A DYNAMIC MODEL FOR STRUCTURING DECISION PROBLEMS James Corner* Department of Management Systems Waikato Management School University of Waikato Private Bag 3105 Hamilton, New Zealand
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John Buchanan Department of Management Systems Waikato Management School University of Waikato New Zealand
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Mordecai Henig Faculty of Management Tel Aviv University Tel Aviv, Israel
[email protected]
* Corresponding Author Keywords Problem Structuring; Descriptive Decision Making; Multi-Criteria
ABSTRACT This paper develops a new model of decision problem structuring which synthesises a number of models and approaches cited in the decision making literature in general and the multi-criteria literature in particular. The model advocates a dynamic interaction between criteria and alternatives as a decision maker understands his preferences and expands the set of alternatives. This model endeavours to bridge the gap between prescriptive and descriptive approaches. It is prescriptive in its orientation, recommending an approach based on earlier prescriptive work. The model, however, is also validated empirically, based on the descriptive decision making literature and reported case studies of actual decision making.
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INTRODUCTION The structuring of decision problems is arguably the most important activity in the decision making process (Mintzberg et al., 1976; Abualsamh et al., 1990; Korhonen, 1997; Perry and Moffat, 1997). According to Nutt (1998a), the failure rate for strategic decisions in general lies at about 50%, and the implementation rate for multicriteria decision-making efforts is even worse (Kasanen et al., 2000). This suggests to us that existing decision problem structuring approaches are not entirely adequate to suit the needs of decision makers or, if they are, then decision makers are not using them. Practitioners and academics are calling for better decision problem structuring (Korhonen, 1997; Taket and White, 1997; Wright and Goodwin, 1999, to name a few); recognizing that, in general, improved decision structuring will improve the quality of the decision outcome. It is this call that motivates the work presented here.
In this paper we develop a conceptual model for multi-criteria decision problem structuring. Our starting position is consistent with that of Simon (1955), where decision makers are assumed to be intendedly rational in their decision-making. This perspective of bounded rationality accommodates the persistent departure of decision makers’ choice behaviour from strict economic rationality as a result of behavioural limitations, and is well documented (see, for example, Kleindorfer et al., 1993 or Bazerman, 1990). Also, this approach brings together our earlier work on process concepts in multicriteria decision making (Henig and Buchanan, 1996; Buchanan et al., 1998; Buchanan et al., 1999) and that of other authors, notably Wright and Goodwin (1999).
Such rational approaches to problem structuring typically involve the determination of alternatives, criteria, and attributes to measure the criteria. The goal is then to develop ways in which these components can be generated and considered relative to each other. Previous efforts at decision problem structuring (which include von Winterfeldt, 1980; Keller and Ho, 1988; Henig and Buchanan, 1996 and Wright and Goodwin, 1999) address the creation of criteria and alternatives, and present arguments about their inter-relationships in a static way. The model we present here involves the dynamic interaction of criteria and alternatives, which helps explain where these components come from and how they can influence each other in the structuring process. We offer this model as a descriptive model of problem structuring behaviour, in that it accommodates a number of empirically validated models of decision making. We further offer this model as a prescriptive model, in that it follows from the earlier prescription of Henig and Buchanan (1996), and others cited above.
The distinction, and the gap, between descriptive and prescriptive models is important and reflects the different perspectives adopted by the Psychology and Management Science research communities, respectively. The descriptive perspective focuses on how we actually make decisions and the underlying reasons for such behaviour. The prescriptive perspective considers how we should make decisions with a view to improving the quality of decision-making, both in terms of process and outcome.
This paper, then, bridges the gap between prescriptive and descriptive models of decision making. Prescriptive models lack empirical validity and often require a standard of rationality that is theoretically satisfying but practically unobtainable. Descriptive models, while rich in
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their description of real behaviour, are often context-dependent (in respect of both decision making context and decision maker cognition), thereby limiting their general applicability for prescription. One principle underlying our approach here is to draw heavily from both perspectives and synthesise them into an approach that has a well-reasoned foundation, clear empirical support and which potentially can be used in practice.
