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Wisdom - the Blurry Top of Human Cognition in the DIKW-Model? Anett Hoppe1 Rudolf Seising2 Andreas Nürnberger2 Constanze Wenzel3

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Wisdom - the Blurry Top of Human Cognition in the DIKW-Model? Anett Hoppe1 Rudolf Seising2 Andreas Nürnberger2 Constanze Wenzel3 EUSFLAT-LFA 2011 July 2011 Aix-les-Bains, France Wisdom - the blurry top of human cognition in the DIKW-model? Anett Hoppe1 Rudolf Seising2 Andreas Nürnberger2 Constanze Wenzel3 1European Centre for Soft Computing, Email: fi[email protected] 2Otto-von-Guericke-University Magdeburg, Email: [email protected] 3Otto-von-Guericke-University Magdeburg, Email: [email protected] Abstract draws from a real integrational view throughout scientific communities. Therefore, in Section 3, we Wisdom is an ancient concept, that experiences a review definitions used in different domains in order renaissance since the last century throughout sev- to outline their common points and discrepancies. eral scientific communities. In each of them it is In Section 4, we finally try to apply the found interpreted in rather different ways - from a key definitions to the DIKW-model. We argue that ability to succesful aging to a human peak perfo- none of the definitions we found in several different mance transcending mere knowledge. As a result, domains suggest a positioning of wisdom in a chain miscellaneous definitions and models exist. There with data, information and knowledge except the is for instance the DIKW-hierarchy that tempts to one, Ackhoff proposed himself to justify his model. integrate the concept of wisdom into information Section 5 offers a closing reflection on how our science, still without providing a proper definition. conclusions might affect the view of computer The work at hand tries to sum up current ap- science, artificial and computational intelligence/ proaches (out of computer science as well as others) soft computing on wisdom and proposes directions with a focus on their usefulness for the positioning of further research. of wisdom atop the DIKW-model and the actual usefulness of the term for information science. At 2. The Data-Information-Knowledge- the end, with our characterization of wisdom as a Wisdom-Chain fluctuating concept, we propose fuzzy sets to model wisdom as a scientific concept. Data, information, and knowledge can be defined as terms that directly build on top of each other. Keywords: Wisdom, Knowledge, Information, A first approach to distinguish the concepts may Data, Fuzziness have been published by Nicholas L. Henry in 1974 [2]. Whereas he did not actually offer a hierachical representation, the necessary transition is strongly 1. Introduction implied [3]. First notions of a hierarchy ordering the Wisdom has been referred to for more than twenty terms were proposed by Milan Zeleny [4] and later centuries. While an ancient concept, it experiences Russell L. Ackoff [1]. Both authours are credited a renaissance since the last century throughout with responsibility for the proposition of the DIKW several scientific communities, for example in pyramid (Fig. 2), altough none of them never ref- psychology, neurology and computer/information erences such a structure. science. By all of them, it is interpreted in rather different ways – from a key ability to successful aging (lifespan psychology) to a human peak perfor- mance transcending mere knowledge (information science). As a result, miscellaneous definition approaches and integrating models exist. The work at hand examines the integration at- tempt mainly used in information science – the Data-Information-Knowledge-Wisdom-Hierarchy (DIKW-Hierarchy), dating back to Ackhoff [1]. It displays wisdom at the top of the DIK-hierarchy, Figure 1: The Information (or Knowledge) Pyra- enhancing the concept of knowledge by still un- mid: Relations between data, information, knowl- known properties. (A further description of the edge and wisdom. model and its implications can be found in Section 1.) However, while the complexity of these concepts Whilst there exist definitions for the underlying with respect to their context, required understand- terms of data, information, and knowledge, that ing and experience increases as discussed in [1] several domains may agree upon, wisdom still with- sometimes a different representation, as depicted in © 2011. The authors - Published by Atlantis Press 584 Fig. 2, is used that include these aspects. either exactly or approximately, a message In the following, we briefly define data, informa- selected at another point.” [6] tion and knowledge as proposed by Ackoff, while the definition of wisdom as the core interest of He conceived of communication purely as the trans- this work will be referred to in the following section. mission of messages – that is sequences of data – completely detached from the meaning of the sym- bols. Shannon included “new factors, in particu- • Data: