Advanced Decision-Oriented Software for the Management of Hazardous Substances: Part III - Decision Support and Expert Systems: Uses and Users

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NOT FOR QUOTATION WITHOUT PERMISSION OF THE AUTHOR

ADVANCED DECISION-ORIENTED SOFTWARE FOR THE MANAGEMENT OF HAZARDOUS SUBSTANCES P a r t

IKI

D e c i s i o n S u p p o r t and E x p e r t Systems:

U s e s and U s e r s

K u r t F e d r a , IIASA H a r r y Otway, JRC

April 1986 CP-86-14

CoLLaborative P a p e r s r e p o r t work which h a s not been performed solely at t h e International Institute f o r Applied Systems Analysis and which h a s r e c e i v e d only limited review. Views o r opinions e x p r e s s e d h e r e i n d o not necessarily r e p r e s e n t t h o s e of t h e Insti- t u t e , i t s National Member Organizations, o r o t h e r organizations supporting t h e work.

INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS 2361 Laxenburg, Austria

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ACKNOWLEDGEMENTS

The r e s e a r c h d e s c r i b e d in t h i s r e p o r t is sponsored by t h e Commission of t h e European Communities' (CEC) Joint R e s e a r c h C e n t r e (JRC), I s p r a Establishment, u n d e r Study Contracts No.2524-84-11 ED ISP A and No.2748- 85-07 ED ISP A. I t is being c a r r i e d out by IIASA's Advanced Computer Applications (ACA) p r o j e c t , within t h e framework of t h e CEC/JRC Industrial Risk Programme, and in cooperation with t h e Centre's activities o n t h e Management of Industrial Risk.

This p a p e r w a s originally p r e p a r e d as a background p a p e r f o r a Task F o r c e meeting wnich w a s convened t o discuss t h e implications of computer- based e x p e r t systems for decision s u p p o r t in t h i s application area and t o e x p l o r e t h e i r potential u s e s in decision making at various levels.

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THE AUTHORS

D r . K u r t F e d r a i s t h e P r o j e c t L e a d e r of t h e Advanced Computer Appli- cations g r o u p a t IIASA

Dr. H a r r y Otway i s t h e Head of t h e Technology Assessment S e c t o r of t h e Joint R e s e a r c h C e n t r e , Commission of t h e European Communities, I s p r a Establishment, I s p r a (Varese), Italy.

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CONTENTS

1. P r o j e c t Summary Description 1.1 Background

1 . 2 An Integrated Software System 2. Introduction: Framework and Approach 2.1 The U s e r : a Tentative P r o f i l e

2.2 E x p e r t Systems a n d Model-based Decision S u p p o r t 2.3 Information Requirements for Decision S u p p o r t

3. The Problem Area: Management of Hazardous Substances 3.1 The Systems View: Comprehensive Assessment

3.2 Information Management and Decision S u p p o r t 3.3 Model Integration a n d User I n t e r f a c e

3.4 Data Bases, Simulation, and Optimization 3.5 Embedded E x p e r t Systems Technology 4. Application Areas and Modes of Use 4.1 A Tentative List of Problem Areas 5. Implications and Problems

5.1 The Economic Potential 5 . 2 Availability of Information

5 . 3 Knowledge Acquisition: a Bottleneck f o r Development 5.4 The Hazards of Using a Hazard Management E x p e r t System 6. R e f e r e n c e s

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ADVANCED DECISION-ORIENTED SOFTWARE FOR THE MANAGEMJZNT OF HAZARDOUS SUBSTANCES

P a r t

III

D e c i s i o n Support and E x p e r t Systems:

U s e s and U s e r s

K u r t F e d r a (IIASA) a n d H a r r y Otway (JRC)

1. PROJECT SUMMARY DESCRIPTION

1.1

B a c k g r o u n d

Many i n d u s t r i a l p r o c e s s e s , p r o d u c t s , a n d r e s i d u a l s such as hazardous and t o x i c s u b s t a n c e s , pose r i s k s to man a n d are harmful t o t h e basic life- s u p p o r t system of t h e environment. In o r d e r

to

r e d u c e r i s k s t o individuals and s o c i e t y a s a whole, and to e n s u r e

a

sustainable u s e of t h e b i o s p h e r e f o r p r e s e n t a n d f u t u r e generations, i t i s imperative t h a t t h e s e s u b s t a n c e s are managed in a scde a n d s y s t e m a t i c manner.

The aim of t h i s p r o j e c t i s to p r o v i d e software tools which c a n b e used by t h o s e engaged in t h e management of t h e environment, industrial production, p r o d u c t s , and waste s t r e a m s , and hazardous s u b s t a n c e s a n d wastes in p a r t i c u l a r . This

set

of tools i s designed for a b r o a d g r o u p of u s e r s , including non-technical u s e r s . I t s p r i m a r y p u r p o s e is to improve t h e f a c t u a l b a s i s f o r decision making, a n d to s t r u c t u r e t h e decision-making p r o c e s s in o r d e r to make i t more consistent, by providing e a s y access and allowing efficient u s e of methods of analysis and information management which are normally

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r e s t r i c t e d t o a small g r o u p of t e c h n i c a l e x p e r t s .

In o r d e r t o design a n d develop a n i n t e g r a t e d s e t of s o f t w a r e t o o l s , w e build o n existing models a n d computer-assisted p r o c e d u r e s . F o r t h e c a s u a l u s e r , a n d f o r more e x p e r i m e n t a l a n d e x p l o r a t i v e u s e , i t a l s o a p p e a r s neces- s a r y t o build much of t h e accumulated knowledge of t h e s u b j e c t areas i n t o t h e u s e r i n t e r f a c e f o r t h e models. Thus, t h e i n t e r f a c e h a s t o i n c o r p o r a t e elements of knowledge-based or expert systems t h a t are c a p a b l e of assisting any non-expert user t o s e l e c t , set up, run. a n d i n t e r p r e t s p e - cialized s o f t w a r e . By providing a c o h e r e n t u s e r i n t e r f a c e , t h e i n t e r a c t i o n s between d i f f e r e n t models, t h e i r d a t a b a s e s , a n d a u x i l i a r y s o f t w a r e f o r display a n d a n a l y s i s become t r a n s p a r e n t f o r t h e u s e r , a n d a more e x p e r i - mental, educational s t y l e of c o m p u t e r u s e c a n b e s u p p o r t e d . This g r e a t l y f a c i l i t a t e s t h e design a n d analysis of a l t e r n a t i v e policies f o r t h e management of i n d u s t r i a l r i s k .