The paper is organized as follows. We first discuss several definitions of problem structuring found in the literature, then present and explain our proposed dynamic model. We next perform several validation tests of our model, which demonstrate the suitability of the model as both a descriptive and prescriptive model of decision problem structuring. Finally, we discuss several implications for the use of the model in multicriteria decision-making, and end the paper with a summary.
DEFINING PROBLEM STRUCTURING Simon (1960) proposed a three-phase decision process that continues to be the foundation for many decision making methodologies. The three phases of this well-known process model are intelligence, design and choice. Intelligence involves determining if a problem that has occurred actually requires a decision, which in turn involves some information gathering activities. Once the decision has been identified, the design phase begins – which we refer to as decision problem structuring – where alternatives, criteria and attributes are identified and considered. The final phase is choice which, based on identified criteria, describes the activity of selecting the most appropriate course of action (alternative). Since our emphasis is on multiple criteria, the identification and role of criteria in the design stage is of immediate and particular importance.
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Simon later added, but rarely discussed, a fourth stage entitled review activity, which involved reviewing past choices.
Despite the development of many models that describe and prescribe the structuring of decision problems, it remains as much art as it does science. There is no single agreed upon definition of this important aspect of the decision process, but examples include: • a search for underlying structure (Rivett, 1968), or facts (Thierauf, 1978), • an intellectual process during which a problem situation is made specific (Majone, 1980), • a process for dealing with issues (Taket and White, 1997), • the process by which a problem situation is translated into a form enabling choice (Dillon, 2000), and • the specification of states of nature, options, and attributes for evaluating options, (Keller and Ho, 1988). These examples provide general definitions of problem structuring, except for that of Keller and Ho, who state a definition most often applied in multicriteria decision making and used by us in this paper.
Woolley and Pidd (1981) propose a scheme for categorising definitions and approaches to problem structuring. Although their review of problem structuring may appear dated, their categorisation captures the philosophical differences of these approaches. Their four streams are: • A Checklist Stream, where problems are seen as failures or breakdowns, so a step-by-step procedure is sufficient to correct the problem.
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• A Definition Stream, where problems are seen as a collection of elements such as criteria and alternatives, and the goal is to put these elements, or variables, into some sort of (decision) model. • A Science Research Stream, where quantitative data is gathered in an effort to uncover the “real” problem and its underlying structure. • A People Stream, where problems are individual and/or social constructions of differing realities, so problems do not exist as concrete entities, but from different perceptions.
We give particular attention to the definition and science research streams as most approaches for structuring decision problems fall into one of these. The checklist stream addresses simple, routine decision problems, such as diagnostic checking. Soft Systems Methodology (Checkland, 1981), where multiple perspectives are considered, is one example of the last stream. In terms of streams two and three, an important distinction should be made. Problem structuring approaches that assume a particular structure a priori belong to the definition stream. Most decision problem structuring (including our proposed approach) falls into this stream. For example, Decision Analysis (Keeney and Raiffa, 1976) and AHP (Saaty, 1980) assume a particular structure of criteria (that is, an objectives hierarchy) and alternatives and seek to formulate the decision problem according to that structure. Stream three, however, is more consistent with traditional Operations Research (OR) modelling. Here, data are uncovered about the problem and, based on this data, an appropriate structure is used, typically some type of OR model. While a range of models potentially exists, the particular choice of model is determined from scientific research of the problem data; that is, it comes from the data rather than being a priori assumed. In addition to OR modelling, stream three is well exemplified by Gordon et al.’s (1975) nine generic
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problem structures, which include new product, distribution channel, acquisition, divestment, capital expenditure, lease-buy, make-buy, pricing, and manpower planning problem structures. Our proposed approach is firmly grounded in the Definition stream.
We note that while Woolley and Pidd’s People stream seems conceptually different from the others, one can argue that the other three streams might be occurring within the differing realities of the many individuals involved in a particular decision problem. That is, the first three streams might capture the different structuring approaches available, while the fourth stream recognises that not all decision makers structure a given decision problem in the same way. As we point out later, this latter statement seems to be true.
