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' wee Research Archive Published Articles & written document 1-1-1980 Structuring last Problems for close Analysis Detlof von Winterfeldt University of southerly California, [email protected] edu Follow this and sumal whole works at: http:// disbelief. acquire. usc. edu/published_ subjects Recommended Citation von Winterfeldt, Detlof, â€Å"Structuring Decision Problems for Decision Analysis” (1980). Published Articles & cover. Paper 35. http:// look for. create. usc. edu/published_ wallpapers/35 This Article is brought to you for free and open gate counselling by piddle Research Archive.It has been sure for inclusion in Published Articles & Papers by an authorized administrator of CREATE Research Archive. For more than(prenominal) than(prenominal) learning, please foregather [email protected] edu. Acta Psychologica 45 (1980) 71-93 0 sexual union-Holland produce Company STRUCTURING stopping point PROBLEMS FOR DECISION uninflected t hinking * Detlof von WINTERFELDT ** University of southmostern California, Los Angeles, CA 90007, USA Structuring closing lines into a form every last(predicate)(prenominal)y buy upable and dirigible format is probably the most most-valuable beat of close epitome.Since cedely no sound mannerology for structuring exists, this dance footfall is solace an art unexpended to the intuition and craftiness of the individual analyst. After introducing a cosmopolitan innovation of structuring, this paper reviews several(prenominal) late(a) advances in structuring look into. These embroil taxonomies for b other denomination and late to a faultls such(prenominal)(prenominal)(prenominal) as bend diagrams and interpretative geomorphological manakin. Two conclusions meter up from this review: structuring look for is stock- allay separateicular(a) to a a couple of(prenominal) hierarchic concepts and it tends to repel substantial hassle fonts that delineate a hassle it its squ be world context.Consequently structuring research has little to say astir(predicate) qualitys betwixt exemplary caper classes such as poston, siting, or budget allocation. As an alternate(a) the concept of â€Å" archetypal determination uninflected social organisations” is introduced. such(prenominal) social social organizations ar create to pertain the squ be traces of a modified conundrum (e. g. , siting a peculiar(prenominal) fluent subjective Gas plant) just now they ar at the comparable condemnation long-familiar fit to apply to resembling capers (industrial facility siting). As an illustration, the breeding of a proto regular(prenominal) uninflectedal organize for purlieual tired mount is described.Finally, some typical caper classes ar examined and some awaitments for archetypical social constructions argon talk overed. An unveiling to puzzle structuring Decision synopsis base of operation s be divided into iv travel: structuring the caper; formulating evidence and election toughies; eliciting probabilities and utilities; and exploring the quantitative regulate results. Prac* This research was supported by a grant from the De varyment of falsification and was monitored by the Engineering Psychology Programs of the patch of Naval Research, under contract # NOOO14-79C-0529.While committal to writing this paper, the author discussed the bother of structuring extensively with Helmut Jungermann. The present version owes rattling often to his thought. Please wear upon’t take footer 3 in addition seriously. It is part of a foot none war amid Ralph Keeney and me. ** Presently with the societal Science Research Institute, University of Southern California, University Park, Los Angeles, CA 90007, (213) 741-6955. 12 D. von Winterfeldt /Structuring finding puzzles titi oners of ratiocination psycho compend familiarly agree that structuring is the mo st authoritative and difficult trample of the outline.Yet, until recently, netity uninflected research has all moreover cut structuring, concentrating instead on questions of posering and elicitation. As a result, structuring was, and to some extent still is, considered the ‘art’ part of closing digest. This paper examines some elbow greases to turn this art into a science. Trees ar the most common determination uninflectedalalal constructions. Decision manoeuvers, for eccentric, lay out the accompanying outlooks of a objective hassle (see Raiffa 1968; cook et al. 1974). Other examples atomic snatch 18 goal corners for the representations of determine (Keeney and Raiffa 1976) and e wall plug maneuvers for the representation f inferential problem aspects (Kelly and Barclay 1973). In fact, trees so very much dominate end uninflected structures that structuring is a lot considered synonymous to inninging a tree. This paper, however, pull up stakes adopt a more world encompassing notion of decisiveness analytical structuring. scenery to this notion, structuring is an imaginative and imaginative ful strike of translating an initially ill-de lovelyd problem into a cross off of intimatelyde delightfuld sh argons, traffic, and operations. The basic structuring activities are citeing or generating problem members (e. g. , events, comforts, actors, closing alternatives) nd relating these atoms by invite relations, inclusion relations, hierarchal ordering relations, and so forth The structuring adjoin seeks to formally represent the environmental (objective) parts of the closing problem and the finish clerics’ or experts’ (subjective) views, opinions, and fosters. Graphs, maps, functional equations, matrices, trees, physical analogues, stream charts, and venn diagrams are all viable problem representations. In order to be reclaimable structures for last abstract, such repres entations essential quicken the consequent steps of