An i m p o r t a n t element in t h e o v e r a l l c o n c e p t i s t h e d i r e c t coupling of l a r g e d a t a b a s e s of s c i e n t i f i c a n d t e c h n i c a l information with human e x p e r - t i s e , of formal algorithmic methods a n d models with h e u r i s t i c s a n d human judgement. The e x p e r t - s y s t e m s a p p r o a c h n o t only aliows d i r e c t a n d i n t e r a c t i v e u s e of t h e c o m p u t e r , i t i s designed as a tightly coupled man- machine system w h e r e t h e vastly d i f f e r e n t d a t a handling, analysis a n d judgement c a p a b i l i t i e s of man a n d c o m p u t e r are i n t e g r a t e d i n t o o n e c o h e r e n t framework. F o r a f u l l e r t r e a t m e n t of s t r u c t u r e a n d design, a n d t h e implementation of a demonstration p r o t o t y p e , see F e d r a (1985, 1986).

1.2 An Integrated Software System

The model-based decision s u p p o r t system discussed h e r e combines s e v e r a l methods of a p p l i e d systems a n a l y s i s a n d o p e r a t i o n s r e s e a r c h , planning a n d policy s c i e n c e s , a n d a r t i f i c i a l intelligence (AI) i n t o o n e fully i n t e g r a t e d s o f t w a r e system (Figure 1.1). A demonstration p r o t o t y p e system c a l l e d IRIMS ( I s p r a Risk Management S u p p o r t System) h a s b e e n developed in t h e framework of a c o l l a b o r a t i o n between t h e Joint R e s e a r c h C e n t r e of t h e Commission of t h e E u r o p e a n Communities ( I s p r a , Italy) a n d IIASAJs Advanced Computer Applications p r o j e c t .

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DIALOG

-

MENU SYnEM

SYMBOLIC /GRAPHICAL DISPLAY SYnEM

SCEYLLBfOlUlYYSIS

cONTxT/PBnm

wmn~us~sm smuunm

rn

PRODUCTION SY-

F i g u r e 1.1: Elements of t h e i n t e g r a t e d soj'tware s y s t e m

Conceptually, t h e main elements of such a model-based decision s u p p o r t system are:

an

Intelligent User Interface. which provides e a s y a c c e s s t o t h e system. This i n t e r f a c e must b e a t t r a c t i v e , easy t o understand and use, e r r o r - c o r r e c t i n g and self-teaching, a n d provide t h e translation between n a t u r a l language a n d human s t y l e of thinking t o t h e machine level and back. This i n t e r f a c e must a l s o p r o v i d e a largely menu-driven conversational guide t o t h e system's usage (dialog

-

menu system), a n d a number of display and r e p o r t generation styles, including c o l o r g r a p h i c s and linguistic i n t e r p r e t a t i o n of numerical d a t a (symbolic/graphical display system);

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an Information System. which includes Knowledge and Data Bases (KB, DB), Inference Machine and Data Base Management Systems (IM, DBMS).

I t summarizes information on application and impiementation and contains t h e most useful domain-specific knowledge.

the S i m u l a t i o n System. which consists of a set of r e l a t e d models (simulation, optimization) which d e s c r i b e individual p r o c e s s e s , perform r i s k and sensitivity analyses on t h e relationship between c o n t r o l and management options and c r i t e r i a f o r evaluation, o r optimize plans and policies. given information about t h e u s e r ' s goals and p r e f e r e n c e s , and r u l e s f o r evaluation.

the D e c i s i o n S u p p o r t System, which a s s i s t s in t h e i n t e r p r e t a t i o n and multi-objective evaluation of modeling r e s u l t s , and provides tools f o r t h e selection of optimal a l t e r n a t i v e s with interactively defined p r e f e r - e n c e s and aspirations.

A t t h i s point, i t s e e m s a p p r o p r i a t e t o caution against excessive technologi- c a l optimism. Computers alone are not going t o solve anything, and in f a c t , much c a n b e said against t h e i r all t o o intimate involvement in human a f f a i r s (Weizenbaum, 1976). However, t h e expanding technology of e x p e r t systems could provide a common language and a framework f o r multidisciplinary cooperation, and stimulate new a p p r o a c h e s t o t h e solution of both old and new problems.

2. INTRODUCTION: FRAMEXORK

AND

APPROACH

Whether t h e y a p p e a r as r a w materials, as finished products, as by- products, o r as wastes, hazardous substances pose r i s k s t o man and t h e environment which must b e responsibly managed. Recent accidents have dramatically demonstrated t h e need f o r not only b e t t e r r i s k management, b u t also f o r b e t t e r , and more comprehensive, management of information

(e.g., Hay, 1982; Saxena, 1983; Otway and Peltu, 1985).

The regulatory framework f o r hazardous substances within t h e Euro- pean Community is largely defined by a number of Directives of t h e Council of t h e European Communities and t h e corresponding national legislation which t h e s e Directives r e q u i r e (see, e.g., Haigh, 1984; Majone, 1985; Baram,

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1985). For example, t h e so-called Seveso Directive (Council Directive on t h e major accident hazards of c e r t a i n industrial activities, 82/501/EEC) specifies t h a t manufacturers must provide t h e competent authorities with information on t h e details of substances and processes involved in high-risk facilities. F u r t h e r , people outside t h e establishment who might b e affected by a major accident must b e informed of t h e safety measures

to

b e taken in t h e event of a n emergency.

The Council Directive on toxic and dangerous wastes (78/319/EEC) calls f o r a comprehensive system of monitoring and supervision of facilities and operations involving hazardous wastes, specifically mentioning r i s k s t o water, a i r , soil, plants and animals, while also including nuisance due t o noise and odors and possible degradation of countryside and places of special interest. More recently, t h e Directive on t h e assessment of t h e effects of c e r t a i n public and p r i v a t e p r o j e c t s on t h e environment (85/337/EEC, June 1985) r e q u i r e s comprehensive environmental assessments of p r o j e c t s and installations involving hazardous materials. These assessments are t o include consideration of t h e production and s t o r a g e of materials such as pesticides, pharmaceuticals, paints, etc. A broad analysis of t h e d i r e c t and indirect effects on people, environment, p r o p e r t y and cultural h e r i t a g e i s also foreseen and t h e evaluation of alternatives is required.

A s systems containing hazardous substances have become m o r e techni- cally complex, i t has increased t h e importance of systems interactions and t h e need t o evaluate policy alternatives, and this is reflected in r e c e n t legislation. Paradoxically, however, t h e decreasing cost of computing power seen in t h e past f e w y e a r s has been exploited more f o r t h e creation of sophisticated models of technical subsystems (Vesely et al., 1981; ICE, 1985) r a t h e r than f o r t h e development of overall systems models t h a t

treat

t h e a s p e c t s most relevant t o r i s k management: t h e interactions and trade- offs amongst production, environmental dispersion, use, transportation, and ultimate disposal.