Most decision modelling/structuring, and especially multicriteria decision making, is based on a conceptualisation in terms of criteria and alternatives (Keeney and Raiffa, 1976 and Saaty, 1980). Criteria and alternatives are mutually defined, and are the fundamental components of any multicriteria decision problem. Criteria reflect the values of a decision maker and are the means by which alternatives can be discriminated. Alternatives are courses of action which can be pursued and which will have outcomes measured in terms of the criteria. Henig and Buchanan (1996) and Buchanan et al. (1998) present a conceptualisation of the multicriteria decision problem structure in terms of criteria and alternatives, with attributes as the bridge between them. More precisely, attributes are the objectively measurable features of the alternatives. Therefore, the decision problem was structured so as to separate the subjective components (criteria, values, preferences) from the objective components (alternatives and attributes), with a view to improving the decision process. This model is presented in Figure 1.
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CRITERIA
ALTERNATIVES
Objective Mapping
Subjective Mapping
ATTRIBUTES Figure 1 – Mapping Between Criteria and Attributes, Attributes and Alternatives (Buchanan, et al. 1998)
Figure 1 is a static conceptualisation, or model, of a decision problem consistent with Woolley and Pidd’s (1981) definition stream. It presents a framework comprising the components of criteria, alternatives, attributes and their interrelationships, and proposes a partition into subjective and objective aspects. In their application paper, Henig and Katz (1996) use aspects of the Figure 1 model in the context of evaluating research and development projects.
Recent discussions have occurred in the literature regarding the creation of and interrelationships between all these structuring elements. Keeney (1992) provides a way of thinking whereby criteria are identified first in the decision making process, then alternatives are creatively determined in light of such criteria and choice is then made. Known as value-focused thinking (VFT), this has been advocated as a proactive approach wherein explicit consideration of values leads to the creation of opportunities, rather the need to solve “problems.” However,
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Wright and Goodwin (1999) suggest that values are not sufficiently well formed in the early stages of decision making to be able to do this. They agree with March (1988) that values and criteria are formed out of experience with alternatives and suggest decision simulations as a way to discover hidden values and, subsequently, promising alternatives. Subsequent comments by noted researchers in the same issue as their paper appear to agree with them, but argue that such simulations might have difficulties in practice.
We call the process of first determining alternatives, then applying value and preference information to them in order to make a choice, alternative-focussed thinking (AFT), as do many others. It is clear from the descriptive decision making literature that AFT is easily the more dominant procedure (for example, Nutt, 1993). This is also the case when one considers the prescriptive multicriteria decision making literature. The majority of these prescriptive models explore decision maker values in the context of an already fixed and well-defined set of alternatives. This imbalance is not surprising. In Figure 1, alternatives are objective, and in the experience of most decision makers, they are usually concrete and explicit; they are what decision makers most often confront. In contrast, criteria are subjective, abstract and implicit, and their consideration requires hard thought. Therefore, it makes some sense descriptively to start a decision process with what is obvious and measurable, such as alternatives. This discussion gives rise to the conceptual model of problem structuring presented in the next section.
A DYNAMIC MODEL The model presented in Figure 1 merely specifies problem structuring elements and does not give
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rise to a process of problem structuring. It does not specifically address the process of how preferences can be better understood or how the set of alternatives can be expanded. How are the elements of such a structure (criteria, attributes and alternatives) generated to begin with? That is, where do alternatives come from? And where do criteria come from?
In order to address these questions, we build upon the model in Figure 1. It is difficult to determine which should or does come first – criteria or alternatives – since both are vitally important to the decision problem structuring process. However, our own casual observations of decision-making activity suggest that both generate the other interactively. This is supported by the literature. For example March (1988), as cited by Wright and Goodwin (1999), argues that experience with and choice among alternatives reveals values – and by implication, criteria. This is the thrust of Wright and Goodwin. Decision makers need to “test drive” – undertake a decision simulation of – the alternatives in order to find out what they really want.
Henig and Buchanan (1996) proposed that attributes form the bridge between criteria and alternatives and act as a translation mechanism. That is, as we soon illustrate by example, a decision maker moves from considering criteria to attributes to alternatives. This idea, together with the above discussion, gives rise to a new model of decision problem structuring involving these components. Our proposed model is presented simply in Figure 2, without attributes. The model shows criteria and alternatives joined in a sort of causal loop, with entry into the loop either using AFT (dotted line) or VFT (solid line). This interactive model states that thinking about alternatives helps generate criteria, and vice-versa. That is, neither of these two structuring elements can be thought of alone, independent of the other.