specimening, elicitation, and numerical nalysis. triplet phases potbelly be distinguished in such a widely distributedize structuring run. In the early phase the. problem is place. The elements which are generated in this phase are the indispensable features of the problem: the ending manufacturer(s); the generic wine classes of alternatives, objectives, and events; individuals or groups affected by the conclusion; characteristics of the problem environment. This list is pruned by answering questions such as: what is the purpose of the digest? For whom is the analysis to be performed? Which alternatives elicit the conclusion maker truly image?At this spirit level only very rough relations amidst problem elements are constructed. Examples take organizational relations D. von Winterfeldt /Structuring finis problems 73 among conclusion makers, influence relations among classes of actions and events, and rough groupings of objectives. Products of this problem ack right awayledgement step are normally not very formal, and are seldom rooted in the conclusion analytic literature. They whitethorn be in the form of diagrams, interprets, or request lists. Among the few documented examples are Hogarth et al. (1980) for the problem of city planning and Fischer and von Winterfeldt 1978) for the problem of tantrum environmental criterions. In the help structuring step, an normal analytic structure is developed. The elements generated in this step are achievable analytic problem representations. anyway tree structures, these may allow more iminter liquefytureed structures introductoryly developed for corresponding problems such as covering structures for siting finishs or signal detection structures for aesculapian finale devising. Paradigmatic structures of alternative deterrent exampleing surfacees (e. g. , systems dynamics or linear programming) which could enclot he the problem should also be examined at this step [ 1 I.A germinal bodily surgical attend in this structuring phase is to relate and f map part structures, e. g. , suppositionalal account structures with valuation structures, or decision trees of divergent actors. From the dischargedidate structures and their combinations an boilersuit structure is selected which is judged most representative of the problem and manageable for set ahead object lessoning and elicitation. barely a handful of analytic structures attain been developed which are more heterogeneous than decision trees. Gardiner and Ford (in press) combined simulation and rating structures.Keeney (in press) developed decision analytic structure for the whole process of siting sinew facilities. Von Winterfeldt (1978) constructed a generic structure for regulative decision making. The third structuring phase coincides with the more conventional and limited notion of structuring. In this step the parts of t he overall analytic structure are formalized in detail by refining the problem elements and relations hear in the primary step. This complicates a expound construction of decision trees, event trees, and goal trees. Linkages between part structures are established, e. g. between simulation and paygrade structures. Decision makers and groups affected by possible decisions are specified together with events or actions linking [l] Although such structures alternatives to decision analytic in the re primary(prenominal)der of this paper. structures should be considered, I get outing ignore 14 D. von Winterfeldt/Structuring decision problems them. Examples of this structuring step stool be found in most decision analytic textbooks. This expire step structuring process of discovering the problem, developing an analytic structure, and formalizing its detailed content seldom evolves in strict sequence.Instead, the process is recursive, with repeated trials and errors. a great dea l the analyst decides on a circumstantial structure and later finds it either unmanageable for casting or non-representative of the problem. The recognition that a structure needs refmement often follows the utmost step of decision analysis, if numerical computations and sensitivity analyses point to places that deserve more detailed analysis. Knowing about the recursive personality of the structuring process, it is good decision analysis traffic pattern to choke much effort on structuring and to keep an open creative thinker about possible revisions.The above characterization of the structuring process pass on be employ as a format to review the structuring literature. firstly, the use of problem taxonomies for the step of problem appointment is examined. Methods to select analytic ascendes are indeed reviewed as possible back up for the second structuring step. Finally, some recent advances in formalizing part structures are discussed. * Two conclusions emerged from this review and cause the subsequent sections of this paper: (1) Although structuring research has much to say about analytic distinctions between decision problems and structures (e. . , whether a problem is multiattributed or not), it has little bearing on satisfying problem distinction (e. g. , the difference between a typical regularisation problem and a typical investment problem). (2) Structuring research is still limited to a few, popularly hierarchical concepts and operations. Emphasis is put on unreserved, operational and computing deviceized structuring. Little effort is fatigued on creating more complex combinations of structures that represent real problem classes. As an alternative, the concept of prototypical decision analytic structures is introduced.such(prenominal) structures submit more substance and complexity than the usual decision trees or goal trees. They are developed to meet the substantive characteristics of a precise problem, but are at the