this evolution with Lamarckian concepts, indeed evolution of such an arti®cial system is guided by intelligence instead of chance. Those concepts give a basis to trace the history of concepts, with annotation referring to evolution. It is a good candidate to describe the context in which the considered knowledge has evolved. We choose to model the evolution memory by using evolution trees suggested by the Cladism. Phylogenetic theory traces the evolution tree with rigorous criteria which ®t with our technological concepts. It is a good candidate to describe, as in design rationale, the argumentation and the classi®cation of concepts (or solutions) through time, giving a ``cognitive meaning'' to evolution. The last problem addressed in that paper, is the dynamic evolution of knowledge. There is a need to observe and take decisions about the new knowledge constantly created in organisations. The architecture for Knowledge System evolution proposed in this article complements the MKSM method [10]. Introducing the existence of an evolution system oers a way to control and memorise the evolution of concepts. Indeed, if the evolution system is able to modify the evolution trees, it is possible for the human agents to interact with this system. The decision support system features, such as the de®nition of knowledge evolution indicators
427
will be developed further. Those indicators will be tested on case studies in the CEA.
Acknowledgements We deeply thank Pr K. Schmidt, from the RISO Institute in Denmark, for all his stimulating discussions and remarks.
References [1] J.-L. Ermine, Les systemes de connaissances, Edition Hermes, Paris, 1996. [2] E. Morin, Introduction a la pensee complexe, Communication et complexite, ESF ed., Paris, 1990. [3] J.-L. Le Moigne, La modelisation des systemes complexes, Afcet Systemes, Dunod, Paris, 1990. [4] M. Denton, Evolution: A Theory in Crisis, Burnett Books, Londreys, 1988 (trad. Fr.: Evolution, une theorie en crise, Flammarion, 1992, Paris). [5] M. Ridley, The Problems of Evolution, Oxford University Press, Oxford, 1985 (trad. Fr.: L'evolution, Pour la Science, Diusion, Belin, 1989). [6] T.H. Jukes, Aircraft evolution, Nature 295 (1982) 548. [7] J.J. Torres-Carbonell, J. Parets-Llorca, Biological evolutive models applied to the evolution of software systems, Congres Europeen de Systemique, Roma, 1996. [8] Y. Deforge, Technologie et genetique de l'objet industriel, 1981. [9] Design Rationale Concepts, Techniques and Use, Erlbaum, London, 1996. [10] E. Brunet, J.-L. Ermine, Problematique de la gestion des connaiss ances des organisations, Ingenierie des systemes d'information, vol.2, no. 3, AFCET/Hermes, 1994, pp. 263±291. [11] L. von Bertalany, General System Theory, Braziller, New-York, 1968 (trad. J.B. Chabrol, Theorie Generale des Systemes, Dunod, Paris, 1973). [12] J.-L. Le Moigne, La theorie du systeme general, P.U.F., 1977, 2nd ed., Complementee, 1984. [13] J. de Rosnay, Le macroscope vers une vision globale, Seuil, Paris, 1975.
An architecture for knowledge evolution in organisations FrancËoise Barthelme b
a,b
, Jean-Louis Ermine
b,*
, Camille Rosenthal-Sabroux
1 a
a LAMSADE, Universit e Paris-Dauphine, Place de Mar echal de Lattre de Tassigny, 75150 Paris, France Commissariat a l'Energie Atomique, Direction de l'Information Scienti®que et Technique, Centre d'Etudes de Saclay, 91191 Gif sur Yvette Cedex, France
Received 1 March 1997
Abstract Methodology for Knowledge System Management (MKSM) is a methodology, based on modelling techniques, to support knowledge capitalisation and management. This kind of approach faces a new challenge: knowledge evolution. Indeed, as knowledge of the organisation evolves, it seems necessary to have models and supporting tools to represent this evolution. Biologic evolution theories can oer the basis of a dynamic theory of Knowledge System evolution. We especially propose to adapt Lamarckism's principles and the Cladistic classi®cation to support such a modelling and propose a conceptual architecture for managing knowledge evolution. Ó 1998 Published by Elsevier Science B.V. All rights reserved. Keywords: Knowledge; Evolution; Evolution classi®cation; Knowledge management
«Qui sait les races d'animaux qui nous ont precedes? Qui sait les races d'animaux qui succederont aux n^ otres? Tout change, tout passe, il n'y a que le tout qui reste»
«Who knows the animal races which precede us? Who knows the animal races which will follow ours? Everything change, there is only the whole which remains»
Le r^eve de d'Alembert, Diderot, 1769 1. Knowledge management and knowledge system 1.1. The problem of knowledge management in organisations *
Corresponding author. Fax: 33 1 69 08 26 69; e-mail: [email protected]. 1 This research has been supported by the ``GIS Sciences de la Cognition''.
