An Overview of the Human Decision-Making Process
At the most basic level, decision-making is the process of choosing among two or more alternative courses of action for the purpose of attaining one or more goals. As it relates to business analytics, the alternatives are usually generated through descriptive and predictive levels of analytics by a process of data collection and information creation, and the optimal decision is made through the prescriptive level of the analytics continuum. Managerial decision-making is synonymous with the entire management process. Consider, for instance, the critical managerial function of planning, which involves a series of decisions such as what should be done, when, where, why, how, and by whom. Every phase in the planning process involves managerial decision-making, and the collective accuracy, or the optimality, of the decision made determines the value of the outcome obtained.
Are problem-solving and decision-making synonymous? A problem occurs when a system does not meet its established goals, does not yield the expected or predicted results, or does not work as planned. An opportunity may also be considered a problem; if it is not leveraged in a timely fashion, somebody else could take advantage of it, resulting in an even bigger problem down the road. Generally speaking, decision-making can be considered a step in the problem-solving process. That is, one way to distinguish between the two is to examine the phases of the decision process: 1. intelligence, 2. design, 3. choice, and 4. implementation. Some consider the entire process (phases 1–4) as problem-solving, with the choice phase as the real decision-making. Others view phases 1–3 as formal decision-making, ending with a recommendation, with problem-solving additionally including the actual implementation of the recommendation (phase 4). Note that a problem may include situations in which a person must decide which opportunity to exploit. In this book, we use the terms, problem solving and decision making, interchangeably.
There have been numerous attempts to describe the human decision-making process. Although every person, based on life experiences, develops and employs a different set of logically ordered steps to identify and solve problems and make decisions, common sense suggests that there ought to be a generalized approach to human decision-making, maybe not at the detailed level, but at a high level of conceptualization. Among the related theories suggested, the one by Newell and Simon (1972) and Simon (1982) stands ahead of the rest and has stood against the time for the past 50 years. Herbert Alexander Simon (1916–2001) was an American economist and political scientist whose primary interest was the human decision-making process within organizations. He received the Nobel Prize in Economics in 1978 and the Turing Award in 1975. He was at Carnegie Mellon University for most of his career, from 1949 to 2001.
Simon’s Theory of Decision-Making
Although, historically, decision-making has been viewed as a creative, experience-driven, ad-hoc practice, today it is widely perceived as a systematic, evidence-driven, scientific process. Therefore, to enhance the likelihood of obtaining the best possible outcomes, the decision-makers are advised to follow a standardized, systematic, and logical decision-making process. Simon (1977) said that such a systematic process involves three major phases: intelligence, design, and choice. He later added a fourth phase: implementation. Monitoring can be considered a fifth phase—a form of feedback. However, we view monitoring as the intelligence phase applied to the outputs of the implementation phase. Simon’s model is widely accepted as the most concise and yet most complete characterization of rational decision-making. A conceptual picture of the decision-making process, proposed by Simon, is shown in Figure 1.2.
FIGURE 1.2 The decision-making/modeling process.
There is a continuous flow of activity from intelligence to design, from design to choice; but at any phase, there may be a return to a previous phase—feedback—for validation, verification, and refinement. The seemingly complex nature of the haphazard path from problem discovery to solution development and implementation via decision-making can be explained by the continuous improvement achieved through these feedback loops. Following is a brief description of the phases in this decision-making process.
Phase 1. Intelligence
The intelligence phase in the decision-making process involves scanning the environment, either intermittently or continuously. It includes several activities aimed at identifying problem situations or opportunities. It may also include monitoring the results of the implementation phase of a previously completed decision-making process.
Problem (or Opportunity) Identification
The intelligence phase begins with the identification of organizational goals and objectives related to issues of concern—such as inventory management, job selection, and lack of or incorrect Web presence—and the determination of whether those goals and objectives are being met. Problems occur because of the dissatisfaction with the status quo. Dissatisfaction is the result of a difference between what people desire or expect and what is occurring. In this first phase, a decision-maker attempts to determine whether a problem exists, identify its symptoms, determine its magnitude, and explicitly define it. Often, what is described as a problem, such as excessive costs, may be only a symptom or measure of a problem, such as improper inventory levels. Because real-world problems are usually complicated by many interrelated factors, it is sometimes difficult to distinguish between the symptoms and the real problem. New opportunities and problems certainly may be uncovered while investigating the causes of symptoms.