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2.1. The User: A T e n t a t i v e Profile

The software system discussed h e r e is designed f o r a broad and diverse group of u s e r s , with various backgrounds, and different d e g r e e s of involvement with t h e decision problem at hand. This group also includes non- technical u s e r s , who may b e e x p e r t s in one domain or t h e o t h e r of t h e prob- l e m situation, o r may b e directly concerned, but not have formal training in engineering, toxicology, environmental sciences, systems, r i s k o r decision analysis, or computer sciences. Given t h e broad scope of t h e problems addressed by t h e system, and t h e l a r g e number of scientific, technical, economic, and administrative elements involved, w e can safely assume t h a t no likely u s e r or groups of u s e r s can possibly have sufficient e x p e r t i s e in all t h e areas of concern.

So far, w e have r e f e r r e d

to

t h e 'user' of o u r system in a n a b s t r a c t sense. However, i t i s important to add some substance to t h e term, to make t h e 'user' someone w e c a n more readily conceptualize and cater to as t h e p r o j e c t progresses. The following t h r e e speculative questions relating to use are proposed t o stimulate discussion and to g e n e r a t e f u r t h e r questions:

Who a r e the Likely users? The n a t u r e of o u r system, focusing on interactions and trade-offs r a t h e r than detailed subsystem behavior, means t h a t i t would b e more suitable for s t r a t e g i c planning than for technical design. Consideration of organizational goals (Otway and Ravetz, 1984), suggests use by a regulatory agency o r regional planning authority, especially in view of t h e i r typical r e s o u r c e constraints and t h e problem of main- taining technical competence vis-a-vis industry. Industry seems less likely to b e interested at t h e outset, although a system used by regulatory authorities would b e likely to attract t h e attention of industry as w e l l . Could i t b e used to help develop and evaluate emergency plans? If so, by whom? Could i t play a r o l e in post-accident emergency management? I s t h e r e a potential application for meeting l a w s t h a t r e q u i r e industry to inform t h e public of t h e r i s k s t o which they are exposed and what actions t o t a k e in a n emergency?

What f e a t u r e s w i l l u s e r s want? I t i s generally a g r e e d t h a t u s e r acceptance of management information systems depends upon full u s e r participation in planning and design processes (Keen, 1985). This s o r t of system

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being relatively new, and potential u s e r organizations relatively conserva- tive, a u s e r c a n probably not b e found until t h e r e i s a completed prototype system t o demonstrate. This "chicken-and-egg" situation makes i t necessary t o seek participation of s u r r o g a t e u s e r s t o anticipate, as far a s possible, t h e needs of "real users".

A regulatory agency using t h e system would want enough transparency t o defend decisions t o public groups, industrial interests, and politicians.

They would a l s o want a t least qualitative treatment of socio-political issues, such as employment implications of alternatives. Information on t h e economic implications of regulatory policies f o r industry would also b e necessary f o r regulatory use, especially since industry often advances economic arguments t o oppose regulations. In t h e c a s e of industrial s t r a - tegic planning, a thorough analysis of trade-off costs would also b e required. What o t h e r f e a t u r e s would u s e r s r e q u i r e ?

What a r e t h e i m p l i c a t i o n s of' swstem use? There

are at

least f o u r paradigms of decision making and counsel whose implications f o r interface design should b e explored: t h e artificial intelligence paradigm, t h e decision analysis paradigm, t h e operations r e s e a r c h paradigm, and t h e 'cognitive styles' paradigm. For example, e x p e r t systems a r e based on t h e assumption t h a t formal knowledge can b e supplemented by knowledge elicited from e x p e r t s as a n inferential basis f o r decision making. This implies t h a t e x p e r t s are able t o impart t h e i r e x p e r t i s e and, f u r t h e r , t h a t they can make rational use of i t

-

a n assumption t h a t t h e existing world, t o some extent shaped by e x p e r t knowledge, is basically all right. Decision models, in con-

trast,

usually assume t h a t human beings, including e x p e r t s , are r a t h e r more limited in t h e i r abilities t o make rational decisions o r ,

at

least, t h a t decisions would b e b e t t e r if they w e r e more formally s t r u c t u r e d .

There is also a dichotomy between t h e traditional operations r e s e a r c h belief t h a t t h e adviser must b e as close as possible t o t h e "problem holder"

if his e x p e r t i s e is t o be of use and t h a t of t h e e x p e r t systems paradigm. The e x p e r t system tacitly assumes t h a t t h e e x p e r t adviser does not need t o b e in d i r e c t contact with t h e policy maker, but t h a t his expertise can be summarized, condensed and drawn upon when required (Figure 2.1). These, and o t h e r , decision and e x p e r t advice paradigms should b e examined, if t h e full

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ECI

SION SUPPORT

SY

W E R T SYSTEM

Figure 2.1: The roles of d e c i s i o n makers, s p e c i a l i s t s a n d the computer:

D S v e r s u s expert s y s t e m s p a r a d i g m .

consequences of system use a r e t o b e understood.

2.2 Expert Systems and Model-based Decision Support

Underlying t h e concept of decision s u p p o r t systems in general, and e x p e r t systems in p a r t i c u l a r , i s t h e recognition t h a t t h e r e i s a c l a s s of (decision) problem situations, t h a t are not w e l l understood by t h e people involved. Such problems cannot b e p r o p e r l y solved by a single systems analysis e f f o r t o r a highly s t r u c t u r e d computerized decision aid (Fick and Sprague, 1980). They are n e i t h e r unique

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s o t h a t a one-shot e f f o r t would

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b e justified given t h e problem is big enough

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nor do they r e c u r frequently enough in sufficient similarity t o subject them t o rigid mathematical

treat-

ment. They are somewhere in between. Due t o t h e mixture of uncertainty in t h e scientific a s p e c t s of t h e problem, and t h e subjective and judgmental elements in i t s socio-political aspects, t h e r e is no wholly objective way t o find a best solution.

One approach t o this class of under-specified problem situations i s a n i t e r a t i v e sequence of systems analysis and learning generated by ( e x p e r t o r decision support) system use. This should help shape t h e problem a s w e l l a s aid in finding solutions. Key ingredients, following Phillips (1984). a r e t h e Problem Owners, Preference Technology (which helps t o e x p r e s s value judgements, and formalize time and risk p r e f e r e n c e s , and tradeoffs amongst them), and Information Technology, (which provides and organizes d a t a , information, and models (Figure 2.2)).

There i s no universally accepted definition of d e c i s i o n s u p p o r t s y s - tems. Almost any computer-based system, from d a t a base management o r information systems via simulation models t o mathematical programming o r optimization, could possibly support decisions. The l i t e r a t u r e on information systems and decision s u p p o r t systems is overwhelming (e.g., Radford, 1978; Bonczek

et

al., 1981; Ginzberg

et

al., 1982; Sol, 1983; G r a u e r

et

al.

