Data to Wisdom

8/2/2019 Data to Wisdom

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DIALOGUE AND UNIVERSAU.SMNo- 5-6/2005

Andrew Targowski

FRO M DATA TO W ISDOMABSTRACT

The paper defines units of cognition from data, through; information, concept,knowledge, and to wisdom, applying the Semantic Ladder. This concept is later used indescribing different levels of computer information systems and defining a process ofdecision-making. Finally, the Semantic Ladder is applied in understanding art, wherecertain compositions reflect different units of cognition, including the simplest and mostcomplex ones. This study implies thai wisdom as the ultimate unit of cognition is theresult of hierarchical processing of dala, information, concept, and knowledge. Whatdoes it mean lor civilization? The more we know, the more we want; and we may be inmore trouble! Can we overcome knowledge that we created and become wiser?

Key words: units of cognition; data; information; concept, knowledge; wisdom;Semantic Ladder; computer informalioii systems; process of decision-making; under-standing art.

INTRODUCTIONThe purpose of this study is to define information, mainly in terms of cogni-tion units, and also find out its relation towards wisdom. Once we understandinformation, then it is possible to define its role in an organization, particularlyat the level of information systems and decision-making. Finally, different unitsof cognition are applied to understand art. In conclusions we will ask what do

information and wisdom mean for the well-being of civilization.


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5 6 Andrew Targowskifactor. Hartley (1928) showed that a message of N signs chosen from an "alphabet" or a code book of S signs has S"^ possibilities, and that the "quantity oinformation" is most reasonably defined as the logarithm:H = N lo g S [1]

Since Hartley's time, this definition of information as a selection of symbolhas been generally accepted, although widely interpreted. As a result, Hertleytheory crystallized into an exact mathematical definition, provided by Shanno(1948), according to him, the generalized information quantity of a message othe probability p of a - event is:I = -]o g2 p( a) [2]

It this approach is useful in business decision-making" Let's assume that message that the distance from Kalamazoo to Chicago a =150 miles has p=and therefore 1 = 0, since Log; 1 = 0 (because 2" = 1). In other words, from thquantitative perspective, this message contains no information. However, for thindividual using his personal car for business purpose, this massage containinformation that can be measured monetarily, since, if for each mile driven thindividual receives a $0.40, that 150 miles means $60 in information value fohim/her.

An increase in information yields a resultant reduction of chaos or entropyEntropy, in statistical thermodynamics (II Law), is a function of the probabilitof the states of the particles that form a gas. In the quantitative communicatiotheory, the entropy means how much information one must introduce into given information-oriented system to make it informationally organized and athe same time reducing its chaos. The relationship between information anentropy is presented most objectively by the Shannon-Wienner formula (1949)H,,, = - I p ( a ) lo g 2 ( a ) (bit) [3]

In a descriptive sense, entropy is referred to as a "measure of disorder". Information introduced to a given system eliminates that disorder and is thereforsaid to be "like" negative entropy or order. Starr (1971) demonstrates the idea oentropy using the following example: suppose that eight different orders can btransmitted from the bridge of a ship to the engine room. If those orders arequally likely, then the probability of any of these being sent is p=l/8. Knowinp, entropy H can be determined:


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From Data to W isdom 57

Table 10-1. Eight Messages in a Binary Coded

Order Number01234567

Binary Form000001010O il100101110111

The entropy function is widely used in communication networks in codingfor the assessment of channel capacity and code efficiency. However, from thehuman communication point of view, this perspective has limited applications.Because it does not provide any human-oriented meaning to the "bits and prob-abilities". This approach has a technical significance how to design a technicalcommunication channel. Finally, the entropy function lacks the semantic mean-ing of information, which can drive human communication..THE QUALITATIVE PERSPECTIVE OF INFORMATION

It is obvious that without quality, information loses its usefulness. This ideais reflected in a well known phrase often used in information processing"garbage, in garbage out". The emphasis on the quality aspects of informationand their importance in organizations is apparent in two streams research on: Message flow, e.g. Monge , Edw ards, and Kriste (1978 ), Roberts and

