dss: buzzword or or challenge?

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European Journal of Operational Research 22 (1985) 1-8 1 North-Holland Invited Review DSS: Buzzword or OR challenge? Henk G. SOL Department of Informaties, Delft Unwersity of Technology, Julianalaatr 132, 2628 BL Delft. Ne,,.erl,.mds Abstract: The original concept of Decision Support Systems has evolved from a movement with various contributing disciplines, like information systems and operations research to a bandwagon attracting numerous researchers and practitioners. We may wonder what new ir~sights we gained from this movemen! to improve organisational efficiency and effectiveness. Some people even question the vakdits of DSS a, a research track. Clearly a new vision on the concept of DSS is needed A delineation of DSS is proposed in order to make it potentially a new OR challenge, instead of a buzzword. Keywords: Information systems, decision support 1. Introduction organisatioml efficien;:y and effe,'tiveness. On the other hand, we have to observe that the concept of Numerous researchers and practitioners llave DSS does not stand a/one, bu! it is closely related no hesitations in putting the label Decision Sup- to developments in the realm of information tech- pori Systems (DSS) on their work. Huber (1992) nology and informatkm system.~: in the use of identifies that there is no clear definition of DSS. computer.~ in orgar, i::ations we ma> distinguish He sees DSS as a hybrid field with many reference three pha'~es. disciplines. According to Naylor (1982), DSS is a 1. Electronic Data Processing (EDP). redundant term, not based on any formal concep- 2. Management lnformatmn Svste,ns (MIS). real model. 3. Decision Support Systems (DSS). It is remarkable that the term DSS is much used 1. i. EDP without a very strict definition of its content. Many writers se:m to approach DSS a- a philoso- This phase can be characterized b) automation phy to seek a useful complementarity be, ween of mass administrations. The developments are technological to,'ls and human judgement ar, zt dis- controlled by technological constraints. Benefits cretion. Klein ++.nd Hirschheim (1985) point out are gained by shorter and more frequent dat+~ that "there appears to be an implicit assumption processing. Information requircme:~t~ are mo,tly on the part of DSS writers that DSS are beneficial given, or at least well-defined. Evaphasis is on the to organisations and the DSS interventior, process completeness of the requirement., specification and is not inherently polerr, i,:'. on the constlutfion of efficie;~" ~oltware. -lher,: is We do not want to raise a new. definitional a lov, support of managerial processes, especia.ly debate. Rather, we like to explore what instghts we in preparing decisioqs. gained from applying the DSS concept to improve 1.2. a tIS Rec,:ivcd March 19':;5 !:l the secend pimse computer., are more and more used for planning purposes. From several ;3377-2217/85/'$3.30 ,',3 1985, Elsevier Science Publishers B.V. (North-Holland)

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Page 1: DSS: Buzzword or OR challenge?

European Journal of Operational Research 22 (1985) 1-8 1 North-Holland

Invited Review

DSS: Buzzword or OR challenge?

Henk G. SOL Department of Informaties, Delft Unwersity of Technology, Julianalaatr 132, 2628 BL Delft. Ne,,.erl,.mds

Abstract: The original concept of Decision Support Systems has evolved from a movement with various contributing disciplines, like information systems and operations research to a bandwagon attracting numerous researchers and practitioners. We may wonder what new ir~sights we gained from this movemen! to improve organisational efficiency and effectiveness. Some people even question the vakdits of DSS a, a research track. Clearly a new vision on the concept of DSS is needed A delineation of DSS is proposed in order to make it potentially a new OR challenge, instead of a buzzword.

Keywords: Information systems, decision support

1. Introduction organisatioml efficien;:y and effe,'tiveness. On the other hand, we have to observe that the concept of

Numerous researchers and practitioners llave DSS does not stand a/one, bu! it is closely related no hesitations in putting the label Decision Sup- to developments in the realm of information tech- pori Systems (DSS) on their work. Huber (1992) nology and informatkm system.~: in the use of identifies that there is no clear definition of DSS. computer.~ in orgar, i::ations we ma> distinguish He sees DSS as a hybrid field with many reference three pha'~es. disciplines. According to Naylor (1982), DSS is a 1. Electronic Data Processing (EDP). redundant term, not based on any formal concep- 2. Management lnformatmn Svste,ns (MIS). real model. 3. Decision Support Systems (DSS).