The problematic of knowledge management evolved strongly in about 15 years and changed several times of approach level. After the strictly computer level of simple information processing,
0377-2217/98/$19.00 Ó 1998 Published by Elsevier Science B.V. All rights reserved. PII S 0 3 7 7 - 2 2 1 7 ( 9 8 ) 0 0 0 6 7 - 8
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
the ®rst level is the one of automated knowledge processing, essentially oriented toward problems of software programming. The second level is the one of a structured approach of knowledge by cognitive, systemic (or other) modelling techniques, essentially for computer architectures design. This last level oriented the problematic towards a more in-depth understanding of knowledge in companies, without however clearing the strategic and organisational aspects of it. The question therefore takes another dimension. The problem is how to manage the knowledge in its totality, and not to solve some punctual problems. It is not to ®nd the adequate innovating technique(s), but to identify and to use all techniques, available or in development, useful to this objective. A new approach is initialised therefore, that boosts knowledge and information technologies. Corporate knowledge in companies is now considered as a heritage (``knowledge asset'') extremely competitive that needs to be managed in order to remain in the top organisations (``knowledge is power''). Knowledge management is therefore a problem whose acuteness equals its suddenness (it seems that our developed societies could not anticipate it!) in every organisation, big or small, public or private. The nuclear industry was one of the ®rst to experience this need of ``capitalisation''. The CEA (Commissariat a l'Energie Atomique, the French Atomic Energy Commission) is in charge of research and development in this domain and in many others (advanced technologies, life sciences, material science, etc.). It is one of the major ``producers of knowledge'' in France and as a result it is quite natural that it should be concerned by the management of its scienti®c and technical memory. In 1994, CEA decided that knowledge management was part of corporate strategy and 1995 they created a new operational division (the seventh division of CEA) aimed at scienti®c and technical information with the responsibility of helping all other units set up their own knowledge management systems. Several programs are at present under way to protect and preserve the very wide corpus of knowledge belonging to CEA. At present many units of CEA are setting up policy guide
415
lines for the management of their corpus of knowledge, especially preserving and capitalising their knowledge asset. As this is a new concept, CEA has developed a speci®c methodology to address that problem, Methodology for Knowledge System Management (MKSM) [1]. MKSM is a global analysis of the knowledge to be managed. This is performed by a succession of elicitation and modelling of knowledge, at dierent levels in order to make the Corporate Knowledge intelligible. The obtained models are prerequisites for the design of any knowledge management system. But, in every organisation (especially in a research centre), capitalising knowledge is not sucient. Knowledge is a dynamic creation in constant move, and any knowledge snapshot has to live and evolve to stay pertinent in the organisation. MKSM integrates the problem of knowledge evolution in its modelling process. But up to now, taking in account knowledge evolution has been reported, in order to focus on knowledge capitalisation. The problem is now mature enough, and urging in CEA, to consider a new framework, and add the evolution features to MKSM. This paper gives a sketch of that framework. 1.2. What is a knowledge system? The ®rst main diculty that appears in a knowledge management problem is to seek where this knowledge is. In parallel with the fact that the knowledge can be of dierent kinds (physical, psychological, cultural, social...), it seems that knowledge is everywhere. To visualise this fact, we can take the classical systemic model of an organisation. Every complex system can be described in terms of three dierent systems: the operating system, the decision system, and the information system. Knowledge is not a property of one of the subsystems, but hence the fact of the whole system. MKSM considers then a fourth subsystem, called the Knowledge System, we also call the Corporate Knowledge Repository (or Corporate Memory). We describe it as an active subsystem that exchanges ¯ows with the other subsystems. All the ¯ows that go towards the knowledge repository
416
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
are called the competence ¯ows, and all the ¯ows that come from the knowledge repository are called the cognition ¯ows [2]. Modelling the organisation in that way gives a representation of the system that transforms the inputs ¯ow into the outputs ¯ow and that contains the four subsystems (including the Corporate Memory) as described above (Fig. 1). We propose to illustrate the concept of Knowledge System by the modelling of an organisation (a university department) whose aim is to teach an Information System Course. We can discriminate the following features: Goal: To teach Information Systems theories. In-¯ows: Students registered in Information System Course. Out-¯ows: Students graduate in Information System theories. Some Agents: (de®ned in the model by their role, consumed and produced information, consumed and produced knowledge): University
managers, Educational program director, Library, Computers network, Teaching teams, Professors, Pedagogic tools... Operating system: Set of agents who perform the transformation of in-¯ows into out-¯ows. In this example, it is mainly the professors and pedagogic tools. Decision system: Set of agents which manage the production of the operating system. In this example persons responsible for the administrative and pedagogic choices. Information system: Set of agents, sources of information which memorise and put them at the organisation members' disposal so as to be used to the organisation's aim. In this example it deals with libraries, school books, educational programs and so on. It is important to notice that one organisation can be represented dierently according to the aim considered in the analysis. Indeed, a dierent goal will draw dierent borders for each system. For instance, if we consider the national
Fig. 1. A de®nition for knowledge system.