The existence of a problem can be determined by monitoring and analyzing the organization’s productivity level. The measurement of productivity and the construction of a model ought to be based on real data. The collection of data and the estimation of future data are among the most difficult steps in the analysis. The following are some issues that may arise during data collection and estimation and thus plague decision-makers:
Data are not available. As a result, the model is made with, and relies on, potentially inaccurate estimates.
Obtaining data may be expensive.
Data may not be accurate or precise enough.
Data estimation is often subjective.
Data may be insecure.
Important data that influence the results may be qualitative (soft).
There may be too much data, resulting in information overload.
Outcomes (or results) may occur over an extended period. As a result, revenues, expenses, and profits will be recorded at different points in time. To overcome this difficulty, a present-value approach can be used if the results are quantifiable.
It is assumed that future data will be similar to historical data. If this is not the case, the nature of the change has to be predicted and included in the analysis.
When the preliminary investigation is completed, it is possible to determine whether a problem really exists, where it is located, and how significant it is. A key issue is whether an information system is reporting a problem or only the symptoms of a problem. For example, in the case of “sales are down,” there is a problem, but the sales being down is a symptom, indicating existence of a real problem. It is imperative to identify the real problem, and not the symptom, to solve and thereby create a real business value.
Problem classification is the conceptualization of a problem in an attempt to place it in a definable category, possibly leading to a standard solution approach. An important approach classifies problems according to the degree of structure evident in them. This ranges from completely structured, or programmed, to completely unstructured, or unprogrammed.
Many complex problems can be divided into subproblems. Solving the simpler subproblems may help in solving a complex problem. Also, seemingly poorly structured problems sometimes have highly structured subproblems. Just as a semistructured problem results when some phases of decision-making are structured whereas other phases are unstructured, when some subproblems of a decision-making problem are structured while others are unstructured, the problem itself is semistructured. Decomposition also facilitates communication among decision-makers. Decomposition is one of the most important aspects of the analytic hierarchy process (AHP is discussed in Chapter 4, “Multi-Criteria Decision Making”), which helps decision-makers incorporate both qualitative and quantitative factors into their decision-making process.
In the intelligence phase, it is important to establish problem ownership. A problem exists in an organization only if someone or some group takes on the responsibility of attacking it and if the organization has the ability to solve it. The assignment of authority to solve the problem is called problem ownership. For example, a manager may feel that there’s a problem because interest rates are too high. Because interest rate levels are determined at the national and international levels and most managers can do nothing about them, high interest rates are the problem of the government, not a problem for a specific company to solve. The problem companies actually face is how to operate in a high–interest-rate environment. For an individual company, the interest rate level should be handled as an uncontrollable (environmental) factor to be predicted. When problem ownership is not established, either someone is not doing his job, or the problem at hand has yet to be identified as belonging to anyone. It is then important for someone to either volunteer to own it or assign it to someone. The intelligence phase ends with a formal problem statement.
Phase 2. Design
The design phase involves finding or developing and analyzing possible courses of action. These include understanding the problem and testing solutions for feasibility. A model of the decision-making problem is constructed, tested, and validated. Modeling involves conceptualizing a problem and abstracting it to quantitative or qualitative form. For a mathematical model, the variables are identified, and their mutual relationships are established. Simplifications are made, whenever necessary, through assumptions. For example, a relationship between two variables may be assumed to be linear even though in reality there may be some nonlinear effects. A proper balance between the level of model simplification and the representation of reality must be obtained because of the cost-benefit trade-off. A simpler model leads to lower development costs, easier manipulation, and a faster solution but is less representative of the real problem and can produce inaccurate results. However, a simpler model generally requires less data, or the data is aggregated and easier to obtain.