It is remarkable that the term DSS is much used 1. i. EDP without a very strict definition of its content. Many writers se:m to approach DSS a- a philoso- This phase can be characterized b) automation phy to seek a useful complementarity be, ween of mass administrations. The developments are technological to,'ls and human judgement ar, zt dis- controlled by technological constraints. Benefits cretion. Klein ++.nd Hirschheim (1985) point out are gained by shorter and more frequent dat+~ that "there appears to be an implicit assumption processing. Information requircme:~t~ are mo,tly on the part of DSS writers that DSS are beneficial given, or at least well-defined. Evaphasis is on the to organisations and the DSS interventior, process completeness of the requirement., specification and is not inherently polerr, i,:'. on the constlutfion of efficie;~" ~oltware. -lher,: is

We do not want to raise a new. definitional a lov, support of managerial processes, especia.ly debate. Rather, we like to explore what instghts we in preparing decisioqs. gained from applying the DSS concept to improve 1.2. a tIS

Rec,:ivcd March 19':;5 !:l the secend pimse computer., are m o r e and

more used for planning purposes. From several

;3377-2217/85/'$3.30 ,',3 1985, Elsevier Science Publishers B.V. (North-Holland)

Page 2: DSS: Buzzword or OR challenge?

2 H.G. S o / / D S S : Bu=zword or OR challenge

disciplines, especially from operations research, al- between IS, MIS and DSS, but rather a gradual gerithms are pm forward to solve these planning shift in emphasis. In general, for a comparison of problems on computers with greater capacities and research contributions, one might ~,onder direct-access memory. The concept of MIS - w h a t p r o b l e m s e , re addressed, emerges. A great many writers define a MI~: as a - what paradigm or 'Weltan~chauung' governs concrete system with three functions, see Burch the process of problem conceptualization and and Stratei (1974): problem specification, - to meet legal and transzctionai data processing - what conslruct-paradigm or modelcycle is fol- requirements: lowed, expressing i~ broad '.erms the order of - tc provide information to management for sup- activities, port of planning, controlling and decision making - .vhat methodology, as an actual sequence of activities; activities in view of a problem situation is used, - to provide a variety of reports, as required, to telling what to do in which activity, e.~ternal constituents. - what project control is performed, during the

Implicit to the M~S philosophy is the assump- activities, tion that. organisational behaviour can be de- - what theory is fnllowed, contributing to the :~cribed through coupled n~odels on various levels actualization of the modelcycle arm the mefl~od- .)f aggregation. M6=,Iv, these models consist of a elegy in terms of how the activity is to be per- ,et of equations. These modc.',s a;e only applicable, formed, and especially, how alternative solutions if there is empirical support for the behavioral are to be generated. equations in these models. Comparison of the three phases results in Table

1. The table illustrates the shift in types of prob- 1.3. D S S lores, from well-structured to ill-structured.

Whereas in the EDP and MIS approach the em- Of key imp~.rtance to ~he third phase is the phase is on the identification of objects in the

development ot data base management systems inform~tmn system from a process or a data point. and interactive software. Through the technologi- of-v~ew, lies the point el departure in DSS in :l',e cal possibilities of data bases, man-machine inter- identification of individual objects, firstly in the action, data communication and wordprocessing real system, later on in the information system one can describe directly deci.~on making and controlling the real system. The incremental char- data processing by individuals. There is neither a acter of a methodology for designing DSS is often need to collect information requirements nor to referred to by evolution,-,.rv design or prototyping, develop corporate models. According to Keen which sometimes coincides with a participative (1980), project format. - DSS help managers in decision making, espe- cially in ill-structured problem situations. -- DSS support, rather than replace the managerial 2. DS,_3 definitions judgement.