1984; Wierzbicki, 1983; Humphreys, 1983; Phillips, 1984). Approaches r a n g e from rigidly mathematical treatment, t o applied computer sciences, management sciences, o r psychology.

Decision s u p p o r t paradigms include p r e d i c t i v e models, which give unique answers but with limited a c c u r a c y o r validity. Scenario a n a l y s i s r e l a x e s t h e initial assumptions by making them more conditional, but a t t h e

same

time more dubious. Normative models p r e s c r i b e how things should happen, based on some theory, and generally involve optimization o r game theory. Alternatively, d e s c r i p t i v e o r behavioral models supposedly describe things a s they are, often with t h e exploitation of statistical techniques.

Most r e c e n t assessments of t h e field, and in p a r t i c u l a r those concen- t r a t i n g on more complex, ill-defined, policy-oriented and s t r a t e g i c problem a r e a s , tend t o a g r e e on t h e importance of interactiveness and t h e d i r e c t

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f i g u r e 2.2: The components of d e c i s i o n technology (qf'ter P h i l l i p s , -84).

involvement of t h e end u s e r . Direct involvement of t h e u s e r r e s u l t s in new l a y e r s of feedback s t r u c t u r e s (Figure 2.3). The i n f o r m a t i o n s y s t e m model is based on a sequential s t r u c t u r e of analysis and decision support (i.e., t h e relationships shown in t h e upper p a r t of Figure 2.3, from Radford, 1978).

In comparison, t h e d e c i s i o n s u p p o r t model implies feedbacks from t h e applications, e.g., communication, negotiation, and bargaining onto t h e information system, scenario generation, and s t r a t e g i c analysis.

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The realism of formal models i s increased, f o r example, by t h e introduction of M u l t i a t t r i b u t e U t i l i t y theory (Keeney and Raiffa, 1976; B e l l

et

al., 1977), extensions including uncertainty and stochastic dominance con- c e p t s (e.g., Sage and White, 1984), by multi-objective, multi-criteria optimization methods, and finally by replacing s t r i c t optimization, requiring a complete formulation of t h e problem a t t h e outset, by t h e concept of satisf- icing (Wierzbicki, 1983).

Il-IF OWAT1 ON

GATHEFSNG STMTEGIC ANALYSIS

-

b OF 'ZHE SCEliTAPJ 0

GENEEAT1 ON PROBLEM STRUCTURE

1

NEGOTIATX ON IUF O P U T I ON

SCENARI 0 GENERA11 OW

Figure 2.3: S t r a t e g i c d e c i s i o n problems: i n f o r m a t i o n s y s t e m s v e r s u s aS;S a p p r o a c h b a r t l y @ e r R a u o r d , l9?8).

NEGMIATI ON BAIGAINIIJG SIFiiTEGIC ANALYSIS

OF

THE

PROBLEM STRUCTUFiE

1

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Another basic development i s getting c l o s e r t o t h e u s e r s . I n t e r a c t i v e models and computer g r a p h i c s are obvious developments h e r e (e.g., Fedra and Loucks, 1985). Decision conferences (Phillips, 1984) are a n o t h e r a p p r o a c h , useful mainly in t h e e a r l y s t a g e s f o r t h e clarification of a n issue.

While c e r t a i n l y i n t e r a c t i v e in n a t u r e , most methods involve a decision analyst as well as a number of specialists (generally supposed t o b e t h e problem holders). Concentrating on t h e formulation of t h e d e c i s i o n problem, d e s i g n and e v a l u a t i o n of alternatives, i.e., t h e s u b s t a n t i v e models, are only of marginal importance.

Often enough, however, t h e problem h o l d e r (e.g., a r e g u l a t o r y agency) i s not specialized in a l l t h e component domains of t h e problem (e.g., indus- t r i a l engineering, environmental sciences, toxicology, etc.). E x p e r t i s e in t h e numerous domains touched upon by t h e problem situation i s t h e r e f o r e as much a bottleneck as t h e s t r u c t u r e of t h e decision problem. Building human e x p e r t i s e and some d e g r e e of intelligent judgement into decision supporting software i s one of t h e major objectives of AI.

Only r e c e n t l y h a s t h e area of e w e r t s y s t e m s o r knowledge e n g i n e e r - i n g emerged as a medium f o r successful and useful applications of A1 techniques ( s e e f o r example, P e a r l e t al., 1982; Sage and White, 1984; o r O'Brien, 1985 on e x p e r t systems f o r decision support). An e x p e r t system i s a computer program t h a t is supposed t o help solve complex real-world problems, in p a r t i c u l a r , specialized domains (e.g., B a r r and Feigenbaum, 1982). These systems use l a r g e bodies of d o m a i n knowledge, i.e., f a c t s , p r o c e d u r e s , r u l e s and models, t h a t human e x p e r t s have collected o r developed and found useful t o solve problems in t h e i r domains.

Typically, t h e u s e r i n t e r a c t s with a n e x p e r t system in a consulting dialog, just as h e would with a human e x p e r t . C u r r e n t experimental applications include t a s k s like chemical and geological d a t a analysis, computer sys-

t e m s

configuration, s t r u c t u r a l engineering, and medical diagnosis (e.g

.

^,

Duda and Gaschnig, 1981; B a r r and Feigenbaum, 1981; f o r a r e c e n t over- view, see Weigkricht and Winkelbauer, 1986). E x p e r t systems are machine- based intermediaries between human e x p e r t s (who supply t h e knowledge in a knowledge a c q u i s i t i o n mode), and t h e human u s e r , who s e e k s consultation and e x p e r t advice from t h e system ( c o n s u l t a t i o n modes). An important

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element in t h e u s e r i n t e r f a c e and t h e dialog with such systems i s t h e i r abil- ity to guide t h e u s e r in formulating his problem, and t o e z p l a i n t h e reason- ing used by t h e system.

The system u n d e r design combines s e v e r a l methods of applied systems analysis, o p e r a t i o n s r e s e a r c h , planning, policy sciences, and a r t i f i c i a l intelligence into o n e fully integrated software system (Fedra, 1985, 1986).

The basic idea is t o provide d i r e c t and easy access

to

t h e s e largely formal methods and a substantial factual information basis.

2.3 I n f o r m a t i o n R e q u i r e m e n t s f o r D e c i s i o n S u p p o r t

Given t h e t h e o r e t i c a l framework discussed above, t h e kind of u s e r w e a n t i c i p a t e , a n d t h e r e g u l a t o r y background briefly described in t h e introduction, w e c a n now t r y t o compile o r define a set of information r e q u i r e - ments f o r decision s u p p o r t . What are t h e major c h a r a c t e r i s t i c s of decision-making p r o c e s s e s within t h e above framework, a n d what is, o r r a t h e r should b e , t h e f a c t u a l and p r o c e d u r a l basis (in a decision analysis sense) f o r t h e s e decision p r o c e s s e s ?