Data is given by simple sequences lar the effect of noise in the channel, and the sav- of signs and symbols that have no further ing possible due to the statistical structure of the meaning besides their simple presence. original message and due to the nature of the final destination of the information” [6]. He introduced • Information: Information is data that has the variables of the information entropy and redun- been given meaning which allows to answer dancy of a source, and its relevance through the questions like “who”, “what”, “where”, and source coding theorem and other statistical mea- “when”. E.g. in computer science data stored sures. The underlying logic-algebraic structure of in a (relational) data base is given meaning by statistics and probability theory is the Boolean al- naming a row (attribute) and assigning them gebra. Thus we consider probability theorie as a to a (named) entity. special case of fuzzy set theory that is covered by statistics and probability theory. On the contrary, • Knowledge: Knowledge is information that fuzziness in information can not be covered by prob- is connected by some relations. It allows to ablity theory and statistics because information is answer “how”-questions. not synonymic to data. Due to the fact that Shannon reduced in his article the concept of information to that of data (or signs) Ackoff [1] distinguished as a further step (between or message (i.e. a sequence of data or signs) there knowledge and wisdom) also understanding. While have been many misunderstandings in the history knowledge in his view is simply based on collected of information theory. masses of information, understanding requires in The mathematician, physicist and scientific man- addition probabilistic or interpolative processes in ager Warren Weaver wrote his paper “The math- order to answer “why”-questions. These processes ematics of communication” [7] to propagate Shan- could then be used to create new knowledge or in- non’s Mathematical Theory of Communication to a formation. On the other hand this definition also general and scientific interested public but more- means that understanding can not exist on its own: over, he considered the concepts of information and it requires knowledge and some kind of reasoning communication in a philosophical way: mechanism. “In communication there seem to be prob- lems at three levels: 1) technical, 2) se- mantic, and 3) influential. The techni- cal problems are concerned with the accu- racy of transference of information from sender to receiver. They are inherent in all forms of communication, whether by sets of discrete symbol (written speech), or by a varying two-dimensional pattern (television). The semantic problems are concerned with the interpretation of mean- ing by the receiver, as compared with the intended meaning of the sender. This is Figure 2: Relations between data, information, a very deep and involved situation, even knowledge and wisdom (taken from [5]) when one deals only with the relatively simple problems of communicating through speech. [...] The problems of influence or effectiveness are concerned with the suc- 2.1. Fuzziness of the concepts of Data, cess with which the meaning conveyed to Information and Knowledge the receiver lead to the desired conduct on Claude E. Shannon’s information theory is statisti- his part. It may seem at the first glance cal science. He opened his seminal article in 1948 undesirable narrow to imply that the pur- with the assertion: pose of all communication is to influence the conduct of the receiver. But with any “The fundamental problem of communica- reasonably broad definition of conduct, it tion is that of reproducing at one point, is clear that communication either affects 585 conduct or is without any discernible and moral or ethical codes. Compared to the defini- provable effect at all.“ ([7], p. 11) tion of understanding given above, this means that wisdom requires knowledge and – possibly several In the revised version of the paper that was pub- different – reasoning mechanisms that are able to lished in [8], Weaver explained the trichotomy of the handle complex additional constraints implied by, communicaion problem in extenso and he devided e.g., ethical codes. it into three levels: 1. Level A contains the purely technical problem 3.1. Wisdom in Philosophy involving the exactness with which the symbols Philosophers as the self-declared "lovers of wisdom" can be transmitted (Greek φιλoσoφια; Latin: “philosóphia“ - “love of 2. Level B contains the semantic problem that in- wisdom“) provide us with the first known efforts of quires as to the precision with which the trans- a proper definition of wisdom. In the early tradi- mitted signal transports the desired meaning, tion, wisdom was widely defined as attempt to re- 3. Level C contains the pragmatic problem per- veal the mysteries of the natural world and the life taining to the effect
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