- DSS try to improve the effectiveness of organi- As mentioned, we do not want to enter a new sations, rather than efficiency, debate on definitions of DSS. Gintzberg and Stohr

By using the word phases we like to emphasize (1982) remark that ' the basis for defining DSS has that, ,,o our opinion, there is no st~arp distinction been migrating from an explicit statement of what

Table 1. Companion of the three phase~,

Phase EDI' MIS DSS

Type of problems wetl-:;tructured well-tletincd ill. struclured Paradigm pro~:c~s-oriented, datalogical, data-oriented, object-oriemcd,

teel':m,!ogical ,afological sy:itelogival b.,Iodelcycle deductive ind,Jctivc hypod~,:tico-iad uct;ve Methodology lin.ra," iterative incremental Projectcontrol pro~.zc tgroup harmon. partidpative

Page 3: DSS: Buzzword or OR challenge?

It.G. Sol . /DSS: Buzzword or OR challenge 3

a DSS does to some ideas about bow the DSS toolsmith from which a DSS can be viewed. In objective can be accomplished (i.e. what eompo- accordance with this distinc.,io,'~ the concept of a nents are required?, what usage pattern is ap- DSS generater is put forward "o bridge the gap propriate?, what development process is .aeces- between general tools and specific DSS. Sprague sary?)', dnstinguishes as the raain components of a D$S a

This migration during the years can be shown data base, a model base, and an intermediate in the fo~iowing descriptions for DSS: software system which interf?ces the DSS with the

1. In the early 1970's DSS was described as 'a user. computer based system to aica in decision making'. Within :he data base fc~ decision :;,tpport one The starting point was found in the application of can distinguish between external data from public interactive technology to managerial tasks in order data sources, administrative data produced by ~.he to use computers for better decision-making. There transaction processing, system, and internal d;it~ was a strong cognitive focus in this DSS concept, createc by personal computing. viz. that of a single decision-maker. Tile models in the model ba:~ as .~nvisaged by

2. In the mid to late 1970's the DSS movement Sprague ale mostly of the equation type: A great eraphasized 'interactive computer-based systems number of so called corporate model,, or financial which help decision makers utilize data bases and models consists of definition eqt'at~ons and be- models to solve ill-structured problems'. The em- haviouca! equations. Econometric mcdel~, as phasis lies not so much on the decision process, another category, also consists of ~qoation models. but rather on the support for personal computing Another category is formed by opdmaliza',ion with fast development tools and packages for models based on linear, dynamic or stochastic financial planning, programming.

3. In the later 1970's to early 1980's the DSS A first generation o? so-called DSS generators bandwagon provides systems 'using suitable and focuses on e.quafinn models with dat~ base a~d available technology to improve effectiveness of interactive f~cilities like data-, vaodel- and I:ext managerial and professional activities'. User- manipulation, cf. Klein and Manteau (1983)and friendly software is produced almost unexception- Bergquist an,] McLean (1983). By new, the in- ally under the label DSS. Disciplines like oper- tegrated facilities are not only offeced on ations research and psychology are jumping on the mainframes, lint also on micro-computers togoher bandwagon. The concepts like information center with facilities for "down-loading from and up!dad- and prototyping are put forward in the same utter- ing to central computer systems through data com- ance as DSS. mtmication'.

4. By now we face a new technical base for A less technological framework is put forward DSS: the convergence on intelligent workstations, by Bonczek et at. .98I). They rep!ace the compo- Telecommunications put forward the issues of nents menqoned, by the concepts of a le.nguage organisational versus personal computing and dis- system, a knowledge sys,em arid a p~cblem tributed DSS. We see new technologies emerging processing system. The langua~,.e s~stem is the sum as expert systems and document-based systems, of all linguistic facilities made. available to the This is expressed by Elam, Henderson, Keen, decision maker by a DSS. A knowledge system is a Konsynski, Meader and Ness (1985) in the need DSS's body of koowledge ~'~out a problem do- for a new vision on DSS. They propose to confine main. The probtem processing system is the the notion DSS to 'the ~:xp!oitation of intellectual mediating mechanism between expre'_:sions of and computer-related teclmologies to improve knowledge in the knowledge system c-nd expre.~- creativity in decisions that really matter', sions o | problems in the lar~guage ~ystem.

