Expert systems are typically built to model individual expertise, e.g., a doctor, a travel agent, an automechanic. The view, generally, is of an independently operating problem solver.
Maczgers dcYi1t q p e s r to be e>:>eris in this same sense. Jv!intzSerg (1973), in an empiricel study of the activities of high leire! e:recutives:
notes that a great pol-tion of marlagenial activity is spent in c o m m ~ ~ i c e - tion, observation and diitn gathering. K<oreover, some 70% of their time is spent in informa.! meetings and committees. Indeed, in this s a m ~ l e , managers 01117 spent about 22% of thzir time in isolated concentration.
The s ~ g e s t i o z here is that m a a g e r s , rather then possessing an indicidu- aljzed expertise, are more like specialized nodes in a larger 'organizn- tional cogniticn'. Organizntions in turn, react and pcrticipate in a larger 'social cognition' in their attempts to mzrket nev; products and/or nove!
services.
An important part of the manager's activity is to observe and under- stand changes and trends in the marlcet, the economic, legal and sociel environments. Much of this Is not simply s h f t s in magnitlude on pre- defined dimensional szales. (Were this so, mathernnticz! models would surely have a bigger impact on manzgerial practice.) Instead, mznagerid cognition often involves the modification of primitive concepts. For instance, the range of phenomena we call an 'automobile' changes from year to year. Each competitive innovation, each new marketing angle.
each special interest group expands and re-organizes the phenomena the manager includes in his/her conceptual framework. And, given t h a t his/her contact with the world is primarily through linguistic interac- tions, the semantics of organizational language is constantly shifting.
Because mechanical inference relies on a stable, fixed semantics, the utility of an idealized, fully integrated, knowledge-based inference system will be limited to organizations in completely stable environments.
Similar criticisms can be made of bureaucratic rationalizati~n (Loe 19E3).
The c o ~ c l u s i o n to be d r a v ~ r ? is that integrated information systems will only be of use for those aspects of the or~an;zation's activities where semantic stability can be mzintained. This conciusion corresponds to the empirical observations rilade by Gorry and Scott-KIortor? (1971), which 1 ~ d to the conception of 'decision s u p p r t systems' (e.g., Keen and Scott- Morton (1978), Bonczek et a:. (lSEi), Fick aad S p r a g ~ e (19SOj, So1 (1993).
The underlying idea in the DSS work is to promote t h e dsvelo2ment of technology which, raiher t h . n r e p l ~ c e humzn cognition, sseks to assist and augment it. The trend seems to be to~;ards developing DSS 'genera- tors' which provide computational building blocks v2~ich can be variously structured for different ad-hoc, decision situations.
Interestingly, despite the iiedely recognized importance cf group decision making, nearly all CSS packages are oriented t o ~ r a r d s assisting the individual manager in isolation. The e x ~ l a n a t i o x may be semantic:
an individual can assign an interpretation to a particular syntactic representation (s)he invents. In a group setting however, the semantics is n e g o t i a t e d , and our technology so far seems to have had little effect on these socio-linguistic processes.
SUMMARYREMARKS
The preceding arguments can be summarized i n t h e following state- ment: we make words m e a n what w e want. Three aspects a r e emphasized.
Semantizs is plestic. A s Tarskian model theory so bluntly poilits out, the semzatics cf e lznguage is ar- inierpretation assigned to it. Certain truths (logical truth) are t a u t o l o g ~ u s i s that they h.olZ under any interpretation (true in all possijre m ~ < e l s ) . In organizational app!ications, ho~vever, we are more concerned with specific interpretations (synthetic truths, true in scjrne models, no: true in others). The validity of the inferences drawn depends on the stability of this interpretation. For example,
LEKON (x) 4
YELLOW(x)
is true if in fact all lemons zre yello?~.~, but fails if some botmist succeeds in g e n e r z t i q a strain with different colors an6 declares that they, too, are lemol-is.
We mkke words meari what we want.
Semantic change has a pragmatic component, depending on t h e interests, preferences and values of its users.
We make words mean what we want.
Semantics is plastic, pragmatic, but also the product of social consensus.
Indeed, i t is not only socially determined, but socially understood.
POST SC,R3"
The pw-pose of- this p q e r has beer, mainly to elaborste a p r o b l e : ~ rather than proaose spszific solutions. The point certainly hzs n o t besn to discourage furth2r A! research. Rather, it may serve to explain some of the frustratioa felt in meny of attempts a t kno~vledge representation, particularly in mar~sgerial applications. As we suggest here, the p r o b k m may be overif~helmiaglj~ difficult, requiring dtimately a forEal explicztion of all of s o c k t y . If that is the case, we ~ ~ o u l d do ~l;ell to seek out more achievable goals and strztegies.
Likewise, vre have to be careful not to overstate our claims. -4s pointed out in the beginning, A1 is getting market appeal. Big mccey is shifting. But the people b e h n d those big deeisions aren't techniciens r,or theoreticians. They a r e n ' t accustomed to our tendency to e x t r q o l a t e world shaking impl',caticns from toy-sized implementetions. They may actually believe us. And the plans for 1 9 8 4 are in the making now.
ACKNOKWDGEfEWTS
The author gratefully acknowledges the stimulating interactions with Steven Kimbrough, Eckehart Kohler and Amilcar Sernadas on these topics. As well, Werner Shimanovich and other members of the 'Vienna Quadrangle' (our humble remake of the Vienna Circle) provided a general background of discussion linking artificial intelligence and formal philoso- phy.