The process of modeling involves a combination of art and science. As a science, there are many standard model classes available, and, with practice, an analyst can determine which one is applicable to a given situation. As an art, creativity and finesse are required when determining what simplifying assumptions can work, how to combine appropriate features of the model classes, and how to integrate models to obtain valid solutions.
Principle of Choice
A principle of choice is a criterion that describes the acceptability of a solution approach. In a model, it is a result variable. Selecting a principle of choice is not part of the choice phase but involves how a person establishes decision-making objectives and incorporates the objectives into the models. Are we willing to assume high risk, or do we prefer a low-risk approach? Are we attempting to optimize or satisfice? It is also important to recognize the difference between a criterion and a constraint.
Normative models are models in which the chosen alternative is demonstrably the best of all possible alternatives. To find it, the decision-maker should examine all the alternatives and prove that the one selected is indeed the best, which is what the person would normally want. This process is basically optimization. In operational terms, optimization can be achieved in one of three ways:
Get the highest level of goal attainment from a given set of resources. For example, which alternative will yield the maximum profit from an investment of $10 million?
Find the alternative with the highest ratio of goal attainment to cost (profit per dollar invested) or maximize productivity.
Find the alternative with the lowest cost (or smallest amount of other resources) that will meet an acceptable level of goals. For example, if your task is to select hardware for an intranet with a minimum bandwidth, which alternative will accomplish this goal at the least cost?
Normative decision theory is based on the following assumptions of rational decision-makers:
Humans are economic beings whose objective is to maximize the attainment of goals. The decision-maker is rational. In other words, more of a good thing (revenue, fun) is better than less; less of a bad thing (cost, pain) is better than more.
For a decision-making situation, all viable alternative courses of action and their consequences, or at least the probability and the values of the consequences, are known.
Decision-makers have an order or preference that enables them to rank the desirability of all consequences of the analysis from best to worst.
Are decision-makers really rational? See Schwartz (2005) for anomalies in rational decision-making. Although there may be major anomalies in the presumed rationality of financial and economic behavior, we take the view that they could be caused by incompetence, lack of knowledge, multiple goals being framed inadequately, misunderstanding of a decision-maker’s true expected utility, and time-pressure impacts.
By definition, optimization requires a decision-maker to consider the impact of each alternative course of action on the entire organization because a decision made in one area may have significant effects on other areas. Consider, for example, a marketing department that implements an electronic commerce (e-commerce) site. Within hours, orders far exceed production capacity. The production department, which plans its own schedule, cannot meet demand. It may gear up for as high demand as is possible to meet. Ideally and independently, the department should produce only a few products in extremely large quantities to minimize manufacturing costs. However, such a plan might result in large, costly inventories and marketing difficulties caused by the lack of a variety of products, especially if customers start to cancel orders that are not met in a timely way. This situation illustrates the sequential nature of decision-making.
A systems point of view assesses the impact of every decision on the entire system. Thus, the marketing department should make its plans in conjunction with other departments. However, such an approach may require a complicated, expensive, time-consuming analysis. In practice, the information builder may close the system within narrow boundaries, considering only the part of the organization under study (the marketing or production department, in this case). By simplifying, the model then does not incorporate certain complicated relationships that describe interactions with and among the other departments. The other departments can be aggregated into simple model components. Such an approach is called suboptimization.
If a suboptimal decision is made in one part of the organization without considering the details of the rest of the organization, then an optimal solution from the point of view of that part may be inferior for the whole. However, suboptimization may still be a practical approach to decision-making, and many problems are first approached from this perspective. It is possible to reach tentative conclusions—and generally usable results—by analyzing only a portion of a system, without getting bogged down in too many details. After a solution is proposed, its potential effects on the remaining departments of the organization can be tested. If no significant negative effects are found, the solution can be implemented.
Suboptimization may also apply when simplifying assumptions are used in modeling a specific problem. There may be too many details or too much data to incorporate into a specific decision-making situation, so not all of it is used in the model. If the solution to the model seems reasonable, it may be valid for the problem and thus be adopted. Suboptimization may also involve simply bounding the search for an optimum (by a heuristic) by considering fewer criteria or alternatives or by eliminating large portions of the problem from evaluation. If it takes too long to solve a problem, a good-enough solution found already may be used and the optimization effort terminated.