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AFT
VFT Alternatives
Criteria
Figure 2 - A Dynamic Model of Problem Structuring, Relating Alternatives and Criteria
As an illustration of the dynamic model of Figure 2, consider a car purchase decision. While driving down the road in your car, you decide it is time to replace your car. Perhaps you think that the car is old and costs a lot to repair, and it does not have the acceleration it used to have. Thus, you have begun to identify a gap in performance, between what you have and what you want. In Simon’s terminology, identification of a gap belongs in the intelligence phase; Nutt (1993), in his description of decision-making cases, suggests that all decision structuring begins with a gap. The gap in this illustration is a gap in values or criteria, which suggests that the process began with VFT, since you are loosely thinking about criteria (or, at least, discrepancies in criteria). But where did these thoughts about performance, about criteria, come from? They most likely originated from some consideration of alternatives; perhaps comparing your present car with a recollection of your car when it was new. It is therefore not entirely clear whether you necessarily started with VFT or AFT, and continuing the backward regression could justify starting with either. Perhaps where you started does not matter. However, you do start on the
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“decision journey” and you initially give your attention to either criteria or alternatives. Let us just say that in this illustration, your initial focus is on criteria.
However, as you drive your car, you begin to notice that certain other cars also look appealing, say Brand X, since they appear quick to accelerate, reasonable in price, and are about the size of your current car. In terms of our model, then, you now have moved in your thinking from the criteria oval to the alternatives oval. So, you go to the closest Brand X dealership to investigate further the Brand X cars you have observed while driving. You quickly notice that their prices generally are very reasonable and you “test-drive” a few of them; that is, you experience first hand some of the alternatives and learn more about their attributes. You discover that while they are quick and about the same size as your current car, they also seem more “tinny” than your sturdy old car. You thus start thinking about safety issues and how the modern highway is full of sport utility vehicles (which have high profiles). You deem that your small compact is no longer appropriate on the same road as such vehicles, so safety becomes a salient issue in this decision problem. By considering these particular attributes of the alternatives, you now have surfaced a new and different objective (that is, you have moved back over to the criteria oval in Figure 2).
Next you start to investigate other car brands (alternatives), and realise that there are a range of other manufacturers that address your previously stated criteria, as well as this new criterion of safety. Test-driving these new models then leads to further new criteria (such as a “roominess” or “salvage value” criterion), and so the iteration between criteria and alternatives continues. How choice ultimately is made is not our concern in this paper, but as outlined here, structuring is shown as a dynamic interaction between criteria and alternatives. It demonstrates one problem
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with just VFT, as implied in a quote by Nutt (1993, page 247), who paraphrased Wildavsky (1979), “…managers do not know what they want until they can see what they can get.” It also highlights the main problem with just AFT. AFT would require a decision maker to generate all alternatives a priori, perhaps only Brand X and a few others, then apply preferences to choose from among this set. No opportunity would have been available in such a procedure for discovering what is truly wanted in the decision problem, since values and criteria would have been considered too late in the decision problem structuring process.
As discussed earlier, how a decision maker enters into this loop may not matter. What does matter is that the previously static, unidirectional models of decision problem structuring (AFT and VFT) have been made dynamic. Consideration of alternatives helps identify and define criteria, while criteria are shown to help identify and define alternatives. If such interaction is allowed to occur, divergent thinking and creativity is explicitly incorporated in the decision making process, which is considered desirable for the process (Volkema, 1983; Abualsamh, 1990; and Taket and White, 1997). While convergent thinking might be more efficient, divergent thinking can lead to more effective decision making.
This dynamic model has, thus far, been motivated both prescriptively (as a recommendation to decision makers) and descriptively (through the car purchase illustration). The model was first argued from a theoretical position (belonging to the Definitions stream of Woolley and Pidd (1981), where a structure has been a priori assumed). Further, it was implicitly presumed that structuring the decision problem is, in fact, desirable and the use of this decision structuring model by a decision maker will aid solution. However, arguing from a theoretical position has
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shortcomings; although the logic may be impeccable, real decision makers generally find prescriptive models to be unworkable, usually because the foundational assumptions or axioms have little empirical validity. In other words, it is a nice idea that does not work in practice. As others and we have noted, the multicriteria landscape is littered with many such models, consigned to obscurity because they are not relevant to real world decision making. They lack empirical validity and do not explain decision making behaviour.
