Encyclopedia of Decision Making and Decision ...

Encyclopedia of Decision Making and Decision Support Technologies Frederic Adam University College Cork, Ireland Patrick Humphreys London School of Economics and Political Science, UK

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Decision Support and Problem Formulation Activity David Paradice Florida State University, USA

INTRODUCTION While decision choices are certainly important and warrant appropriate attention, early stages of the decisionmaking process may be even more critical in terms of needing adequate support. The alternatives from which a decision maker may be able to choose are integrally tied to the assumptions made about the problem situation. Consequently, decision support systems (DSSs) may be more effective in helping decision makers to make good choices when support for problem formulation is provided. Research validates the notion that support for problem formulation and structuring leads to better decisions. This article explores this concept and looks at opportunities in emerging software trends to continue development of problem formulation support in DSS-type settings.

BACKGROUND From its inception, the domain of DSS has focused on providing technological support for decision-making processes in ill-structured environments. Simon's (1977) model of decision-making processes has been a cornerstone of DSS design since the inception of the decision support movement. Simon outlined four processes that he believed account for most of what executives do: The first phase of the decision-making process— searching the environment for conditions calling for decision—I shall call intelligence activity (borrowing the military meaning of intelligence). The second phase—inventing, developing, and analyzing possible courses of action—I shall call design activity. The third phase—selecting a particular course of action from those available—I shall call choice activity. The fourth phase, assessing past choices, I shall call review activity. (Simon 1977, p. 40) Human nature being what it is, the success or failure of choices made in particular decision-making situa-

tions often gets the most attention. The early days of the DSS movement implicitly focused most heavily on the choice phase of Simon's model. At the beginning of the DSS movement, DSSs were still constructed from programming languages such as FORTRAN (formula translator) or PL/1 (programming language 1), although DSS environments containing interactive modeling languages were soon developed. In these environments, construction of the model that would form the basis of the decision process often fell on technical experts with little or no direct stake in the decision outcome. These experts simply translated a model specification into the appropriate programming code and returned a "system" to the ultimate decision makers. The actions of the decision makers involved executing the model, typically with varying combinations of input values, so that various scenarios could be examined to determine which set of input values led to the most desirable outcome. In other words, the function of the user was to choose one of several alternatives. In some cases, claims were made that the users had designed a solution and consequently that the DSS had supported the design stage of Simon's model. Closer examination, however, shows that the design stage of Simon's model was executed in the specification of the model to be programmed. The power of the model was well documented in the work by Pounds (1969). Pounds learned that problem finding is essentially the recognition of a difference between reality and what a decision maker expected, where expectations were typically based upon some preexisting model. The model may be the decision maker's own mental model, based on historical events orpersonal experience, oritmay be amodel constructed by someone else. Regardless of their origin, the models used by decision makers were critical in their efforts to recognize and address problems. Pounds found that even though business models were quite naive compared to decision-making models in scientific domains, model-building techniques were making significant contributions to management effectiveness.

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Decision Support and Problem Formulation Activity

Models are comforting because they provide a means of removing uncertainty. Humphreys and Berkeley (1985) note seven types of uncertainty in the process of conceptualizing decision problems. Decision theory can adequately account for only four of the uncertainty types. These uncertainties are primarily related to the likelihood of outcomes and events. Procedural uncertainty, such as specifying the relevant issues, what information to seek, and how to structure it, is not addressed by decision theory. When a decision model is constructed, much of the procedure for attacking the problem is then specified. One collects the appropriate data, executes the model, and assesses the results. These activities are much less cognitively straining than the construction of the model. Of importance here is that these models, once specified and constructed, rarely have been examined at later times to determine whether they remain accurate models of reality. Decision makers (typically managers) specify a model to be constructed based on their experiences and perceptions, and programming professionals translate this specification into a functioning DSS. Once a model is producing acceptable results, rarely has anyone asked later, "Is this model still correct?" The assumptions underlying the model specification have been assumed to be accurate still. This is a critical aspect of DSS, for the alternatives from which a decision maker may be able to choose are integrally tied to the assumptions made about the problem situation. Because decision makers "satisfice" (Simon, 1976), they will naturally be driven to consider ranges of feasible alternatives rather than choosing maximizing or optimizing behavior. Simon identified premises (i.e., assumptions) as the most fundamental unit of analysis in decision making. According to Simon, the premises that one recognizes are the most relevant to a decision situation. These control the alternatives considered. Consequently, premises dictate behavior. Schein (1985) has concluded that understanding a culture and a group's values and overt behavior requires understanding the underlying assumptions. These are typically unconscious but actually determine how group members perceive, think, and feel. Churchman's (1971) examination of inquiring systems most clearly illustrates the fundamental dependence that models have on assumptions. Churchman developed the notion of inquiring systems—systems that create knowledge—by examining the design of