The kind of r e g u l a t o r y decision making described above i s c h a r a c t e r - ized by

at

l e a s t t h r e e major problems:

t h e necessity f o r making o r accepting t r a d e o f f s ;

t h e i n c o m m e n s u r a b i l i t y of t h e e f f e c t s weighed in t h e trade-offs;

t h e u n c e r t a i n t y o r lack of information a b o u t t h e consequences of a l t e r n a t i v e c o u r s e s of action.

Based on a r e p o r t by t h e Committee on Principles of A x i s i o n Making for R e g u l a t i n g Chemicals i n the Environment (NAS, 1975), t h e Study Group

R e p o r t on R i s k Assessment by t h e Royal Society (1983), a Brookings Insti- tution R e p o r t on Quantitative R i s k Assessment i n R e g u l a t i o n (Lave 1982), and finally t h e industry's point of view, summarized in t h e Institution of Chemical Engineers International Study Group R e p o r t on R i s k A n a l y s i s in t h e Process I n d u s t r i e s

(ICE,

1985), w e h a v e compiled, e x t r a c t e d and condensed t h e s e wishlists and recommendations into t h e following specifications f o r a Decision S u p p o r t (DS) framework:

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The DS framework should b e based on a simplified model (preferably in graphical r e p r e s e n t a t i o n , e.g., a p i c t u r e , o r flow diagram) of t h e t o t a l system of production, distribution, use, a n d disposal of t h e chemical.

The model should help identify points of economic impact, n a t u r e and s o u r c e of benefits and damages, and possible means of control. This information will provide t h e basis f o r specifying a l t e r n a t i v e control s t r a t e g i e s , and t h e quantification of costs, hazards, a n d benefits.

The decision framework should make i t possible t o identify and p r e s e n t information on a full r a n g e of a l t e r n a t i v e s t h e decision maker has.

Alternative c o n t r o l s t r a t e g i e s as w e l l as a l t e r n a t i v e implementation p r o c e d u r e s a n d schedules should b e included.

The framework should display t h e d a t a s o t h a t all r e l e v a n t a l t e r n a t i v e s c a n b e considered t o g e t h e r . Other major f a c t o r s t h a t might influence t h e decision maker's choice, e.g., legal constraints, previous action, o r ease of implementation, should a l s o b e identified.

The framework should make i t possible t o meet t h e increased public i n t e r e s t in r i s k estimates, which are inherently imprecise, and r i s k management p r o c e d u r e s , by supporting open discussions with t h e aim of achieving a more balanced approach.

The framework should include all identifiable e f f e c t s and consequences of a l t e r n a t i v e actions. This would include social and economic benefits of t h e chemical's use; i t s health effects, ecological e f f e c t s , c o s t of control, economic impacts (plant closure, unemployment, economic indi- c a t o r s such as regional o r national product), enforcement and monitoring costs, and distributional effects (who pays, who benefits).

The DS framework must e n s u r e t h a t no r e l e v a n t c a t e g o r i e s of e f f e c t s are overlooked. Use of a chemical always involves benefits a s w e l l as risks. For example, i t may entail health benefits as well as r i s k s ; and t h e benefits and r i s k s might impinge upon different population groups.

For example, a n insecticide might control infectious disease v e c t o r s while having long-term carcinogenic e f f e c t s a f t e r bioaccumulation.

The decision maker may a l s o wish t o distinguish between r i s k s borne voluntarily with full knowledge, and those b o r n e involuntarily o r without knowledge.

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The level of detail t o b e included in t h e description of effects should b e easily adapted t o t h e decision problem. I t is partly determined by t h e decision maker t o t h e e x t e n t t h a t h e chooses t h e time and r e s o u r c e s available f o r study and analysis, and partly by t h e quality, amount, and availability of data. The level of detail will also v a r y with t h e s t a g e of t h e decision-making procedure in t h a t a brief and quick analysis may b e made

to

s c r e e n potential options and then a more e l a b o r a t e analysis conducted on those options selected f o r more c a r e f u l study. The framework should b e flexible enough t o m e e t t h e s e various needs.

The framework should facilitate t h e comparison of major effects resulting from a l t e r n a t i v e actions and should s e r v e as a convenient basis f o r t h e discussion and review of trade-offs. To t h i s end, t h e r e s u l t s of t h e analysis may need t o b e presented in a variety of formats (e.g., verbal, graphical, tabular) and

at

different levels of detail. The final "briefing version" may only p r e s e n t major effects and major alternatives. However, t h e most detailed analysis available should b e provided as background material s o t h a t t h e decision maker can examine t h e s e details if h e wishes to.

The framework and procedures should b e flexible enough t o

m e e t

t h e demands of different kinds of decisions,

at

different levels, by dif- f e r e n t groups of decision makers.

A l l effects should b e quantified and measured in commensurate terms t o t h e g r e a t e s t e x t e n t possible. F u r t h e r , t h e number of incommensurable measures should b e as small a s possible t o simplify t h e trade-off considerations of t h e decision maker.

While quantitative methods are t o b e used wherever p r a c t i c a l and feasible within reason, t h e a p p a r e n t certainty of numerical outputs should b e carefully i n t e r p r e t e d and presented with responsible qualifi- cations.

The framework should indicate t h e r a n g e of uncertainty and level of ignorance about key pieces of information. If detailed analyses are aggregated o r summarized f o r presentation t o t h e decision maker, information about t h e d e g r e e of uncertainty must b e presented.

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A l l assumptions made by t h e analyst o r implied in t h e methods used in reducing detailed d a t a t o summary o r a g g r e g a t e measures should b e made explicit and c l e a r l y indicated as such.

Where uncertainty e x i s t s in some key pieces of information, o r where assumptions must b e made about t h e r e l a t i v e importance of c e r t a i n effects, t h e decision maker should b e a b l e t o examine t h e sensitivity of t h e r e s u l t s t o variations in both input d a t a and d i f f e r e n t assumptions about relationships.

The framework should make i t convenient t o determine t h e value of obtaining f u r t h e r information and specifically what information should be obtained. In o t h e r words, i t should make i t c l e a r t h a t resolving uncertainty (collecting more information) is a r e l e v a n t choice f o r t h e decision maker although i t may involve some time and cost. In t h i s way t h e framework will facilitate t h e p r o c e s s of continuous review o r sequential decision making.

Key value judgements about weights o r values t o b e assigned t o incom- m e n s u r a b l e ~ should b e t h e responsibility of t h e decision maker. The presentation of information should make i t clear what t h e key t r a d e - offs are and facilitate t h e examination of a l t e r n a t i v e value judgements by t h e decision maker.