O'Reilly (1978); and Decision-making, e.g. Cyret and March 1963), O'R eilly, Chatman, andAnderson (1978).One can argue that literature addressing message flow can add context andrealism to decision-making. Likewise, decision-making literature can help makethe outcome variables of message flow more concrete and measurable.In order to communicate (message flow) or make decisions (decision sci-ence) successfully, information must possess eight qualitative attributes (Tar-

gowski 1990a):


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58 Andrew Targowski VcrifiabiUty (V) Price (P)Each attribute can be measured on a scale from 1 to 5, where the highest rating corresponds to the greatest influence on the attribute on the communicatedecision-making process. A set of all the attributes creates the InformatioQuality Space (IQS), which is depicted in Figure 1.

A * . V

Figure 1 Hie [de:il]iiriimali


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From Data to Wisdom 59Through the interpretation of message transmission and decision-making

values, we are able to obtain the holistic measurement ofthe feasibility of deci-sion-making-driven by information F (dm) in the organizational environment:F(dm)i = P(c s)xP T(d m ) [8]

F(dm), = P(cs) X PT(dm)i = 0.5 x 0.2 = 0.1 [9]F(dm)2= P(cs) X PT(dm)2 = 0.5 x 0.4 = 0.2 [10]

The proposed qualitative assessment of information provides the followingrules: Rule 1: The larger the Information Q uality Space is, the better com muni-cation and decision-making potential has a given individual, since a givenIQS approaches the ideal IQS. Rule 2: The smaller the difference is between the Information QualitySpaces of communication agents, the greater the probability of communi-cation success these agents have, since P(cs) approaches 1, Rule 3: The higher probabilities of communication success and higherpotentialities of decision-making will lead to the higher feasibility of de-cision-making, since F(dm) approaches 1.These rules imply that a decision-maker either has quality information avail-able [P(dm)] to make choices or will obtain information through succe.ssfulcommunication [P(cs)]. The proposed indicators can be integrated into a "theoryof choice" and a "theory of search" (Cyret and March 1963). Some researchfrom the pre-Information Wave period indicate that decision-makers prefer tolook for inexpensive and well accessible information.' Let's hope that the Inter-net potential (paid or free information) should improve decision-makers questfor better quality information in the 21st century.

THE COGNITIVE PERSPECTIVE OF INFORMATION

In order to describe the central role of information in the development ofcivilization, the theory of Information Ecology creates a model that views theexisting body of accumulated human information as a distinct, apart from theminds of information users. This body of information is called a Cognition Res-ervoir (CR) as it is shown in Figure 2. The recognition of the CR permits re-' O'Rcily Churtman, and Andersen (1978) indicate the decision-makers are noticeably biased


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60 Andrew Targowskisearchers and users to assign descriptive characteristics to cognition unit(among them to information), and treat CR as though it was an independenentity of civilization. Information Ecology considers the interaction betweeusers and the CR to be the most significant factor shaping human civilization.

f information j f knowledge j

Figure 2 The Cognition Reservoir of Civilization

The Cognition Reservoir contains a semantic cross-section of cognition (decreased chaos) with cognition units of data, information, concept, knowledgeand wisdom. These units are created by science and practice (culture in genera


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From Data to Wisdom 61 Datum (D)

A measuring unit of cognition that describes transactions between natural,artificial or other semantic systems. In business, data can measure performancecharacteristics of production, distribution, transportation, construction, or ser-vice. For example, Dow Jones Stock Index (at the New York Stock Exchange)was 10,000 points high on February 15, 2005. Information (I)

A comparative unit of cognition that defines a change between the previousand present state of natural, artificial, or semantic systems. Businesses oftencompare performance characteristics in two or more periods. For exam ple, DowJones Stock Index was 11,000 points high on February 14, 2005. The change is-1,0 00 or 9% Concept (C)