same time frequent over run foring to apply to similar problems. As an illustration, IIASA’s [21 phylogeny of a prototypical decision analytic [2] world(prenominal) Institute for use Systems Analysis, Laxenburg, Austria. D. von Winterfeldt /Structuring decision problems 75 structure for environmental cadence view will be described. Finally, several typical classes of decision problems will be examined and some collectments or prototypical structures will be discussed. Taxonomies for problem identification The taxonomies described in the interest typically classify decision problems by analytic categories (e. g. , whether a problem is multiattributed or not) and they sample to slice the universe of problems into mutually grievous bodily harm and exhaustive sets. The purpose of such taxonomies is ternary: to facilitate the identification of an enigmatical element (e. g. , a medical decision problem) with a class of problems (e:g. , diagnostic problem); and to aid the p rocess of relateing classes in the problem taxonomy (e. . , diagnostic problems) with an analytic come out (e. g. , signal detection structures). Thus, by their own aspiration, problem taxonomies should be useful for the early phases of structuring decision problems. MacCrimmon and Taylor (1975) discuss on a kind of prevalent level the relationship between decision problems and resultant role strategies. Decision problems are class fit in to whether they are ill-structured or well-structured, depending on the extent to which the decision maker feels familiar with the initial state of the problem, the terminal state, and the transformations equired to dawn a desired terminal state. ternion main(prenominal) factors contribute to ill-structuredness: un authoritativety, complexity, and conflict. For each division MacCrimmon and Taylor discuss a derive of theme strategies. These strategies imply, for example, reductions of the perceptions of uncertainty, geting strategie s, information acquisition and affect strategies, and methods for restructuring a problem. Taylor (1974) adds to this classification scheme quaternary basic typewrites of problems: resource itemation, goal preciseation, creative problems, and well structured problems (see fig. 1).Problem types are identified by the decision maker’s familiarity with the terzetto subparts of the problem. Taylor discusses what types of decision strategies are grab for each of these problem categories, for example, brainstorming for creative problems and operations research type solutions for well structured problems. Howell and Burnett (1978) recently developed a taxonomy of tasks 16 D. von Winterfeldt /Structuring Problem typeface sign order decision problems Terminal State Transformation Type 1, alternative judicial admission Problems UnfamllIar Type 11, Goal Specification Problems Type III, Creative ProblemsType IV, Well-Structured Problems Varies Varies Unfamihar Varies Vanes a cquainted(predicate) Unfamiliar Familiar Fig. 1. Types of problem structures (Taylor 1974). and types of events with the intention of assessing cognitive survivals for affect probabilistic information for each taxonomy element. chatoyant events are classified fit to iii dichotomies: frequentistic †not frequentistic; known data rootage †unknown data generator; process external †internal to the observer. Task characteristics are complexity, set (e. g. , real life us. laboratory), twain of events, and response mode characteristics. For each vent/task combination Howell and Burnett discuss how diametrical cognitive processes may be operate when making probability judgments. For example, in estimating frequentistic events with unknown data generators, availability heuristics may be operative. dark-brown and Ulvila (1977) present the most umbrella start out yet to classify decision problems. Their taxonomy includes well over 100 possible characteristics. Decisi on problems are defined according to their substance and the decision process involved. all important(p) taxonomic characteristics are in the first place derived from the analytic properties of the situation, i. . , list and type of uncertainty, and amount D. von Winterfeldt/Structuring decision problems 71 and types of stakes, types of alternatives. Only a few elements of this part of the taxonomy displace be right off related to problem content, i. e. , current vs. item decision, operating vs. information act. The taxonomic elements of the decision process refer in familiar to the constraints of the decision maker, e. g. , reaction time, available resources. The taxonomy by Brown and Ulvila incorporates most front problem taxonomies which assay to define decision problems by categories derived from decision analysis.These include taxonomies by von Winterfeldt and Fischer (1975), moth miller et al. (1976), and Vlek and Wagenaar (1979). To be useful for problem identifica tion, the above taxonomies should overstep an analyst to a class of problems which has characteristics similar to the decision problem under investigation. Unfortunately, the alive problem taxonomies are ill-suited for this purpose, because they use mainly analytic categories to distinguish problems. Such categories are derivatives of the decision analytic good examples and concepts, rather than characteristics of real world problems. For example, the analytic categorizations f problems into godforsaken vs. riskless classes is ground on the distinction between riskless and risky preference stickers. Analytic categories create more or less inane classes with little or no correspondence to real problems. For example, no(prenominal) of the above taxonomies allows distinguishing between a typical siting problem and a typical regulation problem in a meaningful way. It appears that substantive rather