such systems based on the philosophies of five Western philosophers. Beginning with Liebnitz and working through the philosophies of Locke, Kant, Hegel, and Singer, Churchman showed that the basic assumptions regarding how knowledge is created drive all other aspects of the system. In the Liebnitzian system, formal logic is the guarantor of knowledge. Consequently, inputs to the system must be well formed and amenable to formal rules of logical conclusions. Lockean systems depend on consensus; therefore, agreement on labels and properties of objects becomes critical. Kantian systems allow for multiple realities, with the best fit of data to model determining how conclusions are drawn. Hegelian systems depend on the dialectic. It is in these systems that overt examination of the assumptions of different realities occurs. Singerian systems rely on continual measurement and a "sweeping in" of new model variables to refine models. Churchman's students have certainly recognized the importance of assumptions. Mason (1969) and Mitroff, Emshoff, and Kilmann (1979) were early leaders in recognizing the need to identify assumptions in models. Mason recommended dialectic processes as a way to surface assumptions for review and reconsideration. He suggested this process could lead to the identification of new and relevant assumptions that strategic planners should consider. Mitroff and his colleagues demonstrated that Churchman's and Mason's ideas formed a good basis for formulating ill-structured problems. Another of Churchman's students, Russell Ackoff (1981), has argued that examination of the models that are developed in decision-making situations leads to important and valuable knowledge. He argued that it is precisely due to making explicit that which is not normally made explicit (i.e., the assumptions of the model) that improvement of the decision-making system is possible. The assumptions should be made open for examination and criticism by decision makers and other researchers. Later, Mitroff and Linstone (1993, p. 15) built on Churchman's work to define a "new way of thinking." They argue for explicit consideration of multiple realities when dealing with complex problems. Their basic premise is that no one perspective of a complex situation will ever embody all of the assumptions of all of the stakeholders involved. The importance of assumptions is not espoused solely by Churchman and his students. Huber (2004, 193


p. 72) also implicitly recognizes the importance of assumptions. He states that in the future, a firm's survival will depend on its ability to "rapidly and effectively sense and interpret environmental changes." Intuitively, decision-making models that are not revised to reflect environmental changes will be based on the wrong assumptions, with consequently poor support for the decision-making processes they are intended to support. One can recognize that assumptions are most prevalent in the process of formulating the structure of a problem model. However, what empirical research exists to verify this preconception? In order to investigate support for problem formulation in DSS, a series of studies was executed under the direction of James Courtney (Courtney & Paradice, 1993). This research was grounded in the fields of cognitive psychology and systems engineering. From the work in cognitive psychology, problem formulation was determined to consist of three phases: problem identification, problem definition, and problem structuring. Problem identification occurs when the need for a decision is perceived by the decision maker. When characterized as a problem situation, this need is typically perceived in the context of some perceived deviation from expectations. However, an opportunity could also create the need for a decision, so one should be careful to avoid the connotation that a problem situation is necessarily a bad situation. Problem definition involves determining the relevant properties of the situation. Problem structuring examines the problem situation to determine a strategy for addressing the situation.