A perceptive unit of cognition that generates thoughts or ideas that createsour intuition and intentiona sense of direction. For example, due to the strongmarket changes, should an investor to sell, to buy, or to hold his/her stocks? Knowledge (K)

A reasoning unit of cognition that creates awareness based on scientific data(ex.: Census Bureau, research), rules, coherent inferences, laws, establishedpatterns, methods and their systems. Knowledge provides a point of reference, astandard for analyzing data, information and concepts. Knowledge can be cate-gorized in many ways. Let's take a look at their four following kinds: Domain knowledge (Kd) Societal know ledge (Ks) Personal knowledge (Kp) Moral knowledge (Km)Once again elaborating on the previous examples, an investor will applyhis/her or adviser's financial knowledge (Kd) to find out which concept he/sheshould apply. He/she can also apply remaining kinds of knowledge to evaluateeach concept option.

Wisdom (W)


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62 Andrew TargowskiThe cognition units can that composed the Cognition Reservoir can be strutured from simplest to most complex in the Semantic Ladder, shown in Figur

3. Events occur at the existence level that are communicated as data and inserted into the Semantic Ladder of a person, discipline or organization. Thesdata are subsequently processed into information, and information is processeinto concep ts, which are later evaluated by available know ledge filter, befoone of those concepts will be chosen by decision-maker's wisdom. Then, frame consisted of a message and decisio n-m ake r's in tentions (very often diferent than the message's content) is returned as a feed-back to the level of exitence.

^ ^ ^ 2 Wisdom

11 _I i 1

f Moral "^^ Knowledge jf Domain \\^ Knowledge J

f Societal^ KnowledgeM f Personal \ j> ^ _ Knowledge J :| :

1 c1 c1 ^1

! : : ; : ; - ; : - : ;

Concept-

in format ion I

Data 1

Existence

Paradiamschoice

1awareness

direction

change

measurement

] happening


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From Data to Wixdom 63

THE COMPUTER PERSPECTIVE OF INFORMATION

The human cognition increases along with the development of info-communication technology, at first: printing, later polygraphic, and recentlytele-computing. The latter is known under the name of "inforniation systems"(IS) or computer information systeiTLs. However, every level of cognition re-quires a different kind of an IS.

Computerized SystemsExpert Systems

Expert Systemsand Data MiningSystems

Expert Systems

Information Systems

TransactionProcessing Systems

o 2 a>Q . Q UJ k_ IWisdomProcessing

KnowiedgeProcessing

ConceptProcessing

InformationProcessing DDataProcessing

On-line Systems ( computer Nerworks

ParadiamsChoice

Awareness

Direction

Chatige

Measurement

Transmission


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64 Andrew TargowskiAt the lowest cognition level takes place data processing under a format oTransaction Processing Systems. At the level of information, supporting sy

tems are of the Information System kind, which compare "planned with actualperformance characteristics. The higher levels of cognition require Expert Systems based on artificial intelligence, with the exception of knowledge processing, where in addition Data Mining Systems are of great value too.The hierarchy of Computer Cognition Systems is depicted in Figure 4. Everkind of these systems requires a different architecture, skills to build, timelinand budget. It reminds the situation in construction, when residential houseneeds different know-how than public buildings, and so forth.

THE DECISION-MAKING PERSPECTIVE OF INFORMATIONThe cognitive perspective of information can be useful in understanding howdecisions are made. A decision is an act of wisdom in choosing a course of action in right time. To reach the cognition level of wisdom one must pass througfour other semantic levels, each one specializing in a specific cognition unprocessing.A number of frameworks have been offered to define the phases of decision

making. Perhaps the best known of these is Simon's (1965) "intelligencedesign-choice of decision" triad. Ackoff (1978) perceives the decision-makinprocess as a function of problem solving. Mintzberg, Resinghani and Theore(1976) add that the decision-making process is subordinated to political gamand decision control in a given circumstances.The information-oriented approach to decision-making in problem solving ipresented in Figure 5, where the following five phases are recognized:1. The Problem Measurement Phase identifies a problem based upo

transactions which are processed into organized data, indicating problem symptoms and implicit stimuli for action.2. The Problem Diagnosis Phase involves comparisons of different burelevant sets of data, which leads to the production of information on a changin a given state of the problem.3 . The Decision Conceptualization Phase defines concepts of actiontriggered by the change, diagnosed in the Phase Two. The automation of thphase is very difficult; however, knowledge engineering proposes some tech

niques of discovering new facts. One cannot exclude the possibility that one daa new concept will be generated by the computer.