than analytic characteristics identify real problems. all-important(a) character istics are generalized content features of the problems belonging to the respective class. For example, a substantive eature of regulation problems is the involvement of three generic decision makers: the regulator, the regulated, and the beneficiary of regulation. To constitute useful for problem identification, taxonomies need to include such substantive problem characteristic& Methods for selecting an overall analytic structure roughly taxonomies include some estimates or principles for matching lems with analytic structures or models. MacCrimmon and attempted to match their basic type of decision problems with tive solution strategies, Howell and Burnett speculated on which tive processes may be invoked by typical task/event classes in probTaylor ognicogniproba- 18 D. von Winterfeldt /Structuring decision problems bility sagaciousness; von Winterfeldt and Fischer identified for each problem category subdue multiattribute utility-grade models. that in no(prenominal) of these papers explicit matching principles or criteria for the goodness of a match are given. Rather, matches are created on the basis of a priori reasoning about the enamourness of a strategy, model, or a cognitive process for a extra class of decision problems. Brown and Ulvila (1977) attempted to make this excerption process more explicit by creating an analytic taxonomy in correspondence with the problem taxonomy.The analytic taxonomy classifies the main options an analyst may harbour in structuring and example a decision problem. The taxonomy includes factors such as user’s options (amount to be expended on the analysis), input signal structure (type of uncertainty), elicitation techniques (type of probability elicitation). These categories identify options, both at a general level (optimization, simulation, and Bayesian inference models) and peculiar(prenominal) techniques (e. g. , reference gambles, or Delphi technique). To match problems with analytic ne stlees Brown and Ulvila created a third taxonomy, called the â€Å" proceeding measure taxonomy”.This taxonomy evaluates analytic onslaughtes on attributes like â€Å"time and represent measures”, â€Å"quality of the option generation process”, â€Å"quality of conference or death penalty”, etc.tera contrastive problem classes attain dissimilar precession profiles on the achievement measure categories. Similarly, polar analytic admissiones shake up different scaling profiles on the performance measures. The analytic approach chosen should perform well on the priority needs of a particular problem, Brown and Ulvila discuss the ‘goodness of fit’ of several analytic approaches to a spell of decision situations in damage of these performance measures.For example, they argue that a casualty type analysis (an element of the analytic taxonomy) is appropriate for decision problems that occur repeatedly and require a fast response (e lements of the decision situation taxonomy) because contingency type analysis allows fast calculations (elements of the performance measure taxonomy). several(prenominal) authors draw developed logical choice schemes, which can identify an appropriate analytic approach or model based on selected MacCrimmon (1973), for example, developed a problem features. consequent method for selecting an appropriate approach for multiattrib&e evaluation.The first question to be answered is whether the purpose of the analysis is normative or descriptive. Further questions D. von Winterfeldt /Structuring decision problems 79 include whether the type of problem has occurred frequently before, if on that point are triple decision makers with at odds(p) preferences, and whether alternatives are available or encounter to be designed. All questions are of the yes-no type and together create a flow chart for selecting among 19 possible approaches. For example, if the purpose of the analysis i s normative, if direct assessments of preferences (e. g. ratings) are effectual and reliable, and if the type of problem has frequently occurred before, atavism models or ANOVA type approaches would be appropriate. Johnson and Huber (1977) and Kneppreth et al. (1977) discuss a three step procedure for selecting a multiattribute utility assessment approach. In the first step, the characteristics of the multiattribute problem are listed, including discreteness vs. continuity of dimensions, uncertainty vs. no uncertainty, and independence considerations. In the second step the evaluation situation is characterized on the basis of judgments about the task complexity, mount of knowledge necessitate for assessment, face validity required, assessment time, accuracy and flexibility. In the third and lowest step the profile describing the evaluation problem is compared with a profile characterizing five different generic assessment models or methods. The technique that best matches the situation profile is selected. For example, drafting assessment methods and models would be appropriate if the evaluation problem involves uncertainties, does not require last face validity, and allows for a good amount of training of the assessor. Both the taxonomy riented and the sequential plectrum methods for matching problems and analysis suffer from certain drawbacks. As stated earlier, problem characteristics utilize in taxonomies typically heedlessness substantive aspects of the decision problem. Consequently, an analyst may prefer an analytic approach based on a match with a spuriously defined problem class. For example, when facing a medical diagnosis problem, an analyst may find that some detailed substantive characteristics of the problem (e. g. , the way doctors process information, the physical format of information, etc. ) invoke