The fr~lmework put forward b~ Bonczek ct ~tl. makes ~t easy to relate the work in the field c,f

3. DSS components articifial intelligence to DSS. We d~'finc an "'¢×~ err system as a compute~ system containing organised

A useful framework for resrarch on DSS is knowledge, both fact~,ai and heuristic, th:~t coil- introduced in Spragu'~ (1980). I-I.,. discusses the rains st, me sp:cific area of human expertise, a~d perspective of the end user, the 5ui',der and the :hat is ab!c to produce inferences for the u.,cr", see

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4 H.G. Sol /DS~': Bu-=~;ordor OR challenge

Chang, Melamud and S~brook (1983). assumptions governing the construction of DSS. When one looks upon an inference system as a A great many of the contributions start from

sp~iai kind of problem processing system and the premise that decision sL!pport can be achieved upon the knowledge base as a special kind of throttgh aggregation of dat3. Keen (1989)presents knowledge s)stem, then these expert systems fit a summary of me.jor case studies, all using data on neatly into the framework. Along this line a school an aggregated level The same applies to the case of researchers focuses on tt'.e representation of studies presented in Alter (1980) and Sol (1983). knowledge for decision support, cf. Fox (1984), A great many approaches are starting from the Bonczek et ai. (1983). The relevance of epistemo!- premise that more and better information will aiso ogy t~} improve decision-making processes is ad- lead to better decisions. Diekson (1983) identifies dressed by e.g. Lee (1983). as required facilities for management support:

The processes of designing :OSS is as yet not Writing, communicating, individual processing, much addressed. Sprague and Carlson (1982) ad- managhag data, problem finding, making decisions vocate an approach ' to systems analysis which is and conveying decisions. As Sprague and Carlson intended to identi~' requirements in each of the (1982) put it: "Databases and DBMS are an im- three naajor capability areas of DSS. Tile approach portant prerequisite to a DSS, because building a is based on a set of four user-o,iented entities: DSS without existing data bases and associated Representations, Operations, Memory Aids and DBMS will be extremely difficult'. Control Mechanisms'. Humplueys e t a l . (1983) 3. t he modeicycle behind a great many contri- report empirical research on rovnds and stages in buttons in the DSS-fieid can be characterized as a the de~.elopment paths and the roles played by Singerian one, trying to integrate scientific, ethical various p~rticipants, as analyzed in several pro- and esthetic modes of thot~ght in a synth.tic, inter- j~.cts. Empirical research as presented e.g. in Fick disciplinary way. The availability of data and ap- and Sprague (1980), Ginzber~ et al. (1982), Be- propriateness of data for decision-making in nnett (1983), Sol (1983), shows the variety of ap- organizations is not much questioned. Or. to put it proachcs under.'aken by variou~ ~~searchers and in another way, the modeling paradigm is rnostl~," practitioners to cr ",to systems for effective deci- the. one of thinking ir~ terms of relationships be- stun support, tween variables. Then one might apply statistical

techniqucs to estimate values of parameters in these equations, or sensitivity analysis to financial

4. DSS expcrient~s models, possibly combined with goal-seeking or Monte-Carlo sitnulation, or optimalization teciz-

It n'_.ay be dange,'ou3 to draw conclusions from niques. tlae available expertise on DSS. However, follow- But the underlyi~ig hypothesis still is that the ing the framework for evaluation outlined above it relationships hold al~d that through these the deci- is possible to make some general r:~marks, stun processes are described in enough detai! in

1. DSS are directed at ill-structured problem order to get decision support. situations. It is striking, however, how little atten- 4. As to a methodology for develoFing DSS. it tion is paid to the process of problem solving, is difficult to identify a generic framework. Keen There are various frames In describe these (1980) applies the term DSS to situations where a pro,.e~ses, tf one takes, for inrtaJace, the p h s e s o! final system can be develo~,~d orqy through an i.qte!~igence-de~ign-choice-imph.'ment~tion, then adaptive process of learning and evolution. one mii,~ht obser ,c that a great number of writel.~ Henderson and lngraham (1982) remark that 'pro- address tl,e phases of choice and implementations, totyping or ad.-ptive design has been suggested as Recently ;here in argumentaiitm to focus o,a intelli- an effective approach for developing and imple. ge~ce end choice, ~ee e.g. Land:; ' e ta l . ~ "- ,19,-~), Sol menting DSS. Empirical research ha', snowr, this (1982). design st;ategy is effective in establishing