Asimov, I. 1978. I, R o b o t . Kew York: Fawcett.
Bonczek, R.H., C.MT. Hoisapp!e 2nd A.B. IYhnston. i961. F o u z d c t i o n s of D e c i s i o n S u p p o r t S y s t e m . New York: Academic Press .
Boulding, K.E. 1978. S t a b l ~ P e a c e . Austin: University of Texas Press.
Burns, T., and G.M. Stalker. 1961. t h ~ Idanagenzent of I n n o v a t i o n . Lon- don: Tavistock.
Clocksin, W.F. and C.S. Mellish. 19Bi. P r o g r a m m i n g in Prolog. New York:
Springer-Verlag.
Coelho, H., J.C. Cotta and L.M. Pereira. 1980. H o w t o S o l v e I t W i t h Prolog.
2nd Edition. Lisbon: Laboratdrio Nacional de Engenharia Civil.
Cyert, R.M. and J.G. March. 1963. A B e h a v i o r a l T h e o r y of t h e f i r m . Englewood Cliffs, New Jersey: Prentice-Hall.
Davis, R., and J. King. 1975. An Overview of Production Systems. Stan- ford
AI
Lab Memo AIM-271, Stanford Computer Science Report.STAN-CS-75-524. Stanford, California.
Deal, T.E. and Kennedy A.A. 1982. C o r p o r a t e C u l t u r e s . Reading, Mas- sachusetts: Addison-Wesley.
Dowty, D.R., R.W. Wall, and S. Peters. 1981. I n t r o d u c t i o n t o M o n t a g u e S e m a n t i c s . Boston: Reidel.
Fick, G . and R.H. Sprague, Jr. e2s. 1980. Dac.lsi3n S u ~ ; ~ o r f Systen-&s:
Issues and Ch~Llenaes. Oxford: Pergainon Press.
Galbraith, J. 1973. D e s i ~ r ~ i n g CcrnpLzz O T ~ u z i z a t i ~ n s . ReaCing, 'n'ass. : Addison-Wesley P u b l i c e t i ~ z s .
Galbraith, J. 1977. Qrganizatio:~ Design. Reading, Kassachusettj::
Addison-Mresley, lnformation Systems. SLom. Kanaj,oment Revisw, 13(1):55-70.
Hilpinen, R. 1961a. ed. Deor~tic Logic: I n t r c d u c f o r ? ~ and S y s t e m a t i c
Kent, W. 1978. Data and Reality. Amsterdam: North-Holland.
Kowalski, R. 1979a. Algorithm = Logic
+
Control. Communications of t h e Natural Kinds, London: Cornell University Press.Kripke, S.A. 1972. Naming and Necessity. In Davidson and Harman, eds., S e m a n t i c s of Natural Language pp.253-355.
Lee, R.M. 1980. Analyzing Red Tape: The Performative vs Informative Roles of Bureaucratic Documents. WP-80-164. Laxenburg, Austria:
International Institute for Applied Systems Analysis.
Lee,
R.M.
1981. CANDID Description of Commercial and Financial Con- cepts: A Formal Semantics Approach to Knowledge Representation.WP-81-162. Laxenburg, Austria: Interns.iioz&! Institute for Ap2lied Systems Ana!ysis
Lee, R . X . 1923. Eureaucracies, Eilreaucrkts and Info~ination Technology.
VP-e3-20. Laxenburg, Anstria: International Institute for Applies Systems Analysis.
March, J.G, and J.P. Olsen. 1979. Ar~bigzi-lty rznd Ciioice in OrganizatCons.
2. Edition. Bergen: I/'niversitetsforle.get.
March, J.G. an$ H.A. Simon. 1958. &ganieatim-ci. Nevi York: Y(i1lep.
McGregor, D. 1980. The - W i m s n S i d e of EnL'~rp7-ie. Kevi York: KcC-:-a?&:-
Peters, T.J. 1930. Xanagement Systems: The Lznguege of Organizations!
Character and Coinpetence. Organizstignal D j n a m i z s . Summer. IF'IP/IIASA Working Conference on Processes and Tools for Decision Support, July 19-21, 1982, Laxenburg, Austria. Amsterdam: North- Holland.
Strawson, P.F. 1959. I n d i v i d u a l s . Garden City, New York: Anchor Books.
Tversky, A. and D. Kahneman. 1974. Judgment under Uncertainty:
Heuristics and Biases. Science, 185(September): 1124-1 131.
van Fraassen, B.C. 1971. Form .EL S~n-iir,~~tins and Logic. New York: Y L C - millac.
von Kright,
G.H.
1068. Ari Essay in DeoEtic Logic and t h e General T'neol-y of Action. k c t r Pfililoscphiz~ F m n i r ~ , Fas:. XCI, Amsiwdem: Korth- Holland.Weber,
K..
1956/ 1578. E c o n ~ m y and Socicfy. Berkeley, CaliforrLa:University of California Press, transiated from nFi-tscha;t u 7 ~ 3 Gesellschaft, Tuebingen: J.C.B. Mo-b, i 956.
Wiener,
N.
1967. Htmcn Use o; h-umcz. B~ingrs: C y b e r n ~ t i z s a n d S o c i ~ t ~ New York: Avcn Books.Wittgenstein, L. 1953/1953. Philoss?lzcZsal J m u e s t i ~ ~ t i n : z s . Trmslzted b y G.E.M. Anscornbe. Third E d i t i ~ n . New York: Xacmilian.