Related to satisficing is Simon’s idea of bounded rationality. Humans have a limited capacity for rational thinking; they generally construct and analyze a simplified model of a real situation by considering fewer alternatives, criteria, or constraints than actually exist. Their behavior with respect to the simplified model may be rational. However, the rational solution for the simplified model may not be rational for the real-world problem. Rationality is bounded not only by limitations on human processing capacities, but also by individual differences, such as age, education, knowledge, and attitudes. Bounded rationality is also why many models are descriptive rather than normative. This may also explain why so many good managers rely on intuition, an important aspect of good decision-making (see Stewart, 2002; and Pauly, 2004).
Because rationality and the use of normative models lead to good decisions, it is natural to ask why so many bad decisions are made in practice. Intuition is a critical factor that decision-makers use in solving unstructured and semistructured problems. The best decision-makers recognize the trade-off between the marginal cost of obtaining further information and analysis versus the benefit of making a better decision. But sometimes decisions must be made quickly, and, ideally, the intuition of a seasoned, excellent decision-maker is called for. When adequate planning, funding, or information is not available, or when a decision-maker is inexperienced or ill trained, disaster can strike.
Developing (Generating) Alternatives
A significant part of the model-building process is generating alternatives. In optimization models (such as linear programming), the alternatives may be generated automatically by the model. In most decision situations, however, it is necessary to generate alternatives manually. This can be a lengthy process that involves searching and creativity, perhaps utilizing electronic brainstorming in a group support system (GSS). It takes time and costs money. Issues such as when to stop generating alternatives can be important. Too many alternatives can be detrimental to the process of decision-making. A decision-maker may suffer from information overload. Generating alternatives is heavily dependent on the availability and cost of information and requires expertise in the problem area. This is the least formal aspect of problem-solving. Alternatives can be generated and evaluated using heuristics. The generation of alternatives from either individuals or groups can be supported by electronic brainstorming software in a Web-based GSS. Note that the search for alternatives usually occurs after the selection of the criteria for evaluating the alternatives are determined. This sequence can ease the search for alternatives and reduce the effort involved in evaluating them, but identifying potential alternatives can sometimes aid in identifying criteria. The outcome of every proposed alternative must be established. Depending on whether the decision-making problem is classified as one of certainty, risk, or uncertainty, different modeling approaches may be used.
The value of an alternative is evaluated in terms of goal attainment. Sometimes an outcome is expressed directly in terms of a goal. For example, profit is an outcome, profit maximization is a goal, and both are expressed in dollar terms. An outcome such as customer satisfaction may be measured by the number of complaints, by the level of loyalty to a product, or by ratings found through surveys. Ideally, a decision-maker would want to deal with a single goal, but in practice, it is not unusual to have multiple goals. When groups make decisions, each group participant may have a different agenda. For example, executives might want to maximize profit, marketing might want to maximize market penetration, operations might want to minimize costs, and stockholders might want to maximize the bottom line. Typically, these goals conflict, so special multiple-criteria methodologies have been developed to handle this. One such method is the AHP, which is covered in detail in Chapter 4.
All decisions are made in an inherently unstable environment. This is due to far too many unpredictable events occurring in both the economic and the physical environments. Some risk (measured as probability) may be due to internal organizational events, such as a valued employee quitting or becoming ill, whereas others may be due to natural disasters, such as a hurricane. Aside from the human toll, one economic aspect of Hurricane Katrina was that the price of a gallon of gasoline doubled overnight due to uncertainty in the port capabilities, refining, and pipelines of the southern United States. What can a decision-maker do in the face of such instability?
In general, people have a tendency to measure uncertainty and risk poorly. People tend to be overconfident and have an illusion of control in decision-making. This may perhaps explain why people often feel that one more pull of a slot machine will definitely pay off.
However, methodologies for handling extreme uncertainty do exist. Aside from estimating the potential utility or value of a particular decision’s outcome, the best decision-makers are capable of accurately estimating the risk associated with the outcomes that result from making each decision. Thus, one important task of a decision-maker is to attribute a level of risk to the outcome associated with each potential alternative being considered. Some decisions may lead to unacceptable risks in terms of success and can therefore be discarded or discounted immediately.