To b e useful and relevant, a n information and decision s u p p o r t system should b e responsive to t h e above list of requirements

-

and quite a f e w more. However, i t i s obvious t h a t any computer-based information, decision s u p p o r t o r e x p e r t system i s only one tool in a l a r g e a r s e n a l of methods and p r o c e d u r e s used f o r t h e management of hazardous substances. W e d o believe, however, t h a t well-designed and sufficiently "intelligent", i.e., flexible, responsive, and knowledge-based systems could b e v e r y effective and useful tools indeed.

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3. THE PROBLEM AREA: MANAGEMENT OF HAZARDOUS SUBSTANCES Depending on how one counts and classifies, t h e r e a r e more than 100,000 r e g i s t e r e d chemical substances, with up t o 1000 added

to

t h i s list e v e r y y e a r . A substantial number of t h e s e substances are hazardous, o r potentially hazardous t o man and/or t h e environment.

Hazardous substances a p p e a r as feedstocks, interim o r by-products, final products. o r wastes of industrial processes; a f e w hazardous substances a r e even produced by natural processes.

The major s o u r c e s of t h e s e hazardous substances, t h a t may cause human exposure o r environmental contamination, include:

t h e use of hazardous substances, i.e., dispersive use of agrochemicals, solvents, paints and lacquers;

accidental release during t h e production process, i.e., accidents such a s Seveso o r Bhopal;

transportation accidents;

routine release of wastes, from t h e production process o r from

waste

treatment and disposal operations.

The dimensions of t h e problem are also staggering in volumetric terms:

About 2 gigatons of waste are produced annually in t h e countries of t h e EC, somewhat less than 10% of which i s from industrial sources. Roughly 10% of these industrial wastes are classified as hazardous.*) More graphically, this amounts t o 20 million metric tons, o r a t r a i n of roughly 10,000 km length.

The effective management of t h e s e wastes calls for:

a minimization of waste production by p r o c e s s modification and recycling;

t h e conversion t o non-hazardous forms;

finally, a safe disposal of whatever is left.

8 ) J. Schneider, JRC, Ispra, 1984. Personal communication. For comparison, the US Chemi- cal Manufacturers Association reports 314 million tons of hazardous wastes (311 million tons wastewater, 3 million tons non-wastewater) treated and disposed i n 1983; C'MA (1983).

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In addition t o hazardous wastes, t h e r e i s a l a r g e number of commercial products t h a t are a l s o hazardous. Their production, t r a n s p o r t a t i o n , and use

-

b e f o r e they e n t e r any waste stream

-

i s a l s o of concern. Industrial production p r o c e s s e s t h a t involve hazardous raw materials, feedstocks, o r interim products, which may r e a c h t h e environment a f t e r a n accident, caus- ing d i r e c t health r i s k s t o man, have t o b e considered.

A s a special c a t e g o r y , although implied in t h e above, t r a n s p o r t a t i o n of hazardous substances (including. of c o u r s e , hazardous wastes), must b e included in any comprehensive system.

The e n t i r e life-cycle of hazardous substances (Figure 3.1). from t h e i r production and use t o t h e i r processing and disposal, involves numerous a s p e c t s and levels of planning, policy and management decisions. Techno- logical, economic, socio-political and environmental considerations are involved

at

e v e r y s t a g e of t h e management of t h e s e life cycles, and t h e y involve various levels, ranging from s i t e o r e n t e r p r i s e t o local, regional, national and even international scales, and o v e r d i f f e r e n t time scales, from immediate operational decisions t o long-term planning and policy problems.

3.1 T h e Systems V i m C o m p r e h e n s i v e A s s e s s m e n t

The problems of managing hazardous substances are n e i t h e r well defined n o r reducible t o a small set of relatively simple subproblems. They always involve complex trade-offs under uncertainty, feedback s t r u c t u r e s and synergistic e f f e c t s , non-linear and potentially c a t a s t r o p h i c systems behavior

-

in s h o r t , t h e full r e p e r t o i r e of a real-world m e s s . The complexity and ill-defined s t r u c t u r e of most problems makes any single method o r a p p r o a c h fall s h o r t of t h e expectations of potential users. The classical, mathematically-oriented, but rigid, methods of operations r e s e a r c h and control engineering, t h a t r e q u i r e a complete and quantitative definition of t h e problem from t h e outset, are certainly insufficient.

While only t h e combination of a l a r g e r set of methods and a p p r o a c h e s holds promise of effectively tackling such problems, t h e subjective and dis- c r e t i o n a r y human element must a l s o b e given due weight. This calls f o r t h e d i r e c t and i n t e r a c t i v e involvement of u s e r s , allowing them t o e x e r t

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R g u r e 3.1: Lire cycle of h a z a r d o u s s u b s t a n c e s :

components of t h e s i m u l a t i o n s y s t e m @om Fedra, 1985)

i

^w

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discretion and judgement wherever formal methods are insufficient.

IMDIISTEXAL PBODUCTIOI

W e a r e developing a n integrated and interactive computer-based decision support and information system within t h e framework of a Study Con- t r a c t between IIASA and t h e CEC1s Joint Research Centre (JRC), Ispra.

Recognizing t h e potentially enormous development e f f o r t required (e.g., Pollitzer and Jenkins, 1985) and t h e open-ended n a t u r e of such a project, w e propose a well-structured cooperative e f f o r t t h a t t a k e s advantage of t h e l a r g e volume of scientific software already available. A modular design phi- losophy allows us t o develop individual building blocks, which are valuable products in t h e i r own right, and t o interface and integrate them in a flexible framework easily modifiable with increasing experience of use.

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With t h e functional and problem-oriented, r a t h e r than s t r u c t u r a l and methodological design of this framework, working prototypes t h a t allow us t o explore t h e potential of such systems can b e constructed

at

relatively low cost and with only incremental effort.

Any comprehensive assessment of t h e management of industrial risk, and hazardous substances in particular, r e q u i r e s t h e consideration of tech- nological, economic, environmental, and socio-political f a c t o r s (Figure 3.2).

Every scenario f o r simulation o r optimization, defined interactively with this system, must ultimately b e assessed, evaluated, and compared with alternatives in terms of a list of c r i t e r i a . These c r i t e r i a , t h e r e f o r e , must include economic, technical, environmental, resource-oriented, and finally socio-political descriptors.

Clearly. only a s m a l l subset of t h e s e c r i t e r i a may b e expressed in monetary, o r even numerical terms. Most of them r e q u i r e t h e use of linguis- t i c variables f o r a qualitative description. Using fuzzy set theory, qualitative v e r b a l statements can easily b e combined with numerical indicators f o r a joint evaluation and ranking. In t h e system design, t h e use of programming languages like LISP o r PROLOG gives t h e u s e r freedom t o manipulate symbols and numbers within a coherent framework.

3.2 Information Management and D e c i s i o n Support

The s h e e r complexity of t h e problems r e l a t e d t o t h e management of hazardous substances and r e l a t e d risk assessment problems calls f o r t h e use of modern information processing technology. However, most problems t h a t go beyond t h e immediate technical design and operational management level involve as much politics and psychology as science.