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
educational layout, the university in its totality can be considered as an operating system and national authorities as the decision agents. Fig. 2 displays some simple elements of each subsystem. 2. The MKSM macroscope 2.1. Microscope versus macroscope One may become dizzy facing the complexity of corporate knowledge in an organisation and the need to manage it. This complexity comes from numerous origins, either human or not, appearing during the achievement of some goals due to the complexity of their dependencies, to the multitude of the information sources taking place in the
417
process, to the knowledge taking place in the problem solving, to certain procedures, skill necessities, to the tacit or implicit knowledge needed in certain tasks implementation, and so on. It is then clear we are facing complex systems, in a widely spread meaning. In front of this complexity, it is inviting to reduce the problem into a simpler one, and to solve ``at least this one'', more especially as tools exist which may achieve the job pretty well. One decides to build a data base, to produce documents, to set training sessions, to build a knowledge-based system, and so on. All this leads generally to leave apart some knowledge which could unfortunately in turn become strategic. This recalls the metaphor of the optical microscope given by the anthropologist Claude Levi-Strauss saying we only have control over one partial point of
Fig. 2. The knowledge system related to an information system course.
418
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
view of a system ``as it is with an optical microscope. It is unable to reveal to the observer the ultimate structure of the material. The observer only sets dierent magnifying factors, each of these produces an organisation level of comparative availability, which excludes, as long as we use it, the perception of other levels''. The question is, of course: can we do something else? The answer is not new either, and can be expressed by another metaphor given by Joel de Rosnay: the macroscope metaphor that is precisely opposed to the microscope metaphor. The microscope idea (we could have spoken about telescope as well) is the one of an instrument which allows to go far beyond the human perception and to reveal organisations, that is structures that cannot be observed, on ®rst thoughts, with the help of classical means. This is then a powerful knowledge capturing instrument which widely proved its eciency. Its metaphoric power is then obvious but it hides another bias. For through the unknown world of the in®nitely small where it can open doors, it hides another ignorance. The ignorance of another dimension, as mysterious and unexplored, the dimension the systemicians such as J. de Rosnay call in®nitely complex. We need then to imagine a new tool, which enables the exploration and the discovery of the systems in a fertile and pertinent way in this new in®nitely complex dimension. A tool that may help us to have a global vision of the systems without any reduction. It is a deep change, because this tool is neither physical nor material, on the contrary: ``the macroscope is not a tool as the others. It is a symbolic instrument, made of methods and techniques inherited from various domains and put together. It does not help to see bigger or further but to observe what is altogether too big and too slow and too complex for human eyes'' [13]. MKSM intends to de®ne a macroscope to observe complex systems and to control them. And above all MKSM intends to state this macroscope in points of views which are relevant for what we work with: knowledge systems. In the next section we set the basic theoretical hypothesis for MKSM which allows to build a macroscope able to work with knowledge systems.