The car illustration leads us into the issue of descriptive or empirical validation. There are at least two issues to be considered. First, how does descriptive decision making theory and data fit with the dynamic model we have proposed? And second, is any of this behaviour actually successful when it comes to decision outcomes? We endeavour to demonstrate that the dynamic model has empirical validity in that it accommodates recognised models and data in the descriptive literature. We also show that the descriptive decision making models closest in spirit to the dynamic model are successful in practice.
VALIDATING THE MODEL DESCRIPTIVELY We now consider the empirical or descriptive validity of the dynamic model. This section presents validation tests separately below.
Known Theories and Models The first test determines whether or not our model accommodates current descriptive theories and models of decision making. There are many descriptive theories of decision making (for taxonomies of descriptive models see Schoemaker, 1980; Lipshitz, 1994; Perry and Moffat,
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1997; Dillon, 1998). Many descriptive models deal with the entirety of the decision making process (Mintzberg, et al., 1976; Nutt, 1984) or just with the choice phase (Einhorn, 1970; Tversky 1972). However, we pay particular attention here to those decision making models that on the surface concentrate mainly on decision problem structuring. Consequently, we consider three of the more enduring and valid models in the descriptive literature, relative to our dynamic model: Recognition-Primed Decisions (Klein, 1989), Image Theory (Beach and Mitchell, 1990) and the Garbage Can Model (Cohen et al., 1976).
The first step in the Recognition Primed Decisions model (Klein, 1989) is to recognize that the current decision situation has some similarity with past decision situations. Thus, prior experience is drawn upon to guide the current decision making procedure. The model then states that the decision maker examines goals, cues, expectations and actions in order to gauge the current situation relative to past situations. This is done through a mental simulation process, which attempts to project past experience with alternatives into the future to see how this experience might be used in the current situation. This involves a sequential, dynamic, mentally simulated generation and evaluation of alternatives. During this simulation exercise, alternatives can be modified, and goals and expectations can become revised, in order to make the current situation match with some useful experience, thus enabling a direction in which to proceed with the decision problem.
Image Theory (Beach and Mitchell, 1990) is similar to Recognition Primed Decisions in that it draws on the decision maker’s experience with decision making when trying to decide what to do in the present situation. In this model, the decision maker’s view is represented by three
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different images (or criteria) in order to evaluate alternatives. That is, an alternative-focused process is begun and an alternative is assessed to determine its ‘fit’ with the decision maker’s value, trajectory, and strategic images. The fit is determined through comparing the attributes of the alternatives with the decision maker’s images. If the alternative ‘fits,’ that is, it exceeds certain threshold values for the attributes, then it is adopted. Otherwise it is rejected. Finally, any appropriate choice procedure is then used to choose among the alternatives that fit. Without going into the complexities of this theory, initially the images are considered as fixed. If the outcome is that all alternatives are rejected, then the thresholds may be revised or, perhaps, revised images are developed. However, this further consideration of images, or criteria, only occurs when all alternatives are initially rejected. If acceptable alternatives are found that fit with the images, then little or no effort is directed toward considering new criteria.
Both Recognition Primed Decisions and Image Theory contain aspects of the dynamic model of Figure 2, with some interaction of alternatives and criteria (and goals, expectations, and actions). Clear attention is given to criteria and alternatives, and one is shown to influence the development of the other. In the case of Recognition Primed Decisions, alternatives are seen to influence goals, and perhaps new alternatives are surfaced as a result. In Image Theory, if all alternatives are rejected, new images/criteria may surface, which in turn, might lead to a new set of alternatives. Both of these models appear to be alternative focused, and the interaction between alternatives and criteria seems to be limited to a single iteration between the two.
The Garbage Can Model (Cohen et al., 1976) is a model of organizational decision-making, although it also applies at the level of the individual. This model says that a rational search for
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alternatives does not occur, since organizations are really full of anarchy and chaos. Rather, solutions are randomly identified, and then one is matched to an appropriate and compatible decision problem in need of a solution. No explicit mention is made of where criteria and attributes might fit into this scheme, and it remains classified by us as being an alternativefocused approach to decision making. This well-known model of decision-making has little correspondence with our model in Figure 2.