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From Data to Wisdom 655. The Decision Choice Phase chooses wisely a given decision conceptfrom iTiany ones evaluated by knowledge in the previous phase. Wisdom ap-plied at this phase is a result of accumulated (recorded in a human or computermemory) experiences coming from analysis of existence (ontological aspect ofbeing) or the study of knowledge (the epistemological aspect of being).

backward chaining

CONCEPTS

Decision- H CONCEPTUALIZA-TION

INFORMATION

Kroblem \DiagnQsis )1

DATA1 ' . " . " . - . ' :-. -

ConceptsVerification viaKNOWLEDGE

^ _ _ ^ Prohlem ^^V Measurement J

TRANS AOTION prob lem


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66 Andrew TargowskiThe phases of problem-solving cycle are guided by the decision support routinethe operating system which plans the cycle (schedules, conceptualizatio

strategies, commits resources to the cycle, etc.) and switches the decision-makefrom one phase to another. The implementation of the selected decision dependon the power game and consensus among the persons affected by it.The decision-making cycle incorporates a backward path in the events thafeedback indicates the need to correct, clarify or repeat the previous phase. Ialso is implied that the cycle is applicable only to rational decisions, as Simodefined bounded rationality, where formal rules are applied.

INFORMATION AS ARTArt is information which cannot be falsified. The history of art is the history oviewing the world and reality in a language of beauty. Until the 18th century awas exact, "photographic" registration of events and figures playing importanroles in the society. It was a time of Leonardo da V inci's academicism, emphasizing symmetry and perspective (data processing). Art was in those times, rhetoriof power. Its mission was to glorify a ruler and his court (Figure 6).After the Great French Revolution, artists abandoned their sponsors; thebecome poorer, but free to do what they wanted to do. In the 19th century, romanticism in music and literature as well impressionism in painting liberateartists. They left their studios and entered the real worid of nature. Van GaugeMatisse, Gauguin go to the country side and paint sun, flowers, and good moodsupported by a good company and wine. Ever since artists have tried to defintheir own concept of reality, and they often saw it as a processed actuality, wita message for changesaying "we are free and can paint as we wish" {informa

tion processing).The academicism in art, based on Leonardo da Vinci's rules of symmetrand perspective, was replaced in the 20th century by a law anything permittinpossible in art. In 1901 Paul Gauguin proclaimed the manifesto of postimpressionism. The artists broke with strategy how to paint, and looked for new strategy what to paint. The same process takes plays in physics. Now aand science seek the same clue, which is truth. The 20th century in art is a century of permanent search by the avant-garde for a perception and synthesis otimes {concept processing).Pollock's action painting is an art without beginning and end. It is reflectio


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From Daia lo Wisdom 67

Paradigms

Warchol's pop-artknowledge processing

Picasso, Ernest. Pollock, Rothkoconcept processing

Van Gauge & Ma tisse'simpressionisminformation processing

Leonardo da Vinci's academicismdata processing

choice of truth

awarenessof new rules & trends

direction - anti-war &anti-scholacism

change message -"we are free, can paintas we wish"

measurem ent ofsymmetry andperspective

Human Cogni t ion


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68 Andrew TargowskiYork's happenings, Italian transavan-garde, and superealism. All quest for newrules and patterns. Marshal McLuhan even proclaimed "the medium is the message". On other words information "converts itself into energy and matteA secret of life and its chance of survival reflect knowledge processing.