2.2. The dierent points of view of a knowledge system The semiotic hypothesis: The ®rst strong hypothesis is that the knowledge asset of an organisation is an ``object'', a ``phenomena'' perceived by anybody as a global set of elements that may be either virtual, real, conceptual, physical, etc. Even if they are not easy to distinguish and interpret, these elements make sense and give a signi®cance and an intrinsic coherence to the system, even though one does not know anything about and hence cannot give it a name at this very time. This is the semiotic hypothesis: These elements are named with the general term of signs and hence a knowledge system is perceived as a signs system. Let us just give the basis of the sign theory which is generally approved as a reasonable base. Each perceptible phenomena (sign) has to be observed within three levels considered as a whole: the sign (the evidence), the signi®ed (the designation), the signi®cant (the way it takes sense) that can be described as well in terms of three dimensions: syntactic, semantic and pragmatic. To split this conjunction of points of view is a nuisance, so that all system must be described as the conjunction of three points of view tightly linked together. These three points of view have been given a lot of names. We keep in mind, as a terminological convention, the three terms: syntactic, semantic and pragmatic which can be represented in a scheme we call the semiotic triangle scheme shown in Fig. 3. The systemic hypothesis: The second strong hypothesis is that the knowledge asset of an organisation is a system as described in the general system theory [11], put in France in the limelight by Jean-Louis Le Moigne's famous book on General System Theory [3,12]. Let us give the de®nition, ``trivial but easy to remember'', J.-L. Le Moigne wrote in his book in ch. 2: ``...taking a general de®nition of the word object, (a system is de®ned as) an object, at one and the same time: active, steady and moving in an environment for the sake of some goals''. This general system de®nition leads, as for the semiotic, to a triangle scheme as shown in Fig. 4. A system must be observed using three points of
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
419
Fig. 3. The three axis of the semiotic triangle scheme, the S3 conjunction.
view tightly linked together. Here again, there are various names to describe these points of view depending on the concept one wants to enhance. The ®rst point of view is named ontological. It considers the system in its structure, in a sense the system
is described as a set of objects settled together as ``one thing''. The second point of view is named phenomenological (or functional). It considers the system in its function, in a sense the system is perceived as acting, as ``doing things''. The third point
Fig. 4. The three axis of the systemic triangle scheme.
420
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
of view is named genetic. It considers the system in its evolution, in a sense the system is perceived as evolving according to its goal. Here again, by convention, we select the following terminology: structure, function and evolution. The perception, the analysis, the modelling of a system is then done with a weighted choice between these three points of view. The system analysis structure: The last hypothesis comes straight from the ®rst two. It produces the macroscope which enables to globally understand a knowledge asset in an organisation. For a system is perceived both as signs system and a general system, with three points of view each, the global perception we may have on the system is the product of these two kinds of approaches. This leads to have nine possible points of view: a syntactic, semantic and pragmatic point of view, each one having in turn three other points of view: structure, function, evolution. From now on, this nine-points-of-view set will be considered as our macroscope. It will help to approach, even to control the system complexity we will have to analyse and manage.
It is this nine-points-of-view system split up which stands as a basis. Now, we need to give a speci®c vision about the knowledge systems for this macroscope. Hence, the knowledge macroscope is a methodological tool which enables to de®ne and approach the knowledge by the syntactic, semantic and pragmatic component with three dierent and complementary viewpoints each. 2.3. The knowledge macroscope and the points of view of evolution According to the nine points of view we have seen in the above section, which de®nes a macroscope for the general complex system, we still have to make some hypothesis which leads to the interpretation and the adaptation of this macroscope to knowledge system analysis (see Fig. 5). The ®rst hypothesis is that the syntactic component of the knowledge concerns information. The term information is so widely used, that it is hard to precisely state it without stepping into a certain technicality. Let us say roughly that the informa-
Fig. 5. The knowledge macroscope.
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