HOW DSS CAN SUPPORT PROBLEM FORMULATION Studies have shown that a critical part of problem solving is developing the problem structure (Abualsamh, Carlin, & McDaniel, 1990; Gettys, Pliske, Manning, & Casey, 1987; Mintzberg, Raisinghani, & Theoret, 1976; Mitroff & Featheringham, 1974). Consequently, some effort to support decision makers in this part of the decision process through appropriate DSS features should be valuable. An initial DSS study that examined this argument was executedby Kasper(1985). Kasper had participants compete in a business-simulation gaming environment. The business simulation provided a complex 194

decision-making environment, requiring participants to make up to 56 different decisions in each decision period. The game's outputs took into consideration the decisions made by all participants in determining how any one participant's decision inputs were processed. Participants had access to a database management system that provided access to their corporate data. It also provided access to competitors' data, but in such a way that mimicked the equivocal nature of acquiring information in real business environments. Kasper (1985) divided his participants into three groups. The control group simply played the game using the database as they desired to make the best business decisions they could make. A second group was required to create a model of the decision environment. This group was required to specify the variables that they perceived as important to their decision-making process as well as the relationships between those variables. The third of Kasper's groups also created a model of the decision-making environment. However, unlike the second group, this group was required to actually use their model in their decision-making process. Kasper's (1985) results indicated that the decision makers who built models outperformed the decision makers who did not build models. Furthermore, the decision makers who used the models they built outperformed the decision makers who only built the model but did not use it. Pracht (1986) and Pracht and Courtney (1988) used Kasper's (1985) results as a starting point for their investigation. Additionally, they built upon work that showed that diagrams and images were useful in the problem formulation process. They hypothesized that participants with high spatial ability (i.e., a high degree of comfort with spatial orientation) would benefit from graphics-oriented problem structuring tools. They designed a 2x2 factorial design experiment where participants were categorized as having high or low spatial ability. The second factor isolated access to the graphics-oriented problem structuring tool. All participants were required to draw a structural model of the business simulation environment. The participants without access to the graphics-oriented tool drew their models by hand. The results indicated that participants with high spatial ability who had access to the graphics-oriented problem structuring tool more closely formulated the correct structure of the business simulation environmentthan any other group. Participants with low spatial


ability performed about the same regardless of having access to the tool. Notably, the participants with high spatial ability who did not have access to the tool performed the worst. Loy (1986) extended these results to small-group environments. He used the same tool in two group-process environments. Some groups used the nominal group technique to structure their decision-making processes. The other groups were given no specific process to follow. To investigate whether spatial ability was again a factor, Loy computed a group spatial-ability score. Loy's results confirmed those obtained in the previous study. Groups with access to the graphics-oriented problem structuring tool outperformed groups without access to the tool. Groups with high spatial-ability scores who had access to the tool outperformed all other groups. Groups with high spatial ability without access to the tool performed the worst. The process factor was not significant. There were no significant results for any factor when groups using the nominal group technique were compared to those using no specific process. Ata Mohammed, Courtney, and Paradice (1988) returned to the tool developed by Pracht and Courtney (1988) and focused their work there. They modified the tool to create causation trees, which are hierarchical diagrams similar to structural models. In this causation tree, the root node is a problem for which potential causes are sought. The branches of the tree represent paths from the problem to variables contributing to the base causes of the problem. The goal was to create an environment of semiautomated problem-structuring decision support. Ata Mohammed et al. (1988) tested the new tool by taking models developed by Pracht and Courtney's (1988) participants as inputs to their DSS and checking the veracity of the base causes identified by the causation trees. The testing of the new tool inadvertently provided additional evidence for the importance of examining the assumptions upon which models are built. The tool was very sensitive to the accuracy of the model, and in an absolute sense the models developed by some of Pracht and Courtney's participants were not very good. Many of these models suffered from cognitive biases held by the participants. For example, the participants often believed that increasing a salesperson's commission would lead to increased sales. In the simulation, however, there is a point at which salespeople become comfortable with their annual salary and no amount of increased commission leads to more sales. In fact, in-