The software system described h e r e is based on information management and model-based decision support. I t envisions a broad and hetero- geneous group of u s e r s , technical e x p e r t s as w e l l as decision and policy makers, and in f a c t , t h e computer i s seen as a mediator and t r a n s l a t o r between e x p e r t and decision maker, between science and policy. The com- p u t e r is thus not only a vehicle f o r analysis, but even more importantly, a vehicle f o r communication, learning, and experimentation.

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1" ^lw

Demonstrahon Prototype: ~ a z a r d o u s ' ~ u b s t a n c e s R~sk Management

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frrpntmtim R i s V C o r t Analysis

Emvi-11- As-t

Prototype Daonstration V u s i o a 1UtS.

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I n t ~ . ~ . t l m u I I m s t l t u t s f v - 1 l . O 5 - t l k l y t s

I I I Y . A-.-I .L- b V 1 a . M c m t r u t tm

t m h r s a l m of t m -E -I*-a. CLT.

Joint R r r u r m C m t r r . 1-a E s t a e l r u m t . I t a l y .

I

to s e l c x t a r n n item, positiao the mo8e pointer, rsd prars tk left

-

b t t a a

...

Figure 3.2: The scope of the demonstration prototype system (master menu).

The two b a s i c elements are t o supply f a c t u a l information based on existing d a t a , s t a t i s t i c s , and scientific evidence, and t o trace t h e likely consequences of new plans. The framework f o r e s e e s t h e selection of c r i - t e r i a f o r assessment by t h e u s e r , and t h e assessment of s c e n a r i o s o r alternative plans in t e r m s of t h e s e c r i t e r i a . The evaluation and ranking is again done p a r t l y by t h e u s e r , where t h e machine only a s s i s t s through t h e compi- lation and p r e s e n t a t i o n of t h e information r e q u i r e d . Alternatively, i t c a n b e done by t h e system on t h e basis of user-supplied c r i t e r i a f o r screening and selection.

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The selected a p p r o a c h f o r t h e design of this software system i s eclec- t i c as well as pragmatic. W e u s e proven o r promising building blocks, and w e use available modules where w e can find them (Zhao

et

al., 1985). W e also e x e r c i s e methodological pluralism: any "model", whether i t is a simulation model, a computer language, o r a knowledge r e p r e s e n t a t i o n paradigm, is by necessity incomplete. I t i s only valid within a small and often v e r y specialized domain. No single method c a n c o p e with t h e full spectrum of phenomena, o r r a t h e r points of view, called f o r by interdisciplinary and t r u l y applied science.

The d i r e c t involvement of e x p e r t s and decision makers shifts t h e emphasis from a production-oriented "off line" system t o a n explanatory, learning-oriented s t y l e of use. The decision s u p p o r t and e x p e r t system i s as much a tool f o r t h e e x p e r t as it i s a testing ground f o r t h e decision maker's options and ideas.

In f a c t , i t i s t h e i n v e n t i o n and definition, i.e., t h e design, of options t h a t i s

at

l e a s t as important as t h e estimation of t h e i r consequences and evaluation. For planning, policy and decision making, t h e generation of new species of ideas i s as important as t h e mechanisms f o r t h e i r selection. I t i s such a n evolutionary understanding of planning t h a t t h i s software system is designed t o s u p p o r t . Consequently, t h e n e c e s s a r y flexibility and expressive power of t h e software system are t h e c e n t r a l focus of development.

3.3 Model Integration and the User Interface

From a u s e r perspective. t h e system must b e a b l e t o a s s i s t in i t s own use, i.e., explain what i t c a n do, and how i t c a n b e done. The basic elements of t h i s self-explanatory system are t h e following:

the i n t e r a c t i v e u s e r interface t h a t handles t h e dialog between t h e u s e r ( s ) and t h e machine; t h i s i s largely menu-driven, t h a t is, at any point t h e u s e r i s o f f e r e d s e v e r a l possible actions which h e c a n select from a menu of options provided by t h e system;

a task scheduler o r control program, t h a t i n t e r p r e t s t h e u s e r r e q u e s t

-

and, in f a c t , helps t o formulate and s t r u c t u r e i t

-

and coordinates t h e n e c e s s a r y t a s k s (program executions) t o b e performed; this

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program contains t h e "knowledge" about t h e individual component software modules and t h e i r interdependencies;

t h e control program can t r a n s l a t e a u s e r request into e i t h e r :

-

a data/knowledge base query;

-

a r e q u e s t f o r "scenario analysis"

t h e l a t t e r will b e t r a n s f e r r e d

to

a problem g e n e r a t o r , t h a t assists in defining scenarios f o r simulation and/or optimization; i t s main task is

to

elicit

a

consistent and complete

set

of specifications from t h e u s e r , by iteratively resorting t o t h e d a t a base and/or knowledge base t o build up t h e iqf'ormation context o r j'rame of t h e scenario. A s c e n a r i o i s defined by a delimitation in s p a c e and time, a

set

of (possibly recursively linked) processes, a

set

of con- t r o l variables, and a

set

of c r i t e r i a t o describe results. I t i s r e p r e s e n t e d by

a s e t oj'process-oriented models, t h a t c a n b e used in e i t h e r simulation o r optimization modes. The r e s u l t s of c r e a t i n g a s c e n a r i o and e i t h e r simulating o r optimizing i t a r e passed back t o t h e problem g e n e r a t o r level through a

e v a l u a t i o n a n d c o m p a r i s o n module, t h a t attempts t o evaluate a scenario according

to

t h e list of c r i t e r i a specified, and assists in organizing t h e r e s u l t s from s e v e r a l scenarios. For t h i s comparison and t h e presentation of results, t h e system uses a

g r a p h i c a l d i s p l a y a n d r e p o r t generator, which allows selection from a variety of display styles and formats, and in p a r t i c u l a r enables t h e r e s u l t s of t h e scenario analysis t o b e viewed in graphical form.

Finally, t h e system employs a

system's a d m i n i s t r a t i o n module, which i s largely responsible f o r housekeeping and learning: i t attempts t o incorporate information gained during a p a r t i c u l a r session into t h e permanent data/knowledge bases and thus allows t h e system t o "learn" and improve i t s information background from one session t o t h e next.

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I t is important to notice t h a t most of t h e s e elements are linked r e c u r - sively. F o r example, a s c e n a r i o analysis will usually imply s e v e r a l data/knowledge b a s e q u e r i e s t o provide t h e f r a m e and n e c e s s a r y parameters t r a n s p a r e n t l y . Within each functional level, s e v e r a l i t e r a t i o n s are possible, and

at

a n y decision b r e a k p o i n t t h a t t h e system cannot r e s o l v e from i t s c u r r e n t goal s t r u c t u r e , t h e u s e r c a n specify a l t e r n a t i v e b r a n c h e s t o b e followed.