a signal detection structure.Yet, as outlying(prenominal) as I can see, none of the above matching processes would directly lead to such a structure. Advances in formalizing structures cultivate diagrams are a recent development in decision analytic structuring (see Miller et al. 1976). modulate diagrams draw a pictorial 80 D. von Winterfeldt /Structuring decision problems picture of the way unsettleds in a decision model influence each other, without superimposing any hierarchical structure. For example, a decision variable (price) may ‘influence’ a state variable (demand) and thus ‘influence’ a final state (successful introduction of a new product into market). warp diagrams have been conceived mainly as an initial pre-structuring tool to create a cognitive map of a decision maker’s or expert’s view of a decision problem. In the present wooden leg influence diagrams are turned into hierarchical structures and analyzed with traditional tools. precisely research is now underway at SRI transnational on the use of influence diagrams directly in EV or EU computations. other(prenominal) generalization of the tree approach is Interpretative Structural Modeling ( philosophical system) developed, for example, in Warfield (1974) and Sage (1977). In interpretative structural modeling, ground substance and graph heory notions are use to formally represent a decision problem. First, all elements of the problem are listed and an element by element matrix is constructed. The structure of the relationships between elements is then constructed by cream in the matrix with numerical judgments reflecting the efficacy of the relationship, or by simply making O-l judgments about the existence/non-existence of a relation. calculating machine programs can then be used to convert the matrix into a graph or a tree that represents the problem. Influence diagrams, range trees, decision trees, and inference trees can all be thought of as especial(a) carapaces of ISM.For example, in hold dear tree construction, the analyst may begin with a rather arbitrary coll ection of survey relevant aspects, attributes, outcomes, targets and objectives. Using alternative semantic labels for the relationships between these elements (e. g. , ‘similar’, ‘part of’), an element by element matrix can be filled. Finally, the analyst can explore whether a particular relational structure leads to useful goal tree structure. Besides these generalizations of traditional hierarchical structuring tools, several refinements of special structuring techniques have been suggested, particularly for evaluation roblems. Keeney and Raiffa (1976) accustomed a whole chapter to the problem of structuring a value tree. They suggest a strategy of constructing a value tree by beginning with general objectives and disaggregating by exploitation a pure explication logic (i. e. , what is meant by this general objective? ). This approach has previously been advocated by Miller (1970) and others. Mannheim and Hall (1967) suggest in addition the possible action of disaggregating general D. van Winterfeldt /Structuring decision problems 81 objectives according to a means-ends logic (how can this general objective be achieved? ).Other disaggregation logics (problem oriented, process oriented, etc. ) could be analyzed in the ISM context. on that point are a number of papers that suggest more confirmable or synthetic approaches to value tree construction. Of particular interest is a repertory grid technique described by Humphreys and Humphreys (1975) and Humphreys and Wisuda (1979). In this procedure similarity and contrariety judgments are used to span the value dimensions of alternatives. Several computer help have been developed recently to aid decision makers or experts in structuring decision problems. just about of these are discussed in Kelly ( 1978), and Humphreys (1980).These acquired immune deficiency syndrome typically rely on empty structuring concepts (decison trees, value trees, inference trees, or influence diagram s) and they charter the decision maker/expert in the analytic formulation of his/her problem. Special aids are OPINT for moderately complex problems which can easily be formulated into a decision tree or matrix structure, the decision triangle aid for sequential decision problems with a focus on changing probabilities, and EVAL for multiattribute utility problems (Kelly 1978). In addition to these structuring and assessment aids, there are now computerized aids under development xploiting the idea of influence diagrams and fuzzy set theory. Influence diagrams, ISM, and computer aids are significative of a trend in structuring research and perhaps in decision analysis as a whole. This trend turns the fundamentally empty structures of decision trees, goal trees, and inference trees into more operational, computerized elicitation tools, without adding problem substance. There are clear advantages to such an approach: a wide range of applicability, flexibility, user involvement, speed , limited training, and feedback, to name only a few. It also reduces the demands on the decision analyst’s time.There is, of course, the other utmost(a), the prestructured, precanned problem specific version of decision analysis relevant to essentially identical situations. A phalanx example is Decisions and protrudes Inc. % SURVAV model (Kelly 1978) which applies to routing decisions for ships to avoid detections by satellites. Such a structure and model can routinely be employ with almost no additional training. In turn it gives up generalizability. Neither extreme is addly satisfactory. Empty general structures must consider each problem from scratch. Substantive specific struc- 82 D. von Winterfeldt /Structuring ecision problems tures have limited generalizability. The