Whether art will reach a level of wisdom processing is a question. Jean Dubuffet who is considered equal to Picasso says; "The w ise art? What a crazquestion! Art is nothing more than a product of happiness and craziness. A mawithout bread dies, without art the man dies from boredom." It is one artiopinion.The Polish artist. Stanislaw Witkiewicz, said in 1919: "art is a disciplinwhere a lie never leads to positive results". The Witkiewicz's rule can be teste

in the Soviet Union and Nazis Germany. In the former, after the BolsheviRevolution in 1917, the new order accepted only socialistic realism in art. Thartists could only glorify work in the fields and the shop-floors. Artists such as great poet, Osip Mandelstam who did not follow this direction was sent to Gulag, or convicted as parasite Joseph Brodsky, a future Nobel laureate, did noobtain a government license to be a poet! Vlodimir Majakowski, a poet of thRevolution, in protest against this official cultural policy committed suicideBoris Pasternak, who received the Nobel Prize for Doctor Zhivago, could noaccept the prize, since the Soviet prime-minister Nikita Khrushchev did not likthe book. At the beginning of the 1980s (the Brezhniev regime), the avant-gardexhibition in Moscow was demolished by government bulldozers. Needlesto say, that in those 70 years, Soviet official art lied, how ever it lost control oits artists in 1991, when its sponsor, the Communist Party was proclaimeillegal.

In the period between two World Wars, Berlin became the capitol of decadency, and the movie "Cabaret", illustrates this period in German culture. I1933 when Hitler came to power, the deconstruction of German culture begaon a wide and premeditated scale. Police closed the famous Bauhaus schooAbout 25 directors of museums were fired, leading artists fled the country. Aminister of propaganda Joseph Goebbels ordered books to be burned whicwere not in line with national propaganda. It took place in the same countrywhere a century ago, Heinrich Heine said: "where books bum there minds flarup". All avant-garde painters were condemned. The Fuhrer asked "what artist ithat who paints sky in green and grass in blue?" He called the avant-guard sick people who should be sent to psychiatric hospitals. A new school of "thbeautiful German" could only be practiced. It was nothing more than a repet


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From Data to Wisdom 69Today, a free man applies art, literature, and music as a thermometer of life'srush and a compass which indicates the world's state of mind. These instru-ments are metaphors, since art's calling is to provide a perception of reality in alanguage of beauty. Neverless, art to be important must look for truth, this questnowadays is undertaken by artist-photographers who travel around the worldand document tragic and unwise human stories, which can be called wisdomprocessing, as a alert to be more informed and wise about these causes and re-

sults.

CONCLUSION

The Information Wave impacts strongly the way humans perceive cognitionand its ultimate unitwisdom. Information itself has no one universal defini-tion, it can be perceived in terms of perspectives or images or both, dependingon circumstances. After civilization learned how to process materials, now itlearns how to process information. It leads to the transformation of hard to softcivilization, with more emphasis on informed, knowledgeable and even wisercontrol of events, with the desire to cognize more and more. The latter is a goodsign of human awareness that the future of civilization depends on human wis-dom, which comes along with more knowledge-based and positive experiencesof its applications.

On the other hand, the more informed and knowledgeable decisions are forbusiness the more problems they may bring for the society, which is growing atthe time when employment goes down and strategic resources are more inten-sively use or even used. What does it mean for civilization? The more we know,the more we want and in more troubles we may be! Can we overcome knowl-edge that we created and become wiser?

BIBLIOGRAPHY

Ackoff R. L. (1967), "Management Misinformation Systems". Management Science.Nov. 14, pp. 147-156.Anthony R. N. (1965), Planning and Control System: A Fram ework for Analysis. Bos-ton: Harvard University Press.Ackoff R. L. (1978), The Art of Problem So lving. New York: J. Wiley.Beach L. R., T.R. Milchel, M. D. Daeton, and Prothero (1997), "Information Relevance,Content, and Source Credibility in the Revision of Options". Organizational Behav-


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7 0 Andrew TargowskiCliaran R. (1994), "New Networks Reshape OrganizationsFor Results". Han-arBusiness Review. September-October, pp. 93-103.Cyert R. and J. March (1963). A Behavioral Theory of ihe Finn. Englewood Cliffs. NJ