To summarize this sub-section, we feel that at least two of the currently popular descriptive models of decision-making (Recognition Primed Decisions and Image Theory) provide empirical support for our model. That is, there are elements of these two models that indicate that decision makers develop some sort of interaction between criteria and alternatives, which forms the basic concept underpinning our model. This interaction, however, is not necessarily valid for all descriptive models of the decision making process. One might argue that the Garbage Can Model has no real structuring phase at all, so no current model of problem structuring would seem to fit this model.
Empirical Evidence Our second validation test determines if empirical evidence seems to be explained by our model. We use two previously published large sample studies of decision cases involving real managers, both by Nutt (1993, 1999).
The first study considers problem formulation processes involving real decision cases and managers (Nutt, 1993). The author defines formulation as a “process that begins when
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signals…are recognized by key people and ends when one or more options have been targeted for development” (page 227). Based on previously stated definitions, we consider this to be nearly equivalent to problem structuring. By examining traces of 163 decisions, four types of formulation process were generated: Issue, Idea, Objective-Directed, and Reframing. These four processes are briefly summarized below.
The least successful process, as measured by a multidimensional metric measuring each decision’s adoption, merit and duration, is the Issue process. This process is present when an issue is identified and considered by the decision maker(s). Decision makers become problem solvers as they suggest solutions to concerns and difficulties. Used in 26% of the 163 cases, this is similar to Woolley and Pidd’s (1981) Checklist stream of problem structuring.
Third most successful and used in 33% of cases is the Idea process. Here, an initial idea usually a solution - is presented by a decision maker, which provides direction for the decision making process. Formulation proceeds as the idea’s virtues are discussed and refined. Second most successful and used in 29% of cases is the Objective-Directed process. In this formulation process, direction is obtained by setting objectives, goals, or aims early in the process, along with a freedom to subsequently search for new alternatives.
Finally, most successful and used in only 12% of all cases is the Reframing process. This process focuses on solutions, problems, or new norms or practices in order to justify the need to act, thus avoiding Raiffa’s (1968) “errors of the third kind.” That is, the decision problem is re-
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formulated into a problem that the decision makers are confident they can handle, based on some new direction.
In comparing our model of Figure 2 to Nutt’s four types of formulation process, some observations can be made. The Issue and Idea processes, which comprise 60% of the decision cases examined by Nutt, appear to be alternative-focused with no dynamic interaction and little explicit consideration of criteria. Together they are the most used but least successful. The Objective-Directed process appears to be value-focused, including explicit consideration of goals and criteria and search for alternatives. It also performs better that the previous two processes. However, a better descriptive process is available.
The Reframing process, least used but most successful, incorporates more aspects of our model that the other three, when viewed closely. The details of the Reframing process show that while solutions to apparent problems are presented early in the process, new norms and criteria grow out of thinking about these solutions and their underlying problems. Based on this revised set of norms and criteria, other alternatives are then sought. This approach to problem formulation is thought to “open the decision process to possibilities before the narrowing that occurs during problem solving” (Nutt, 1993, p. 245). This divergent and creative approach to problem formulation seems close in principle to our conceptual model of dynamic interaction between criteria and alternatives.
Reframing can be further illustrated as we extend our car example. Assume that thinking about the large size of the sport utility vehicles led you to reflect upon even larger vehicles, say, city
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buses. You then start to realise that what you really are after is just a way to commute to work. Thus, your thinking process has led to a reframing of the problem, not as a car selection problem, but as a transportation problem. The “real” issue was the best choice of transport to and from work; this reframing greatly enlarged the set of alternatives (to mass transportation) and introduced other criteria (comfort, time and cost savings saved by commuting, etc.).
As discussed earlier in this car purchase/transportation illustration, it remains unclear as to whether one begins with criteria or alternatives. Once started, thinking only about alternatives early in the process, as found in the Issue and Idea processes, appears to lead to relatively unsuccessful decisions, while thinking about objectives and criteria early (Objective-Directed process) is more successful. However, the most successful process (Reframing) also considers alternatives early, and then uses them to identify new norms and criteria. It appears that the starting point matters less than the need to ensure some iterating between criteria and alternatives.