In some cases, some decisions are assumed to be made under conditions of certainty simply because the environment is assumed to be stable. Other decisions are made under conditions of uncertainty, where risk is unknown. Still, a good decision-maker can make working estimates of risk. Also, the process of developing a business intelligence/decision support system (BI/DSS) involves learning more about the situation, which leads to a more accurate assessment of the risks.
A scenario is a statement of assumptions about the operating environment of a particular system at a given time; that is, it is a narrative description of the decision-situation setting. A scenario describes the decision and uncontrollable variables and parameters for a specific modeling situation. It may also provide the procedures and constraints for the modeling.
Scenarios originated in the theater, and the term was borrowed for war gaming and large-scale simulations. Scenario planning and analysis is a DSS tool that can capture a whole range of possibilities. A manager can construct a series of scenarios or what-if cases, perform computerized analyses, and learn more about the system and decision-making problem while analyzing it. Ideally, the manager can identify an excellent, possibly optimal, solution to the model of the problem.
Scenarios are especially helpful in simulations and what-if analyses. In both cases, we change scenarios and examine the results. For example, we can change the anticipated demand for hospitalization (an input variable for planning), thus creating a new scenario. Then we can measure the anticipated cash flow of the hospital for each scenario.
Scenarios play an important role in management support system (MSS) because they do the following:
Help identify opportunities and problem areas
Provide flexibility in planning
Identify the leading edges of changes that management should monitor
Help validate major modeling assumptions
Allow the decision-maker to explore the behavior of a system through a model
Help to check the sensitivity of proposed solutions to changes in the environment, as described by the scenario
There may be thousands of possible scenarios for every decision situation. However, the following are especially useful in practice:
The worst possible scenario
The best possible scenario
The most likely scenario
The average scenario
Errors in Decision-Making
The model is a critical component in the decision-making process, but a decision-maker may make a number of errors in its development and use. Validating the model before it is used is critical. Gathering the right amount of information, with the right level of precision and accuracy, to incorporate into the decision-making process is also critical. Sawyer (1999) described “the seven deadly sins of decision-making,” most of which are behavior or information related.
Phase 3. Choice
Choice is the critical act of decision-making. The choice phase is the one in which the actual decision and the commitment to follow a certain course of action are made. The boundary between the design and choice phases is often unclear because certain activities can be performed during both phases and because the decision-maker can return frequently from choice activities to design activities, such as by generating new alternatives while performing an evaluation of existing ones. The choice phase includes the search for, evaluation of, and recommendation of an appropriate solution to a model. A solution to a model is a specific set of values for the decision variables in a selected alternative.
Note that solving a model is not the same as solving the problem the model represents. The solution to the model yields a recommended solution to the problem. The problem is considered solved only if the recommended solution is successfully implemented.
Solving a decision-making model involves searching for an appropriate course of action. Search approaches include analytical techniques (solving a formula), algorithms (step-by-step procedures), heuristics (rules of thumb), and blind searches (shooting in the dark, ideally in a logical way).
Each alternative must be evaluated. If an alternative has multiple goals, each must be examined and balanced against the other. Sensitivity analysis is used to determine the robustness of any given alternative; slight changes in the parameters should ideally lead to slight or no changes in the alternative chosen. What-if analysis is used to explore major changes in the parameters. Goal-seeking helps a manager determine values of the decision variables to meet a specific objective.
Phase 4. Implementation
In The Prince, Machiavelli astutely noted some 500 years ago that there was “nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things.” The implementation of a proposed solution to a problem is, in effect, the initiation of a new order of things or the introduction of change. And change must be managed. User expectations must be managed as part of change management.
The definition of implementation is somewhat complicated because implementation is a long, involved process with vague boundaries. Simplistically, the implementation phase involves putting a recommended solution to work, not necessarily implementing a computer system. Many generic implementation issues, such as resistance to change, degree of support of top management, and user training, are important in dealing with managerial decisions.