creasing the commission can lead to fewer sales because fewer sales are required for the salespeople to achieve their desired level of income. Thus, even though the tool helped users formulate more accurate models, these models were not accurate enough to provide the basis for semiautomated problem-structuring support. Such support was investigated by Paradice and Courtney (1987). They developed a new version of the DSS software developed by Ata Mohammed et al. (1988). This version attempted to control for the cognitive biases introduced into the models by the users through the use of linear and higher order statistical models. Rather than take the models at face value, Paradice and Courtney's system automatically examined the database created by the business simulation environment and statistically tested the relationships specified in the model. Early testing of Paradice and Courtney's (1987) system exposed the need for an extended taxonomy of business-variable relationships. The system did not improve upon Ata Mohammed et al. 's (1988) approach when working with the simple notion of causality. However, incorporation of the extended taxonomy, in which variables could be described as redefining other variables—acting as upper and lower bounds on other variables, correlated with othervariables, and participating in other relationships—began to lead to improved results. The addition of more sophisticated statistical analyses, such as the ability to conduct path analysis, further improved systemperformance. Finally, Paradice and Courtney constructed a manner for the system to report its confidence in its outputs. This system was trained using Pracht and Courtney's (1988) participants' models. Models that passed statistical tests were stored in a knowledge base for use by an advisory module later. Paradice and Courtney's (1987) system was the first to provide explicit support for changing business environments through the use of its "rejection base." The rejection base was a knowledge base of proposed relationships that did not pass statistical analysis. Recognizing that relationships may be invalid now but valid later (or vice versa) due to changing business conditions, the system allowed the user to store the hypothesized relationships in the rejection base even when statistical support did not exist. The final validation of the system demonstrated that the system performed as well as the participants when the participants' models used to train the system were accurate. Notably, the system also outperformed 195


the participants in other areas, particularly those where the participants' cognitive biases were evident. Paradice and Courtney's (1987) system was a precursor to what would today be called a knowledge management system. It also contained rudimentary data mining capabilities, capabilities that would be extended in the next study conducted by Billman (1988). Billman extended Paradice and Courtney's system's advisory module and statistical capabilities by developing capabilities to discover relationships. Billman implemented three algorithms for assessing the strength of relationships between variables. She tested her system by allowing it to discover the relationships in the business simulation environment that was the basis for the studies that came before hers. When allowed to function without human intervention, Billman's system found 75% of the relationships implemented in the underlying business simulation game related to a target variable (cost of goods sold), but it also discovered six spurious relationships. Billman also tested her system using a composite model built from four of the best human-participant models constructed by players in the simulation game. Using this approach, her system discovered only 60% of the relationships involving the target variable, but it found no spurious relationships. As these studies were under way, the field of group DSS (GDSS) was also beginning to see much activity. Over time, the GDSS field work merged into the domain of computer-supported collaborative work (CSCW). However, much of the research in CSCW can be interpreted as efforts to focus DSS effort on problem formulation activities. Many of the GDSS and CSCW system characteristics support problem formulation and structuring. Anonymous brainstorming capabilities allowed GDSS users to suggest factors believed to be relevant to problem situations. Ranking and rating capabilities allowed the group to work collaboratively to define problem structures. Process techniques provided rules to assist meeting participants in attacking a particular problem well. GDSS and CSCW studies focused on a wide range of dependent variables, but many of them are central to problem formulation and structuring activities. For example, Hartwick, Sheppard, andDavis (1982), Shaw (1981), and Stasser (1992) examined ways that individuals shared information that was held individually but not necessarily known to the group. Dennis, Valacich, 196

andNunamaker (1991) showed that shared information could be used synergistically by the group in ways that exceeded how the individuals would use privately held information. Shaw, and Laughlin, Vander Stoep, and Hollingshead (1991) showed that collective evaluation was more objective than individual judgment, leading to reduced bias in the problem formulations. As the collaborative nature of work has become more widely recognized and supported, DSS researchers have begun to investigate more sophisticated means of supporting problem formulation. In the process, they have also indirectly shed some light on the least investigated phase of Simon's model: the review phase. Hall and Davis (2007) have revisited the work of Mason, Mitroff, and others. They recently report on the testing of a system designed to encourage decision makers to consider others' perspectives. Whereas Mason and his colleagues focused on Hegel's philosophy and dialectic processes, Hall and Davis have focused on Singer's philosophical concept of sweeping in new perspectives of a problem situation. Hall and Davis discovered that whenparticipantsconsiderperspectives that differ from their own innate system of values, they change their behavior as relates to weakly held value positions. Hosack (2007), building on Hall and Davis's work, has found that feedback in different forms can effect similar behavioral changes. Both of these studies force the users of these systems to review their prior decisions. These results suggest that in the absence of active support, decision makers will follow their own heuristics that may lead to biased problem formulations. When DSSs provide means for actively considering problem structures, reviewing one's prior decisions, and considering how others may see a problem differently, decision-making behavior changes. There can be little doubtthat effort spenton defining aproblem's structure helps in the process of solving that problem.