2. As -'.o the paradigm or Weltanschz~.uung up- meaningful user involven~eJ~t and high u~er ~atis- plied, we may conclude that the one of looseiy- factior~. A comparisc.n with the irfformaticn re- coupled systems is still prevailing. It i:; striking quire.,uet~ts ge:,~erated by a st~'u~:tured group pro- how little thought is given to the elicitavion of cess indicates that p~'ototyping is a convergent

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H.G. Sol / DSS" Buz:word or OR chalienge 5

design method that may overlook important user s,..c.~c o,s inforraation needs'. It is clear that an evolutionary or iterati;e approach is prevailing in many cases of ( ~ / i / " - ' " ~ , f \/~'r_///'--'\' DSS development, see e.g. Davis e t a l . (1980) and \ - - ~ Sol (19821. We need more in3ight in the process of " ~ ~ ./" actually developing DSS. ~ I ~

5. As to the possible theories for developing Dss G . . . . . . . . ~ --.-.-~

DSS we can make several observations. Gne line ..-:oo,~ ~ . . . . . . . . . . , ..... of researchers tries to design DS5 according to [ ..... ~"'~ ] principles from cognitive psychology, Cats-Baril __~ and Hubet (1984) point out that the generalizabil- f---z-:----3 [ l "'~

language l J~no~,l rd~,e erap: r ~c,Jl ity of these principles is still low. "l"hey also men- [ ~ob~. k___Jr_____~ o,,~,~ I finn a critical factor in decision support , viz. the L l ~ t___ J

of problem solving heuristics. " ..... ~ ____l____ " / '~ ..... presence A second consideration deals with the relevance \ ' - , , [ ~ [ / /

of AI research to DSS. Stamper (1984) remarks: t J r

'Our growing technical capacity to produce, store and distribute information is no guarantee ol the ...... ~ - ¢ ~ i ~ . . - - information's organisational and social usefulness. - - / / . " - . . , " ' ~ The trend towards i~aelligent, knowledge-based f . . < \ . / ' / ~._~ --.~_.. systems ca:mot solve the problem; instead it could , ~ t ; ...... / well make the problem ,:orse by di.~guising poor .,.. / ~ , ._ . j ' \ _../ information under a cloak of logical self-con-

Figure 1. Extended hamework of the o.e presented by Sprague sistency', a.d Bonc~'ek

Although DSS may provide a link on Ihe palh from traditional information processing t,.~ward:; l, newledge engineerir, g. we may recall that expert solving in a knowledge-based framework. There- systems are always based on historic;,/ expertise, fore 1 extended the frameworks pre.,;t:ns.ed by The search for expertise should not detract atten- Si~rague and Bonczek into a new one, see Fig,.:re I. tion from grasping creativity-processes in new. un- ~ prooose to direc! DSS-researcl: te ~t~e c,m;,.:ept experienced problem situati~ms, of DSS generators or, mort-generally, IASS desigL~

emironments . One of the main ceasons fi'r thi.~ choice is the lack of gencralizability in dealing

5. DSS: A redefinition wi~h specific decisiop support systems. Another :eason is if.at one has to address all stages in :he

Although the interest for DSS shotfld be prc-cess of problem solving, not only at the era- welcomed, a clearer delineation of the concept of pirical, problem-dependent level but also at ihe DSS is needed in order to make it a potenhatly conceptual level. rich research ~rack. l¢dch, in the sense ti:at it can Especially for the solution of ill-structured foster the effectiveness and efficiency of organisa- problems, the ~:ho~ce ol a Weltanschauun? and ; tional deci.,,ion-making. Keen has questioned tbe construct paradigm or modelcyctc, as point oi role of modelling and quantitative models in departure for the activities of problem concepIu- stimulating creative thinking. If the OR discipline alizatkm and problem specification, are closely is taking up the DSS line, it should pick up this re!ated. The expres.,don ef a methodology arid :: challenge and focus on creative dccNio~.-making ti~eory, in viev. of a p r o b e m under consideration. and learning on the merge of MIS anti ¢e-l!ows th, esc choi:es closely. ! put forw,trd the O R / M a n a g e m e n t Scienct:. idea that i~ is possible t:, combine a Weltar, schat:-