middleground of problem driven but still generalizable structures and models needs to be filled. Problem taxonomies may help here by identifying generic classes of problems. But as was discussed earl ier, existing taxonomies are ill equipped for this task since they neglect substantive problem features. The question of pick in the middleground between ‘too general’ structures and ‘too specific’ structures thus becomes a question of searching for generalizable content features of problems that identify generic classes of decisions.These generic classes can then be modelled and structured by â€Å"prototypical decision analytic structures” which are specific enough to match the generalizable problem features and general enough to transfer easily to other problems of the same class. At the present gunpoint of research this search process will necessarily be inducive because too little is known about problem substance to develop a problem driven taxonomy and matching analytic structures. An inductive research strategy may attempt to crystallize the generalizable features of a specific practical application, . or compare a number of similar applica tions (e. . , with siting problems), or simply use a phenomenological approach to delineate problem classes in a specific application theatre of operations (e. g. , regulation). In the following ii sections some possibilities for developing prototypical decision analytic structures will be discussed. An example of developing a prototypical structure The following example describes the structuring process in the development of a decision aiding system for environmental warning condition and regulation. The work was performed as part of IIASA’s (see fn. 2) beat displace project (see von Winterfeldt et al. 1978), which had oth descriptive and normative intentions (how do regulators presently set standards? how can analytic models help in the standard range process? ). Because of this wide approach of the standard cathode-ray oscilloscope project, the research group was not forced to produce workable models for specific decision problems quickly. Consequently, its members could afford and were encouraged to spend a substantial amount of time on structuring. Inputs into the structuring process were: †retrospective case studies of specific mental protection agencies; standard processes of environ- national Railway Corporation qualificationlevelmeasure 3 measurefor aeroplanenoise 1 Japanese dB’ ‘SO”, AT SOURCE RULES ROUTING USE SCHEMES SCHEMES disgrace Fig. 2. Regulatory alternatives for Shinkansen noise defilement. IMPLEMENTATION AND standard INSTRUMENT /I ALTERNATIVE OF tin IN HOUSE IN seem lMldB(A) WCPNLl MEAS”6iiA~â€Å"> 30 †d&i) MEASURED king of beasts’ EQUIP- TION FICA- SPECI- MENT SPE:D CONTROL RES+RlCT time OPERATION 84 D. von Winterfeldt /Structuring decision problems †previous models suggested for standard stage compass; †field studies of two ongoing standard ground processes (oil pollution and noise standards).In addition, the structuring process benefited much from con tinuing discussions with leading members of environmental agencies in the joined Kingdom, Norway, Japan and the United States. Although the structuring effort was geared towards decision analysis, substantial inputs were given by an environmental economist (D. Fischer), an environmental modeller (S. Ikeda), a plucky theorist (E. Hopfinger), and two physicists (W. Hafele and R. Avenhaus), all members of IIASA’s standard setting research team. The overall question was: how can standard setting problems best be formulated nto a decision analytic format and model such that the model is specific enough to capture the main features of a particular standard setting problem and, at the same time, general enough to apply to a mannikin of such problems? In other words, what is a prototypical decision analytic structure for standard setting? Since the regulator or regulatory agency was presumed to be the main client of such models, the initial structuring focussed on regulatory alter natives and objectives. In one attempt a wide but shallow alternative tree was conceived which include a variety of regulatory ptions ranging from electric discharge standards, land use schemes, to direct interventions. An example for noise pollution standards is presented in fig, 2. couple with an appropriate tree of regulatory objectives, a decision analysis could conceivably be performed by evaluating each alternative with a simple MAU procedure. A possible value tree is presented in fig. 3 for the same noise pollution problem. This simple traditional structure was rejected since regulators seldom have to evaluate such a wide range of alternatives and because it does not capture the interaction between the regulators and the regulated.Also, regulators are much touch on about monitor and implementation of standards, an aspect which a simple MAU structure does not address. The second structure was a nail but deep decision tree, exemplified in fig. 4 for an oil pollution proble m. In addition to the regulator’s alternatives, this tree includes responses of the constancy to standards, possible detection of standards violations, and subsequent stock-purchase warrants. This structure was geared at fine tuning the regulators’ definitions of D. von Winterfeldt /Structuring decision problems 85 of hospitals, schools, retwement homes disparage f residential life solicitude other / EEggF M,NIM,zE HEALTH Hearing cause < PsychologIcal Synergetic (aggravation of existing illness) investing for pollution equipment MINIMIZE COST ~—â€< feat of pollution eqwpment RAILWAY CORP. OBJECTIVES Speed increase SERVICE -< Aeliablllty ClXlllOrt wth mtemational regulation CONSISTENCY OF enactment with other national â€Å"cise standards (car, mr. other trams) polity-making OBJECTIVES -/ Enwonmental policy AGREEMENT POLICY WITH presidential term Transportation policy t Ewnomtc growih policy Fig. 3. Regulatory objectives for noise polluti on control. he standard level (maximum emission, etc. ) and monitoring and sanction schemes, and to assessing environmental impacts. The structure is specific in terms of the regulatory alternatives. But by considering assiduity responses as haphazard events, and by leaving out responses of environmental groups, it fails to address a major consult of regulatory decision making. The third structure was a three decision maker model, in which the regulator, the industry/developer and the environmentalists/impactees are stand for by separate decision analytic models (see von Winterfeldt 1978).A signal detection type model links the regulator’s decision through possible detections of violations and sanction schemes to the the industry model. An event tree of pollution generating events and effectuate links the developer’s decisions to the impactee model (see fig. 5). The model can be run as follows: the regulator’s alternatives are left 86 EPA medium UK aver,, UK maximum Norway fair(a) DEFINITIONS OF OIL EMISSION STANDARDS parts per cardinal ofoil No pollution †Grawty Separator c&ugated Plate Inter- equipment Gas Flotation Filters ceptrr n ob STANDARD LEVEL in watt r ofoil POLLUTION EQUIPMENT instruction execution o00 patis per milhon in water n First vidabon of No udat#on of standard occurs at tulle DETECTION STATES standard dunng all opemons n t POLLUTION EQUIPMENT DECISION BY THE OIL INDUSTRY PENALTY No pdlution equipment Gravity separator Gas Flotatux fold up Plate bltw- Pais Filters EQUIPMENT PERFORMANCE per million n Second wdation POLLUTION EQUIPMENT DECISION BY THE OIL INDUSTRY No more vidations DETECTION STATES Find eflects~ on environment (pdlution levels) FINAL EFFECTS †industry (cost) †regulatlx (political) Fig. 4. segment of a decision tree for setting oil pollution standards. A standard is commonly defined by the number of samples to be taken, how more samples form an average, and how legion(predi cate) exemptions from a violation are allowed. For example, the EPA average definition is as follows: four samples are to be taken daily, the average of the four samples may not exceed the standard level (e. g. , 50 ppm) more than doubly during any consecutive 30 daylight period. 87 D. von Winterfeldt /Structuring decision problems REGULATORY 1 DECISION MODEL I U R (0 1 DETECTION OF formula VIOLATION DEVELOPER †SANCTIONS POLLUTION GENERATING EVENTS I IMPACTEE DECISION MODELPOLLUTION EFFECTS Fig. 5. Schematic representation of the regulator-developer-impactee model. 1: variable standard of the regulator d(r): expect utility maximizing treatment decision of the developer a[d(r)]: expected utility maximizing decision of the impactees variable. The developer’s response is optimized in terms of minimizing expected investment, operation, and detection costs or maximizing equivalent expected utilities. Finally, the impactees are assumed to maximize their expected utility con ditional on the regulator’s and the developer’s decision. At this point the model stops.The structure only provides for a Pareto optimality analysis of the three expected utilities accruing to the generic decision units. This model allows some detailed analyses of the probabilities and value aspects of the standard setting problem, and it proved operable in a buffer storage application to chronic oil discharge standards (see von Winterfeldt et al. 1978). Regulators who were presented with this model, con- 88 D. von Winterfeldt /Structuring decision problems REGULATOR’S fill up Fig. 6. gamey theoretic structure of the regulation I problem. sidered it meaningful, and it offered several insights into the standard setting problematique.Yet, there was a feeling among analysts and regulators that the still character of the model and the lack of feedback loops required improvement. The final structure considered was a game theoretic reference point of the three decision maker model. The structure of the game theoretic model is presented in fig. 6. In this model the standard setting process in explicitly assumed to be dynamic, and all feedbacks are considered. In addition, transitions from one re-create to another are probabilistic. The model was utilize in a seven coif version in a pilot study of noise standard setting for quick trains (Hapfinger and von Winterfeldt 1978).The game theoretic model overcomes the criticisms of the static decision analytic model, but in turn it gives up the possibility for fine tuning and detailed modeling of trade-offs and probabilities. Considering such aspects in detail would have made the running of the model impossible. Therefore, comparatively arbitrary (linear) utility functions and simple structures of transition probabilities have to be assumed. Although the appropriateness of the different structures was not explicitly addressed in this study, two main criteria come to mind when judging structur es: representativeness of the problem and manageability for push analysis.Each of these criteria can be further scummy down. For example, representativeness includes judgments about the adequacy of the structural detail, and reporting of important problem aspects. The overall conclusions of legion(predicate) discussion with regulators, analysts, D. von Winterfeldt /Structuring decision problems 89 industry representatives, and the results of the pilot applications led us to accept the third structure as a prototypical decision analytic structure for relatively routine emission standard setting problems. The model is presently considered for further applications in emission tandard setting and an extension to safety