Our second validation test in this sub-section involves Nutt’s (1999) study involving 340 strategic level decisions from as many organizations (a sample of public, private, and third sector U.S. and Canadian organizations). In this study, tactics used to uncover alternatives were determined, as was the success of the decision-making effort, given such tactics. Also, the type of decision was considered (that is, technology, marketing, personnel, financial, etc., decisions, to name four of the eight found in the study) in light of the success of the different tactics. Thus, this second study of Nutt concentrates on aspects of decision problem structuring that are related
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to our proposed model. The empirical evidence presented in this study can be used to determine the extent to which our model fits decision makers’ behaviour descriptively.
Nutt’s study found six distinctly different tactics used to uncover alternatives, namely: Cyclical Search (used in 3% of the 340 decisions studied), Integrated Benchmarking (6%), Benchmarking (19%), Search (20%), Design (24%), and Idea (28%) tactics. Cyclical Search involves multiple searches and redefined needs, dependent on what is found during the search. The two Benchmarking tactics involve adaptation of existing outside practices and Search involves a single request-for-proposal type search, based on a pre-set statement of needs. Design involves generating a custom-made solution, and Idea involves the development of pre-existing ideas within the organization.
In comparing these six tactics with our model, we note that Integrated Benchmarking, Benchmarking, and Idea tactics all seem to be alternative-focused processes while Search and Design seem to be value-focused. Furthermore, the Idea tactic resembles the Garbage Can Model presented earlier, as Nutt himself points out (Nutt, 1999, page 15). Interestingly, Cyclical Search seems to fit surprisingly well with our concept that a dynamic relationship should (or does) exist between criteria and alternatives. As stated by Nutt (page 17), “Decision makers who applied a cyclical search tactic set out to learn about possibilities and to apply this knowledge to fashion more sophisticated searches. Each new search incorporated what has been learned in past searches.” Upon close inspection, this tactic also displayed elements similar to Image Theory in its application.
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What is more interesting is the success of each tactic. Using the same multidimensional success metric as he used in his 1993 formulation study reported above, Nutt found Cyclical Search and Integrated Benchmarking to be the most successful of the six tactics, with the former more successful than the latter. His explanation for the success of these two is based on the fact that they generated more alternatives than the other four tactics, they were not subjected to hidden motives as often, and time was not wasted trying to adapt sub-optimal solutions. Furthermore, these two successful tactics encouraged learning and did not lead to a premature selection of a course of action. The same might be said for a decision process utilizing our problem structuring model. Learning about what a decision maker values in a decision problem should stem from knowledge and experience with alternatives, and learning about creative alternatives should occur as one considers one’s values and criteria. Taking the time to let each of these structuring elements inform the other also should keep the decision process from “settling” prematurely.
In summary, this section provides empirical support for Figure 2 as a descriptive model of the problem structuring process for some decision makers. That is, Nutt’s (1993) Reframing formulation process and Nutt’s (1999) Cyclical Search alternative uncovering process each seem to be similar to our model. However, we note that each of these processes were the least used in Nutt’s two studies. This is not surprising. It is well known that when using iterative processes, a decision maker’s tendency toward satisficing often results in premature termination – this is what satisficing is. In practice, decision makers seem to stop early and consequently do not gain the benefits from that extra iteration.
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Also, since these two models of Reframing and Cyclical Search were the most successful in Nutt’s studies, this suggests that Figure 2 has potential as a good prescriptive model for the decision problem structuring process.
DISCUSSION Our discussion is motivated by the following quote: “A manager should work on integrating his formal models of rational decision making with his intuitive, judgmental, common sense manner of solving choice problems and seek to adapt the former to fit the latter, rather than submit to a bastardization of his intuition in the name of some modern mathematical technique.” (Soelberg, 1967, page 28)
Watson (1992) and Dando and Bennett (1981), among others, reject the supremacy of the rational model of decision making as being impractical and at odds with actual decision making behavior. The tendency for decision makers to discard values and even attributes is well documented. French (1984) and Kasanen, et al. (2000) argue that the assumptions assumed by many methods may not agree with the practical findings of behavioral science. It should not be surprising, then, that some non-quantitative methods, apparently without any rational basis, attract the attention of decision makers. This suggests that there is a demand for decision making tools provided they are tailored to actual decision maker behavior. Furthermore, Henig and Buchanan (1996), conclude that the decision making process should not focus on selection, which is at the heart of the rational decision making paradigm, but rather involve understanding and expanding. They advocate a shift in the rational model of decision making and not a total
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rejection of it. The goal of finding “the right answer” is not what decision making should be all about.