One line is to focus on heurist;cs from an AI ung with a construct para;.lig~a b ; giving die con- persp,zctive. Another mei~ing p~int could be mode! ccpts of a D..SS generator, a lar.guage system, ,t management and model representation. However, knowledge system and a pr~blem processing svs- i would prefer to take up the process ot problem tern a concrete ft, r.-n in the notio~, of an Inquiry

Page 6: DSS: Buzzword or OR challenge?

6 11.(3. Sol / DSS: Buzz~t'ordot OR challenge

System. ! define an inquiry .,.ystem as a structured in a system; I introduce the notion of an Entity as set of instrumenl~ which can be used as a context an identifiable set of associated Attributes. An or model!ng environment in the probkm-soiving entity may portray behaviour by applying one or activities. It expresses a meth~xtology in view of a more transformation rules to change the values of problem r rea. some of its auributes and by interactions with

The inquiry system serves in the first instance other entities. I define in a Scenario related to an as a 'context for conceptualization'. It presents in entity the possible transformation rules and the its languag:~ system and knowledge base the build- possible interaction paths of an entity, as well as ing bloci, s for the creation of a system description, the conditions under which these can be actual- Subsequt:ntly, lhe ir.quiry system appears as a ized. This can be done in various modes. One coacept~Jal mode! for the problem specification, in mode is the 'declarative' mode, as e.g., encoun- an empilical m'cdei for the solution finding and in feted in interpreted predicate logic, see Bubenko a target system for the implementation. (1982), Sernadas (1982). Another mode is the 'pro-

The notion of an inquiry system has several cedural' mode, as we know this style from a gre~t important contributions, many procedural programming languages. It is

1. By a tranglation of a Weltanschauung and a sometimes suggested that these modes are their construc: paradigm into a context for conceptuali- antagonists, 1 prefer to see them as complemen- zation one can discuss the premises behind theory tar)'. Depending on the application domain and formulat om Through its application in tl~e con- the users involved, one might prefer one of the struction of a conceptual model and a model sys- modes tern, a th=ory, comes about. In combining within an entitity a data part and

2. -fae products of the various activities in the an action part, I come close to the concep! of proccs:; of problem solving a.,e building on each object representation in object-oriented languages, other i s successive layers. This allows for an easy see, e.g., Cox (1984). The notions of an entity and adjusuaeat of individual theories and orgaafisa- a scenario enable us to "open the black box'-ol an tional theories and for an evohmonary develop- individual decision-maker and specify the concepts ment of a target system, anti rules applied. We are able to specify their own

3. 2"he modeling environments in the inquiry language system, knowledge system and probiem system enable a flexible support o|" all phases in processing system. As to the constntction cf a 1he pr ~ces.; of problem solving. Problem finding conceptual model of an individual decision-maker, can get at least as .much attention as solution we have to describe how scenario's of entities v:ith finding, their attributes are actualized. We may specify

For the constracdon of conceptual models and various phychological types of a decision maker in empirical modcls c.ne needs a language system a muitidisciplinary way by introducing entities which does not restrict the capabilities of inquirers with corresponding attributes and scenario's tor and decision mak~)rs in t~sing knowledge to make processing data and making decisions, in a scenario models and to create evidence. I make a distinc- for an individual we may even describe the changes tion between on the. (3ne hand a description form in the data input mode and decision making dur- using equations and on the other hand process or ing the process of problem solving. We observe rule-based models. The Equation Models are fre- that scenario's for portraying behaviour may bring qu,:ntly e:tcountered in DSS for strategic and about transformation rules not previously thought organisational problems, especial!y in corporate or of. as well as attributes and interaction paths financial models, h~ these models one has to de- involved. We may have to describe that a decision ,~'ine functional relauoaships in terms of definition maker has a local scope: As an entity he only deals equati~ns or behaviou~'al equation:;. Although these with a specific set of attributes and specific may be specified in a non-procedural way as in access-paths to other entities. He only observes a several DSS generators, they still appl) to the 'representative state' of an entity, i.e. those attri- outside of a phenomenon, seen as a black box. butes and that part of the scenario which gave

In the Process or Rule-base~ models one does meaning to him. However, this representativeness not try to summarize a process in equation form. may change dynamically during the problem-solv- Instead. one tries to describe the sequence ,)f events ing process.