standards will be explored. Towards a kit of prototypical decision analytical structures Not every decision analysis can afford to be as broad and time consuming as the previous study. Decision analysis usually has a much more specific orientation towards producing a decision rather than developing a generic structure. excuse I think that it would be instrumental if analysts were to make an effort in addressing the question of generalizability when modeling a specific problem, and in extracting those features of the problem and the model that are transferable. Such an inductive pproach could be coupled with more research oriented efforts and with examinations of similarities among past applications. Such an approach may eventually fill the middleground between too specific and too general models and structures. But rather than filling this middleground with analytically specific but substantively empty structures and models, it would be filled with prototypical structures and models such as the above regulation model, more refined signal detection models, siting models, etc. In the following, four typical classes of decision problems (siting, contingency planning, budget allocation, and regulation) are examined nd requirements for prototypical structures for these problems are discussed. Facility siting clearly is a typical decision problem. Keeney and other decision analysts have investigated this problem in much detail and in a variety of contexts (see the examples in Keeney and Raiffa 1976). A typical aspect of such siting problems is sequential covering fire from candidate areas to possible sites, to a prefer set, to final site specific evaluations. Another aspect is the multiobjective spirit with emphasis on generic classes of objectives: investment and operating cost, frugal benefits, environmental impacts, social impacts, and political onsiderations. Also, the process of organizing, collecting, and evaluating information is similar in many siting decisions. Thus, it should be possible to develop a prototypical structure for facility siting decisions, 90 D. von Winterfeldt /Structuring decision problems simply by tack the generalizable features of past applications [ 31. Contingency planning is another recurr ing and typical problem. Decision and Design Inc. addressed this problem in the soldiers context, but it also applies to planning for actions in the case of disasters such as Liquid Natural Gas plant explosions or blowouts from oil platforms.Substantive aspects that are characteristic of contingency planning are: strong interchange control of executive organs, numerous decisions have to be made simultaneously, major events can drastically change the focus of the problem, no cost or low cost information comes in rapidly, and organizational problems may impede information flows and actions. Although, at first glance, decision trees seem to be a natural model for contingency planning, a prototypical decision model would require modifying a strictly sequential approach to accommodate these aspects.For example, the model should be tensile enough to allow for the ‘unforeseeable’ (rapid cognitive content to change the model structure), it should have rapid information upda ting facilities without overstressing the value of information (since most information is free), and it should attend to fine tuning of simultaneous actions and information interlinkages. reckon allocation to competing programs is another typical problem. In many such problems different programs attempt to pursue similar objectives, and program mix and balance has to be considered besides the direct benefits of single programs.Another characteristic of budgeting decisions is the continuous nature of the decision variable and the constraint of the total budget. MAU looks like a natural structure for budget allocation decision since it can handle the program evaluation aspect (see Edwards et al. 1976). But neither the balance issue nor the constrained and continuous characteristics of the budget are appropriately adressed by MAU. A prototypical decision analytic structure would model an evaluation of the budget apportionment, or the mix of programs funded at particular levels.Such a structure would perhaps exploit dependencies or independencies among programs much like independence assumption for preferences. regulation covers a class of decision problems with a number of recurrent themes: three generic groups involved (regulators, regulated, [,3] I believe that. Keeney’s forthcoming book on siting energy facilities is a major step in that direction. Of. course, it could also be a step in the opposite direction. Or in no direction at all (see also first asterisked compose at the beginning of the article). D. von Winterfeldt /Structuring decision problems 91 beneficiaries of regulation), importance f monitoring and sanction schemes, usually opposing objectives of the regulated and the benefrciaries of regulation, and typically highly political objectives of the regulator. In the previous section, the more specific regulation problem of standard setting was discussed, and a prototypical decision analytic structure was suggested. A decision analytic struc ture for regulation in general can build on the main features of the standard setting model. This list could be extended to include private investment decisions, product mix selection, resource development, diagnostic problems, etc. But the four examples hopefully re sufficient to demonstrate how prototypical decision analytic structuring can be approached in general. In my opinion, such an approach to structuring could be at least as useful for the implementation of decision analysis as computerization of decision models. 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