tion concerns the obvious part, the form of the knowledge, as orthography or grammar concerns the obvious part of the language. Information is then the point of view that takes care of the form into which the knowledge is translated, the code it uses to take form. The structural aspects of information is given by Shannon's theory which gives a formal, quanti®ed de®nition of the code used by knowledge to appear as informational data (we will shorten that term in data, which has unfortunately a lot of meanings too!). The functional aspect of information deals with information processing (mixed with computer science) which is a very wide scienti®c domain. It describes how the data may be manipulated. Structural and functional aspects of information are perfectly dual. The second hypothesis is that the semantic component of the knowledge concerns the information signi®cation. It is, of course, dierent from its form, as in the language, the meaning of a sentence does not depend (or not only) on its syntax. Accumulating data about a knowledge is not sucient, it is necessary to join, in a way or another, the meaning of these data in the data themselves to achieve a somewhat pertinent whole. It is then this point of view that takes care of the substance regardless of the form of the knowledge, that takes care of the structure the knowledge uses to get meaning. The structural aspect de®nes the nature of meaning. Tough program which requires elements of linguistics, cognitive psychology, anthropology, because meaning is deeply rooted in human nature and culture. This aspect is given by semantic networks (semantic data versus informational data) which are built and stored, according to certain cognitive theories, in mental structures. The functional aspect deals with semantic data processing. The semantic structures do not exist by themselves, they are built to be used for a given goal, in a given action. This action is characterised by a problem to solve, an objective and is described by a strategy built by human mind to solve that problem. That is why the semantic data are used by cognitive tasks (we will say tasks) which are problem solving methods, which are, in turn, built and stored in mental structures. The third hypothesis is that the pragmatic component of the knowledge concerns the context in
421
which the meaning we have been talking about has to be put together. As we may easily guess this meaning strongly in¯uences this component. A knowledge exists not only because of its form or its signi®cation, but also because this form and this signi®cation take place in a speci®c context which gives its ¯avour and its relevance. This is then the point of view that takes care of the system, the environment that uses the knowledge to be put in context. The structural aspect of the context deals with the knowledge domain. To build a model of the domain, one must identify a set of fundamental processes allowing to build a pertinent ontology (or a glossary) of the domain. The functional aspect of the context is a functional analysis of the knowledge system. One must identify or de®ne the activities performed in the system, generally in terms of data ¯ows. A Knowledge System is then perceived, through our macroscope, as information which takes a given signi®cation in a given context. This knowledge macroscope is a fundamental tool to make knowledge system analysis and perform strategic knowledge management. It is a way to face the complexity of the problem of corporate knowledge, to get both a sharp and global view of what are the available components of knowledge, to spot patterns and gaps, to synthesise existing knowledge, to capture tacit knowledge in operational activities. This is the basis of MKSM. Up to now, the stress has been put on six points of view, the structural and the functional ones (in fact for the information point of view, it is classical, as data structures and data processing). Models have been built and widely used to address those points. The three remaining points of view related to evolution are still vacant. When (a part of) the knowledge is modelled and then capitalised, the maintenance problem appears: knowledge must evolve. A classical maintenance system is not sucient. Knowledge evolution seems to be a very complex process, which cannot be supported by elementary systems. There is a need to deeply understand how a Knowledge System evolves, in order to de®ne the architecture of a tool (conceptual, methodological, organisational or software) able to support and follow the evolution
422
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
of the capitalised knowledge. We propose here some directions to ®ll these gaps, and an architecture to manage those points of view. 3. The evolution theories 3.1. Darwinism against Lamarckism A necessary research direction for complex systems evolution is given by biological systems evolution. For those types of systems, evolution theory is a very wide area with an astounding amount of studies, still in debate between pros and cons of Darwin's theory [4,5]. Attempts have been already made to compare biological evolution and complex technical systems [6] (even in 1863 Samuel Butler wrote an essay entitled ``Darwin among the machines''). It seems natural to elaborate models of complex systems evolution based on those considerations. Several main ideas are to be considered. Nevertheless alternative theories exist, as the transformism of Lamarck. In this theory, the variations are not random, but directed, adaptative and intentional. It is clear for example that in a technological system (or a Knowledge System in an organisation) the evolution of a given functionality is not guided by chance, but by intelligence. We must notice that in the case of biological systems, the existence of an ``intelligence'' is a theological problem (this point has initiated the fabulous impact of Darwin's theory). In another way, there are hypotheses that evolution can occur by sudden jump (saltationnist theory). In short, two main models can be used to explain the evolution: the ®rst one is continuous, with gradual evolution, guided by chance and then by a (natural) selection; the second one is either continuous or discontinuous, but teleological, that is to say guided by an intelligence. 3.2. Lamarckism hypothesis for knowledge system evolution The works of Torres-Carbonell and Parets-Lorca [7] propose a model of software systems evolu-