Page 7: DSS: Buzzword or OR challenge?

H.G. Sol / DSS: Bttzzword or OR ¢halh,,~Ee 7

To summarize, through process specifications of This personal outline of a research effort ,nat.,' objects we can look into the dynamics of challenge people from MIS, AI and, not in th. ~ decision-making processes and the resulting be- least, from OR. OR does have a body of knowL haviour. In this way we amy create evidence on edge in applying a simulation-based apprc, ach f~r problem-solving processes m specific situations improving knowledg,: and understanding of deci. using strategic and actual knowledge in the proces sion situations, it is the opportunity to shift at te:- specifications, finn from the stage of problem solving, to the

The inquiry systems mentioned support the stages of problem identification, formulation an~,! process ol problem solving, firstly by creating an implemt;.atation. cpistemological specification of processes in an empirical situation. This specification is based oil a conceptual model forrn,~lated against the back- References ground of knowledge of an application domain. Alter, S.L. (1980), Decisiot, Support S)'stem.~." Current Pruc t ,~ , Subsequently, the inquiry system facilitates the and Continumg Challenges. Addison-Wesley. Reading

Bennett, J.L. (ed.) 11983). Building Deci:ion Support .~;|'~:e,';s:,. generation of altecnatives and the choice of a Addison-Wesley, New York.

solution. By keeping track of the subsequent steps Bergquist, J.W.. and McLean, E.R. (1983). "Integrated dzt, in the process of problem solving we m~y acquire analysis and management systems: An APL-based decisi.)~, knowledge, not only on the actual problem situa- support system", in; H.G. Sol, (ed.), Proce~sesat:d Tools~o," tions, but also perhaps for future situations. The Decision Support, North-l-lolland, Amsterdam. inquiry sysJ, em can be seen as a problem solving Bonczek, R.H.. Holsappk', C.W., and Whin~ton. A.B. 11981).

Foundations of Decisio,.J Support Systems, Academic Prt.s:, environment. When it is supl:.orted on a computer, New York. we are dealing with a DSS eztvironmem. How can Bonczek, R.H., Holsappie. C.W., and Whinstor.. A.B. (198LP. we apply these environments to create suppor~.ing "'Specification of m.~deling and knowledge in decision sup- inquiry systems for individual decision-makers: port systems", in: H.G. Sol, (ed.). Processe.~ and Tool" ;'or

Decision Support, North-Holland. Amsterdam. The construction of the descriptive understand-

Bosman, A. (1983). "Decision support ~,y.~tems: Probkm ing model and the prescriptive target solutions can processing and coerdination," in: H.G. Sol (ed.). Protes . . '

be facilitated by a design environment in which a~zd Tools for EectsionSup!,ort North-Holland, Aknsterda~. various expert syslems and knowledge bases have Bubetlko, J.A. et al. (1982). "CLAM", in: T.W. Olle. H.(;. 5;,,I their place. The process of producing the epistemo- ar, d A.A. Verrijn Stuart leds.), lnfi~rmation Systems Design

,~,lethodologtes." A Coml,aratu,e Ret.ww. North-Hollaz+d. logical representation can be facilitated by an ex- Amsterdam. pert system w i t h a knowledge base characteristic Burch, J.C.. and Strater. F.R. (1074). Jn]ormotum Sv.~t¢'m.~. for the application domain. Theory and Practice. I lamilton. Sant'~ Barbara.

Further expert systems with a different knowi- Cats-Baril. W.L., and Huber G.P. ~1984,~. "'DSS for all-strut. edge-base, to support the process of problem solv- tured problems: A cognitive approach and an empiri,:al

study". Proceedings of the IFIP 8. 3. Working Conferent e on ing are dedicated at Knowledge Representation [or De~'iston Support Systems. - verification and validation of the descriptive IS'urham.

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