The reader may now justifiably ask, "so which method do you recommend for the decision maker who needs advice?" Our answer is: clearly, any method that endeavors to understand and expand - in this case, as applied to alternatives and criteria. Figure 2 shows how this is done; criteria not only evaluate the alternatives but also generate them. Alternatives are not only to be ranked and selected by criteria, but they also reveal criteria. This paper does not suggest a method for doing this in detail, but we have outlined here the theoretical, empirically grounded, foundation for decision problem structuring. This shifts the focus of methods away from techniques of selection, which are too technical, and should attract at least a few decision makers.
We reserve the development of a method for doing this for future research. Certainly, the decision simulation ideas mentioned by Wright and Goodwin (1999) could be part of this development, and the literature on creativity should contribute as well (for example, Altshuller, 1985). However, we anticipate that decision makers should iterate between alternatives and criteria, at least twice, regardless of where they enter the loop. Certainly, more experienced decision makers might have an established history of such iterations, and they may not need as many new iterations as those less experienced. As Simon (1956) has pointed out, the observed behavior of decision makers is to satisfice. In terms of our model, this means they perform only one iteration and prematurely stop the decision making process. The entire point of our model is that they can do better in their decision making if they perform two or more iterations.
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We have restricted our discussion to the field of multi-criteria, since most real-life problems involve more than a single criterion. We note that our model, which attempts to allow decision makers to understand and expand their thoughts regarding criteria and alternatives, is similar to ideas mentioned by others in this field. Vanderpooten (1992) argues that in any multi-criteria procedure, iteration should, or perhaps does, occur between two phases of the decision making process: learning and search. He states that decision makers do not have well formed preferences when facing a decision problem. Preference structures become better formed as decision makers relate past experiences and present explorations of alternatives to the decision problem at hand. He further states that the decision process is an iterative sequence of such activity, which appears conceptually similar to what we propose in this paper. Also, Kasanen, et al. (2000) discuss how decision alternatives are not well developed during most decision processes and therefore need to be developed further using creative methods. They further discuss how not all decision-relevant criteria are known by the decision maker at the start and need to be discovered during the decision process. We think that our model helps address these problems in a practical way.
SUMMARY This paper offers a new model of decision problem structuring that highlights the interactive and dynamic nature of criteria and alternatives. It is offered as both a prescriptive and descriptive model. It is validated as a descriptive model of structuring behaviour based on well-accepted theories of decision making behaviour, as well as on large sample empirical studies of the problem structuring process. The model is also prescriptive, based on the success of similar structuring behaviours found in the large sample empirical studies, as well as having extended
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Henig and Buchanan’s (1996) prescription involving understanding and expanding in the decision process. New methods for multi-criteria should consider building in mechanisms that allow for criteria to generate new alternatives, and in turn, new alternatives to help re-define the set of criteria.
We caution, however, as does Watson (1992), that there probably is no single best problem structuring method for all decisions. The influence of contextual factors on decision process is significant (Dillon, 1998; Nutt 1999), as is the initial frame taken by the decision maker (Nutt 1998b). These and other influences should be studied further as they impact the problem structuring process. In a world where the environment is chaotic, wants and desires are constantly changing, and alternatives appear before decision makers perhaps randomly, problem structuring methodologies need to be made more dynamic.
Acknowledgements This project was financed by grants from The Leon Recanati Graduate School of Business Administration and the Waikato Management School. We also acknowledge The Arizona State University, where the first author was on sabbatical leave during much of the writing of this paper.
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CRITERIA
ALTERNATIVES
Objective Mapping
Subjective Mapping
ATTRIBUTES
Figure 1 – Mapping Between Criteria and Attributes, Attributes and Alternatives (Buchanan, et al. 1998)
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AFT
VFT Alternatives
Criteria
Figure 2 - A Dynamic Model of Problem Structuring, Relating Alternatives and Criteria
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