tion. Software systems are analysed as complex systems with an evolutionist approach inspired by Lamarck's theory of evolution. As Parets-Llorca we draw a parallel between an open system evolution and biological evolution theories to model Knowledge System evolution. Indeed, as software system, it is possible to classify the Knowledge System as an open system in Von Bertalany's typology. Contrary to the natural selection proposed by Darwin, Lamarck assumes that traits genetically transmitted are issued from an adaptation process and that inheritance results from a goaloriented behaviour. For corporate Knowledge System, whose objectives are clearly prede®ned, it seems more suitable to use the Lamarckian concepts. This approach can be sustained by the nature of such a system: an arti®cial product which does not issue from a hazard process. We state here the fundamental hypothesis that we can derive from the Lamarckism concepts. Hypothesis 1. Knowledge systems evolve, that is to say they have the capability to integrate new solutions, that keep the characteristics of their ancestors and some new ones. Hypothesis 2. Evolution of a Knowledge System can be explained by new needs or constraints in its environment. Hypothesis 3. The new solutions are integrated as long as their objectives and goals exist. In the other case, they are integrated to the system history. Hypothesis 4. The adaptation of the solutions to their environment will be transmitted either wholly or in part to the next generations and then accumulated as far as this adaptation is adequate. This principle is known as the inheritance of acquired characteristics. Among the features of acquired characteristics, we propose to adopt three to characterise the Knowledge System evolution: · The acquired characteristic is a structure emerging from a new behavioural pattern of the organisation or in the organisation (a new move). · The acquired characteristic can be a direct and involuntary answer to the evolution of its own
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
environment (a new need). · As new characteristics appear, some parts of the system which are no more useful can be reduced or removed (dependence with use or non-use). The hypothesis of adaptation to the environment is justi®ed by the fact that the generations of dierent systems integrate all the adaptations performed during the life of the organisation. The hypothesis of inheritance of acquired characters and the hypothesis of oriented adaptation is justi®ed by the fact that a knowledge is kept in an organisation only if it answers a need or a goal. 3.3. Evolution modes To explain the evolution of arti®cial systems, Parets-Llorca suggests the use of three key concepts issued from Piaget and Simon works. We can then underline three ways of evolution. (1) The accommodation/learning process: In a favourable environment, an agent can adapt itself by learning to use the knowledge system structure in the best way. Therefore knowledge system evolves without performing structural changes. Thus, a Knowledge system, in a stable environment (which includes the stability of the organisation behaviour) is able to answer to the organisation's agents' needs better and better. (2) The assimilation: The cognition ¯ows produced by the Knowledge System modify its environment apprehension and thus they contribute to strengthen its function. (3) The mutation/dierentiation: It is a much more radical process than the previous ones. It occurs in a reluctant environment, from a deep
423
divergence between the system and the needs of the agents. It leads to a new structure, unknown until then by the system. The system structure must be modi®ed in this interaction. These kinds of modi®cations cannot be realised by the system alone and must be managed by another one: the Knowledge evolution system. This last system is the mutation's agent of the Knowledge System's evolution. Thus, an agent in an organisation (whatever the system it belongs to) can generate three evolving functions that we can link to three cognition ¯ows: · Mutation/dierentiation which is issued from the competence ¯ows. · Assimilation which is due to cognition ¯ows. · Accommodation which is the result of auto-referent cognition ¯ows. To sum up we can propose in Fig. 6 the model of agent interactions. 3.4. Evolution hypothesis for knowledge system evolution modelling in the MKSM macroscope All the above hypotheses give the basis of a model for representing the pragmatic evolution in a Knowledge System (one of the missing points of view in the MKSM macroscope). This gives the context in which the evolution takes place. The principles of the model can be stated as follows. · The model represents the objectives or goals that originated the new solutions. · The solutions are represented with links to the objectives. · The links between solutions are evolution links labelled by the concerned mode of evolution.
Fig. 6. The evolution modes.
424
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
Such models are in progress in dierent knowledge management projects, but the graphical items and conventions are not still stabilised (Fig. 7). 4. Modelling evolution with genetic classi®cations To ®ll another gap in the evolution points of view in the MKSM macroscope (the semantic point of view, after, as above, the contextual point of view), there is a natural candidate given by a classi®cation that addresses the problem of evolution: a genetic classi®cation, built exclusively by using evolution criteria. The problem is well known from a biological point of view, and has been largely developed in a domain called cladism. This idea has been deeply studied by historians to build the story of techniques or the evolution of technological objects (see [8]). We give here some hints to use those techniques to describe the evolution of knowledge, in terms of concept (or solution) classi®cation. In spite of theoretical dierences, biologists have elaborated classi®cations for a long time. The classi®cation concerns living systems or species. There exists two types of classi®cations. The ®rst one, called phenetic, de®nes and builds groups according to any chosen similarity criteria. The second one, known as phylogenetic classi®cation,
Fig. 7. The pragmatic evolution point of view.