Prentice Hall.Dewhirst H. D. (1971), "Influence of Perceived Information-sharing Norms on Communication Channel Utilization". Academy of Management Journal, no. 14. pp305-315.Davi.s St., and J. Bolkin (1994), "The Coming of Knowledge-based Business". HarvarBusiness Review. September-October, pp. 165-189Draft R. L. and R. L. Lengel (1984). Infonnation Richness. Greenwich, CT .: JAI Press.Galbraith J. (1973), Strategies of Organization Design. Reading. MA.: AddisonWesley.Gore A. (1993), Earth in ilie Balance. New York: A Plume Book.^, (1991). "Information Superhighways: The Next Information RevolutionThe Futurisi. January-February, pp . 21-23.Halal W. E. (1996), The New Management. San Francisco: Oerett-Koecliler Publishers.Harris Ph. R. (1985), Management in Transition. San Francisco: Jossey Bass Publishers.Kanler R. M. (1989). "The New Managerial Work". Harvard Bu.siness Review. November-December, pp. 85-92.KarrakerR. (1988). "Highways of Mind". Who le Earth Review. Spring, pp. 4- 15 .Leavitt H. J. (1979). "Beyond (he Analytical Manager". California Management Review, no. 3, pp. 5-12 .Lengel R. H. (1984), "Management Information Processing and Communication-MediSource Selection Behav ior". P h.D. Dis.serlalioii. Texas A & M University.Mintzberg H.. D. Rai.singhani. and A. Theoret (1976), "The Structure of UnstruclureDecision Process". Adm inistrative Science Q uarterly, no. 21 , pp. 246-27 5.Mongc P.. J. Edwards, and K. Kirste (1978). "The Determination of CommunicatioStructure in Large Organizations: A Review of Research". //) Communication Year

book. Ed. B. Ruben. New Brunshwick, NJ.: Transaction Books, pp. 311-331.Naylor J. (1964), "Accuracy and Variability of Informalion Sources of determiners anSource Preference of Decision Makers". Journal of Applied Psychology. No. 48 . pp43-49.Nye J. S. and W. Owens (1996). "'America's Information Age". Foreign Affairs, vol. 75no. 2. pp. 26-3 6.O'Reilly C. A., J. A. Chatman, and J. C. Anderson (1978), "Message Flow and DecisioMaking". In Handlwok of Organizational Comnumicaiion. Ed. F. M. Joblin, L. LPutman, K. H. Roberts, and L. W. Porter. N ewburry Park, CA .: Sage, pp. 60 1.O'Reilly C. A. (1982), "Variations in Decision Makers' Use of Information Sources: thImpact of Quality and Accessibility of Information". Academy of ManagemenJournal, no. 25 . pp. 156-111.Powers W. T. (1973), Behavior: Tiie Control of Perception. Chicago: Aldine Publishin


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From Data lo Wisdom 71Starr M. K. (1971), Management: A Modern Approach. New York: Harcourt BraceJovanovich, Inc.Targowski A. S. and J. P. Bowman (1988), "The Layer-based Pragmatic Model of the

Communication Process". The Journal of Business Communication, vol. 25 . no. 1.Targowski A. (1990), "Beyond a Concept oi a Communication Process", The Journal ofBusiness Conmiunicaiion, vol. 27. Number I. W inter, pp.75 -86 .. (1990a). The Strategy and Architeclure of Enterprise-wide InformationManagement Systems. Harrisburg, PA.: Idea Group Publishing.(1991). "Computing in a Totalitarian Stale: Poland's Way to an InformedSociety". Journal of Information Management. Information Executive, vol. 4. no. 3,Summer, pp. 10-16.., (2003). Electronic Enterprise. Strategy and Architeclure. Harrisburg. PA:Idea Group Publishing.Weick K. E. (1979), The Social Psyciwiogy of Organizing. 2nd ed. Reading, MA.: Ad-disson-Wesley.


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