FUTURE TRENDS Fortunately, today's systems are creating environments that naturally support collaborative problem structuring activities if DSS designers will only take advantage of them. Instant-messaging software, discussion boards, and chat rooms all provide environments conducive to soliciting multiple perspectives on problems, thus naturally creating the Singerian system of sweeping in new views of a problem situation. DSS designers must


investigate ways of leveraging these environments in new DSS designs. The electronic nature of these environments makes them amenable to incorporation into new DSS designs. For example, online archives of discussion boards could provide a new type of knowledge base to be accessed by acomputer-basedproblem-structuring decision support environment. These archives often contain information on how to solve specific problems. In fact, frequently there are multiple approaches described. Chat rooms and discussion boards may one day also provide immediate test environments for decision makers. In the same sense that spreadsheet technology led to scenario analysis in the early days of the DSS movement, chat rooms could provide decision makers with immediate responses to proposed alternative courses of action. This would be a type of scenario analysis that could exceed the benefits of even the best simulation because the audience would consist of the actual stakeholders in the problem situation.

CONCLUSION As long as decision makers struggle with defining the problem they are trying to solve, problem formulation support in DSS will be valuable. When decision makers solve problems without giving adequate attention to the structure of the problem being faced, they run the risk of developing ineffective solutions. In the worst case, they solve the wrong problem completely. Research demonstrates that DSS support for problem formulation activities provides benefits, yet little continues to be done to exploit this finding in the development of new DSSs or new decision support environments. DSS designers should strive to always look for new opportunities to improve this aspect of decision support.

REFERENCES Abualsamh, R. A., Carlin, B., & McDaniel, R. R., Jr. (1990). Problem structuring heuristics in strategic decision making. Organizational Behavior and Human Decision Processes, 45, 159-174. Ackoff, R. L. (1981). Creating the corporate future. New York: John Wiley & Sons.

Ata Mohammed, N., Courtney, J. R, Jr., & Paradice, D. B. (1988). A prototype DSS for structuring and diagnosing managerial problems. IEEE Transactions on Systems, Man, and Cybernetics, 18(6), 899-907. Billman, B. (1988). Automated discovery of causal relationships in managerial problem domains. Unpublished doctoral dissertation, Department of Business Analysis & Research, Texas A&M University, College Station, TX. Churchman, C. W. (1971). The design of inquiring systems. New York: Basic Books, Inc. Courtney, Jr., J.F. and Paradice, D.B. (1993). Studies in Managerial Problem Formulation Systems. Decision Support Systems, 9(4), 413-423. Dennis, A. R, Valacich, J. S., & Nunamaker, J. F. (1991). A comparison of laboratory and field research in the study of electronic meeting systems. Journal of Management Information Systems, 7(3), 107-135. Gettys, C. F., Pliske, R. M., Manning, C., & Casey, J. T. (1987). An evaluation of human act generation performance. Organizational Behavior and Human Decision Processes, 39, 23-51. Hall, D., & Davis, R. A. (2007). Engaging multiple perspectives: A value-based decision making model. Decision Support Systems, 43(4), 1588-1604. Hartwick, J, Sheppard, B. H., & Davis, J. H. (1982). Group remembering: Research and implications. In R. A. Guzzo (Ed.), Improving group decision making in organizations: Approaches from theory and research (pp. 41-72). New York: Academic Press. Hosack, B. (2007). The effect of system feedback and decision context on value-based decision-making behavior. Decision Support Systems, 43(4), 1605-1614. Huber, G. P. (2004). The necessary nature of future firms. Thousand Oaks, CA: Sage Publications. Humphreys, P., & Berkeley, D. (1985). Handling uncertainty: Levels of analysis of decision problems. In G. Wright (Ed.), Behavioral decision making (pp. 257-282). New York: Plenum Press. Kasper, G. M. (1985). The effect of user-developed DSS applications on forecasting decision-making performance. Journal of Management Information Systems, 2(2), 26-39. 197