is based on evolutive relationships, which constitute a basis for evolution theory. This last type of classi®cation is the subject of a scienti®c discipline: Cladism. If phenetic classi®cation is built from direct observation, phylogenetic classi®cation is a reconstruction of evolution relationships. We propose to use the taxonomies elaborated by evolutionists, phylogenetic classi®cations or cladograms, for modelling system's evolution. We consider now, the possibility of classifying the knowledge evolution in the knowledge system not according to similarities of criteria but according to criteria of evolution. The cladistic approach gives us the possibility to gather species (for us technological concepts) according to their evolution. As technological concepts are subject to an evolution, the parallel between knowledge classi®cation and species classi®cation based on evolution criteria seems applicable. Cladistic method known also as the Phylogenetic systematics was born in 1950 proposed by Hennig. The phylogeny consists on founding a degree of relationship (of kinship) between species (or group species) not according to their resemblance but according to the evolution of common characteristics. Thus two species A and B are more similar than another species C if a common A and B ancestor bears the common characteristic and is less old than the common ancestor of the three species. A representation of such relations is a cladogram or cladistic tree. Biologists distinguish three types of similarity: (1) The super®cial similarity which stems from a convergent phenomena of the evolution. For instance the bird wings and the bat wings come both from the functionality ``¯ying'' with dierent modalities. This ®rst type, not natural, is polyphyletic because the convergent character is not inherited from a common ancestor. This kind of relation is homological, we have indeed two dierent structures (the wings) which perform the same functionality. It is possible to draw a parallel because concepts can evolve and develop dierent structures while their functions remain. (2) The similarity which stems from existence of the same characters in all the evolved species. For instance the carnassial for cats and dogs. This group is called monophyletic because the carnassial character is inherited from the common
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
ancestor of the two groups and this character is exclusive in this relationship. Thus two technological concept holders of a speci®c characteristic which does not exist in other concepts are the only heir of the same initial concepts. (3) The similarity which stems from the common character which is unchanged in relation to the initial ancestor's. This group is paraphyletic (the tortoise, the salamender, the human ®ve ®ngers rear limb). This character is inherited from an ancestor who does not belong to the group. Other animals have this character out of the group. The tortoise and human common ancestor is also the horse ancestor. The horse has only one ®nger, and the human is more similar to the horse than the tortoise, indeed both are mammals. Thus some characteristics exist in concepts throughout their evolution, identically to the characteristic of the ®rst generation. The classi®cation proposed by Hennig is based on the three above types of similarity in terms of characteristics. We propose to use the taxonomies elaborated by evolutionnists, phylogenetic classi®cations or cladograms, for modelling system's evolution. Thus, the idea is to classify knowledge regarding their common evolved characteristics in a structure which could constitute a track of the Knowledge System evolution. Moreover, the cladistic trees seem to be shorter than the trees resulting from other types of classi®cation. One of the main interests in this theory is that the introduction of a new species (for instance in our point of view a new technological concept) can lead to a reconsideration of the whole previous classi®cation. This analysis could improve the present model of evolutionary technologies' trees which allows to represent evolution of technological concepts regarding the historic de®nition of technological objects, presently used in the CEA. An evolutionary technologies' tree links a general concept (a generic species) and the technological concepts which stems from it by the ®xation of speci®c objectives. The analysis of each technological concept in terms of disadvantages and advantages gives the opportunity of a further development of this technology. Then new objectives can be assigned and other technologies developed. Those models can
425
be also used to improve the Design Rationale approach [9]. 5. An architecture for evolution management Knowledge System evolution can be partly modelled by the new points of view in the MKSM macroscope: the context de®ned by history of mutations and accomodations, the semantic representation given by genetic classi®cations (evolution trees). Those models complete the others available in MKSM. A fundamental problem is then to observe the Knowledge System in an organisation, in order to control its evolution. In arti®cial systems, evolution is not random and is partly the consequence of some decisions, especially the decision to incorporate new knowledge in the corporate repository. We discuss here a conceptual architecture to perform that kind of evolution management, and, in particular, a system design able to give some decision support in the organisation to make it evolve in a coherent way. If we suppose now that dierent points of view are able to insure evolution of knowledge in the organisation, some of them will have an eect on the own structure of the Knowledge System. ParetsLlorca et al. works proposed to manage this evolution thanks to a ``metasystem'' which is able to analyse and perform the changes [7]. Following Parets-Llorca and the previous works of Simon and Piaget we propose the existence of a ®fth system: the Knowledge Evolution System which is in charge to manage the evolution. Three elements can be pointed in such a system: an observing system, an analysing and deciding system and a performing and capitalising system. (1) The Observing System (OS) observes and analyses the necessary adaptations which do not imply genetic changes. The accommodation process is controlled by this system. (2) The Decision System (DS) takes the decisions concerning the acquired characteristics which are transmitted by inheritance and thus is able to modify the knowledge structure. Through this system it is possible, in co-ordination with the agents of the organisation to decide what
426
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
new pieces of knowledge can be introduced or on the contrary what technological concepts are no more relevant. (3) The Capitalisation System (CS) performs the evolution transformations. It is implied in the mutation as in the adaptation process. Such a structure allows us to represent the dierent ways of evolution and gives some tools able to maintain a Knowledge System. But this model which is relevant to explain how the Knowledge System evolves is still insucient to describe the evolution itself. We propose to draw the tracks of this evolution by using a phylogenetic classi®cation of the knowledge. The integration of the above theories suggests us to propose the model of Knowledge System evolution described in Fig. 8. Our analysis suggests that three ¯ows are the basis of knowledge evolution process: The ®rst
one appears in the OID system between the dierent agents linked by formal or informal networks. This interaction is the source of the knowledge production (1). The second and the third ¯ows exist between the Evolution System and · the agents of decision system for decision support (2), · the knowledge system to perform evolutionary actions and create evolution trees (3).