The simulation models of t h e production system c a n b e configured to d e s c r i b e t h e comprehensive life-cycle of hazardous s u b s t a n c e s (Figure 3.1). The major components of t h e simulation system are:

t h e industrial production s e c t o r , use and m a r k e t ,

waste management, including t r e a t m e n t and disposal, t h e cross-cutting t r a n s p o r t a t i o n s e c t o r ,

and finally man and t h e environment.

Each of t h e s e major components i s r e p r e s e n t e d by s e v e r a l individual m o d e l s , covering a v a r i e t y of possible a p p r o a c h e s and levels of resolution.

E a c h element of t h e simulation system c a n b e used in isolation, o r i t i s linked with s e v e r a l o t h e r s as p r e - or post-processors into increasingly l a r g e r (sub)systems models (Figure 3.3).

It i s a l s o important to n o t e t h a t none of t h e complexities of t h e systems integration are obvious t o t h e user: i r r e s p e c t i v e of t h e t a s k specified, t h e s t y l e of t h e u s e r i n t e r f a c e and i n t e r a c t i o n s with t h e system a r e always t h e s a m e at t h e u s e r end.

The u s e r i n t e r f a c e i s o n e of t h e most c r i t i c a l elements for such a l a r g e and complex i n t e r a c t i v e system (Figure 3.4). Our i n t e r f a c e design is based on:

menu-driven conversational interaction, t h a t r e s u l t s in a self- explanatory system t h a t does not r e q u i r e t h e u s e r to l e a r n any specific command language, but always o f f e r s c u r r e n t l y available options in a self-explanatory style;

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between processes;

a

context, i.e., current-problem dependent variable s t r u c t u r e ;

t h e use of bit-mapped color graphics;

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a consistent s c r e e n layout, where functional blocks like menus, prompts, e r r o r messages, t a b l e s o r maps, are always a r r a n g e d with a similar s p a t i a l s t r u c t u r e .

Many of t h e functions of t h e i n t e r f a c e are e n t i r e l y t r a n s p a r e n t t o t h e u s e r . In p a r t i c u l a r , t h e t a s k s of problem definition and output s t r u c t u r i n g involve much more t h a n meets t h e e y e in t h e c o l o r g r a p h i c s displays. T h e r e are numerous small, special-purpose, rule-based intelligent i n t e r f a c e rou- t i n e s between most of t h e numerous modules of t h e system, including t h e display modules. These enable smooth coupling of t h e building blocks.

An important p a r t of t h e u s e r i n t e r f a c e ' s t a s k s i s in handling uncertainty a n d ambiguity. Various techniques, based, f o r example, o n fuzzy set t h e o r y (Zadeh, 1983) and a number of symbolic a n d probabilistic computa- tion and estimation techniques (e.g., Goodman and Nguyen, 1985; Gupta et al., 1985; Schmucker, 1984) are used.

3.4 Data Bases. Simulation. and Optimization

The system as d e s c r i b e d above c a n b e used in a v a r i e t y of ways. These modes of o p e r a t i o n , however, s e r v e only as design principles. They are not s e e n by t h e u s e r , who always i n t e r a c t s in t h e s a m e manner through t h e u s e r i n t e r f a c e with t h e system. The system must, however, on r e q u e s t "explain"

where a r e s u l t comes from and how i t w a s derived, e.g., from t h e d a t a b a s e , i n f e r r e d by a rule-based production system, o r as t h e r e s u l t of a model application.

The simplest a n d most s t r a i g h t f o r w a r d u s e of t h e system i s as a n i n t e r a c t i v e inJ'ormation s y s t e m . H e r e t h e u s e r "browses" t h r o u g h t h e d a t a and knowledge b a s e s o r a s k s v e r y specific questions. A s a n example, consider t h e s u b s t a n c e s d a t a b a s e , where t h e basic p r o p e r t i e s of a sub- s t a n c e c a n b e found. But, in addition, t h e system will indicate applicable regulations

-

which t h e u s e r t h e n c a n choose t o r e a d , o r a h i s t o r y of spills and a c c i d e n t s t h a t a p a r t i c u l a r substance w a s involved in. The latter may s e r v e t o develop a "feeling" f o r t h e o r d e r of magnitude of possible consequences of a n accident.

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The second mode of use i s termed scenario analysis. H e r e t h e u s e r defines a s p e c i a l situation or s c e n a r i o (e.g., t h e r e l e a s e of a c e r t a i n sub- s t a n c e from a facility), and t h e n traces t h e consequences of t h i s situation through modeling. The system will a s s i s t t h e u s e r in t h e formulation of t h e s e "What if

..."

questions, largely by offering menus of options, and ensur- ing a complete and consistent specification.

The s c e n a r i o analysis mode c a n use any or a l l models in isolation or linked t o g e t h e r ; t h e selection and coupling of models are automatic. The evaluation and comparison of a l t e r n a t i v e s is always performed in terms of a s u b s e t o r all of a l i s t of c r i t e r i a , including monetary as w e l l as symbolic, qualitative d e s c r i p t o r s (Fedra, 1985). The use of c e r t a i n models is implied by t h e selection of indicators and c r i t e r i a t h a t are chosen

to

d e s c r i b e a s c e n a r i o ' s outcome.

Two time domains f o r s c e n a r i o analysis with d i f f e r e n t problems a d d r e s s e d are supported: t h e models can e i t h e r b e used to simulate medium- to long-term phenomena, with a c h a r a c t e r i s t i c time scale of y e a r s , or s h o r t - t e r m events, i.e., accidents, with a c h a r a c t e r i s t i c time s c a l e of days.

Switching from one mode t o t h e o t h e r , with t h e n e c e s s a r y aggregation or disaggregation of information, must b e possible.

Similar to t h i s switching in t h e time domain, a change in t h e s p a c e domain must a l s o b e supported. T h e r e is of c o u r s e a close linkage between time and s p a c e s c a l e s , in t h a t most short-term phenomena like spills or accidents are r e l e v a n t on a local t o regional s c a l e , whereas long-term phenomena like continuous r o u t i n e r e l e a s e of hazardous substances will usually b e considered on a regional t o national scale.

A s implied in t h e above listing of possible application a r e a s , s c e n a r i o analysis may b e e i t h e r s t r a i g h t f o r w a r d simulation, or a combination of simulation and optimization techniques. In t h e latter c a s e , t h e u s e r d o e s not have to specify c o n c r e t e values f o r all c o n t r o l variables defining a s c e n a r i o , but r a t h e r specifies allowable r a n g e s o n them as well as a goal s t r u c t u r e . In t h e optimization mode, o u r system becomes a decision s u p p o r t system p r o p e r (Figure 3.5).