Laughlin, P. R., Vander Stoep, S. W., & Hollingshead, A. B. (1991). Collective versus individual induction: Recognition of truth, rejection of error, and collective information processing. Journal of Personality and Social Psychology, 67(1), 50-67. Loy, S. L. (1986). An experimental investigation of a graphical problem-structuring aid and the nominal group technique for group decision support systems. Unpublished doctoral dissertation, Department of Information Systems and Quantitative Sciences, Texas Tech University, Lubbock, TX. Mason, R. O. (1969). Adialectical approach to strategic planning. Management Science, 75(8), B403-B414. Mintzberg, H., Raisinghani, D., & Theoret, A. (1976). The structure of "unstructured" decisions. Administrative Science Quarterly, 21, 246-275. Mitroff, I. L, Emshoff, J. R., & Kilmann, J. R. (1979). Assumptional analysis: A methodology for strategic problem solving. Management Science, 25(6), 583593. Mitroff, I. L, & Featheringham, T. R. (1974). On systemic problem solving and the error of the third kind. Behavioral Science, 19(6), 383-393. Mitroff, !.!.,& Linstone, H. A. (1993). The unbounded mind: Breaking the chains of traditional business thinking. New York: Oxford University Press. Paradice, D. B., & Courtney, J. R, Jr. (1987). Causal and non-causal relationships and dynamic model construction in a managerial advisory system. Journal of Management Information Systems, 3(4), 39-53. Pounds, W. R (1969). The process of problem finding. Industrial Management Review, 11(1), 1-19. Pracht, W. E. (1986). GISMO: A visual problem structuring and knowledge organization tool. IEEE Transactions on Systems, Man, and Cybernetics, 16, 265-270. Pracht, W.E.,& Courtney, IF., Jr.(1988).Theeffectsof an interactive, graphics-based DSS to support problem structuring. Decision Sciences, 19(3), 598-621. Schein, E. (1985). Organizational culture and leadership. San Francisco: Jossey-Bass.

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Shaw, M. (1981). Group dynamics: The psychology of small group behavior (3rd ed.). New York: McGraw Hill. Simon, H. A. (1976). Administrative behavior. New York: Free Press. Simon, H. A. (1977). The new science of management decision (Rev. ed.). Englewood Cliffs, NJ: Prentice Hall. Stasser, G. (1992). Pooling of unshared information during group discussion. In S. Worchel, W. Wood, & J. Simpson (Eds.), Group process and productivity (pp. 48-67). Newbury Park, CA: Sage Publications.

KEY TERMS Decision Model: It is a codified process for making a decision. An example of a decision model is Simon's intelligence-design-choice model, which specifies that one first determines the existence of a problem or an opportunity (intelligence in the military sense of the term), then designs alternative means to solve the problem (or exploit the opportunity), and then chooses the alternative deemed best. Inquiring System: It is a system designed to systematically investigate a situation for the purpose of acquiring or creating knowledge. Inquiry: Inquiry is a systematic process of investigation. Intelligence-Design-Choice: This is an example of a decision model formulated by H. A. Simon. In this model, one first determines the existence of a problem or an opportunity (intelligence in the military sense of the term), then designs alternative means to solve the problem (or exploit the opportunity), and then chooses the alternative deemed best. Messy Problem: A messy problem is a problem that is characterized by the absence of a correct solution. Messy problems are typically complex problems requiring significant judgment and involving multiple stakeholders who have conflicting goals.


Problem Formulation: It is the process of determining the constituent parts of a problem: its important factors and variables, and the interrelationships between them.

Problem Structure: How the constituent parts of a problem (i.e., its important factors and variables) are believed to relate and interact with one another,

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