6. Conclusion This work is an ongoing research. Once the Knowledge System is analysed in an organisation, we consider its evolution.
Fig. 8. The evolution management system (DSS: Decision Support System, OS: Observing System, CS: Capitalisation System).
F. Barthelme et al. / European Journal of Operational Research 109 (1998) 414±427
The MKSM methodology used to analyse knowledge system, incorporates ``naturally'' three evolution points of view answering the following questions: in what context the knowledge has evolved (or evolves), what makes sense in the evolution of the knowledge (the concepts or the solutions), and what information is linked to the evolution? However, up to now, MKSM gives no models to answer these questions. We have addressed the ®rst two questions, and given hints to build models for the two corresponding points of view. We choose to describe this evolution with Lamarckian concepts, indeed evolution of such an arti®cial system is guided by intelligence instead of chance. Those concepts give a basis to trace the history of concepts, with annotation referring to evolution. It is a good candidate to describe the context in which the considered knowledge has evolved. We choose to model the evolution memory by using evolution trees suggested by the Cladism. Phylogenetic theory traces the evolution tree with rigorous criteria which ®t with our technological concepts. It is a good candidate to describe, as in design rationale, the argumentation and the classi®cation of concepts (or solutions) through time, giving a ``cognitive meaning'' to evolution. The last problem addressed in that paper, is the dynamic evolution of knowledge. There is a need to observe and take decisions about the new knowledge constantly created in organisations. The architecture for Knowledge System evolution proposed in this article complements the MKSM method [10]. Introducing the existence of an evolution system oers a way to control and memorise the evolution of concepts. Indeed, if the evolution system is able to modify the evolution trees, it is possible for the human agents to interact with this system. The decision support system features, such as the de®nition of knowledge evolution indicators
427
will be developed further. Those indicators will be tested on case studies in the CEA.
Acknowledgements We deeply thank Pr K. Schmidt, from the RISO Institute in Denmark, for all his stimulating discussions and remarks.
References [1] J.-L. Ermine, Les systemes de connaissances, Edition Hermes, Paris, 1996. [2] E. Morin, Introduction a la pensee complexe, Communication et complexite, ESF ed., Paris, 1990. [3] J.-L. Le Moigne, La modelisation des systemes complexes, Afcet Systemes, Dunod, Paris, 1990. [4] M. Denton, Evolution: A Theory in Crisis, Burnett Books, Londreys, 1988 (trad. Fr.: Evolution, une theorie en crise, Flammarion, 1992, Paris). [5] M. Ridley, The Problems of Evolution, Oxford University Press, Oxford, 1985 (trad. Fr.: L'evolution, Pour la Science, Diusion, Belin, 1989). [6] T.H. Jukes, Aircraft evolution, Nature 295 (1982) 548. [7] J.J. Torres-Carbonell, J. Parets-Llorca, Biological evolutive models applied to the evolution of software systems, Congres Europeen de Systemique, Roma, 1996. [8] Y. Deforge, Technologie et genetique de l'objet industriel, 1981. [9] Design Rationale Concepts, Techniques and Use, Erlbaum, London, 1996. [10] E. Brunet, J.-L. Ermine, Problematique de la gestion des connaiss ances des organisations, Ingenierie des systemes d'information, vol.2, no. 3, AFCET/Hermes, 1994, pp. 263±291. [11] L. von Bertalany, General System Theory, Braziller, New-York, 1968 (trad. J.B. Chabrol, Theorie Generale des Systemes, Dunod, Paris, 1973). [12] J.-L. Le Moigne, La theorie du systeme general, P.U.F., 1977, 2nd ed., Complementee, 1984. [13] J. de Rosnay, Le macroscope vers une vision globale, Seuil, Paris, 1975.