Engineering Decision: An Important Issue in Engineering Education

A decision is a conscious, irrevocable allocation of resources with the purpose of achieving a desired objective (Skinner 1999). As individuals, people make decisions hundreds of times a week; most decisions have little lasting effect. As managers of the Coca-Cola Company, their decision to change formulas in the mid-1980s invited massive customer rebellion. The decision could have been a marketing disaster, but the company actually gained market share when the original formulation was brought back because of all the negative publicity. From a decision-making point of view, introducing “New Coke” was an interesting decision (one cannot say it was good or bad) with a positive outcome. As engineers we face myriad decisions every day, such as design challenges, production risks, and construction alternatives, within the constrains of limited resources. Often engineering decisions affect future generations. While good engineering decisions improve our civilization and protect the environment, poor engineering decisions have the potential to harm thousands or have long-lived effects that often far surpass current understanding. Poor decisions relating to the environment can result in dangerous and almost completely unexpected by-products.

When making high-stake decisions, people should choose alternatives in an optimal fashion due to limited information and uncertainties. Due to the complexity of decision making, fundamental knowledge on methodology and framework for optimizing decisions is needed. Therefore, it is important for our students to receive education relating to decision making. Unfortunately, most of our engineering curricula do not include specific courses to systematically educate students about decision making. Engineering students take courses to learn fundamentals and engineering design, but they seldom have time to take courses related to decision making. For most engineering students, the only course they take is statistics (or operations research) that is close to decision making. The concept and theory of decision making are often replaced by trade-offs, risk analysis, and cost analysis. “Because so few engineering decisions turn out poorly, engineering decision-making is a little-known and rarely discussed process” (Vesilind and Morgan 2004). For most engineering endeavors, decision making does not have a high priority.

However, the destruction, heartbreak, and family separation that resulted from Hurricane Katrina is a wake-up call for all of us. High-impact decisions often involve significant investment (e.g., upgrading the levee systems in New Orleans to withstand a Category 5 hurricane would cost billions of dollars), high complexity (e.g., decision to launch the Space Shuttle Challenger STS 51-L stretched from technical to ethical), and elements of uncertainty (e.g., the Missouri River had several $100\text{-}\text{year}$ floods in the 1990s). We need to realize the critical importance and the difficulties in decision making. Therefore, we cannot assume that our engineering students will gain decision-making knowledge naturally, like learning to breathe. Instead of letting them rely on their own devices to learn how to make decisions, we should incorporate decision-making theories throughout our engineering curricula and expose students to a variety of decision-making situations applicable to their field of study.

The charge is clear; however, the challenge is that we face difficulties in at least three major dimensions in the endeavor to develop decision-making related curricula. Decision making (or decision analysis) is important for engineering education as well as many other fields (e.g., business administration, law schools, and medical and public health schools, etc.). However, decision making has been included traditionally in curricula of arts and sciences (e.g., mathematical, information management, etc.). This is because decision analysis was originally developed as a branch of mathematics (e.g., statistics), and decision support systems (DSS) as a branch of information systems.

The origins of decision making, as a subject, can be traced back to the 1700s when Reverend Thomas Bayes (an English Presbyterian minister, mathematician, and a Royal Fellow) and Abraham de Moivre (a French-born mathematician and Royal Fellow) developed probability theories, with the objective of proving the existence of God (Skinner 1999). The relative frequency concept of probability, Bayesian statistics (which reversed the traditional flow of thought—result given a state—to what state would come from the results), and the concept of decision making linking with the likelihood of the possible outcomes were all developed in the early 18th century. Operations research started in World War II and contributes to decision making tremendously. In 1947, John von Neumann and Oscar Morgenstern published Theory of Games and Economic Behavior. In game theory, use of a utility function rather than willingness to pay is a major distinction between decision analysis and cost-benefit analysis. Several books published since the 1950s established decision analysis as a methodology with real applications. These include Analysis of Decisions under Uncertainty, by Robert Schlaifer and Decision Analysis, by Howard Raiffa. By the middle of the 1960s, decision analysis was used to solve many interesting and complex problems, such as the decision to seed hurricanes. In recent years, decision making has become an important technique in engineering, business, industry, and government. Because of its complexity, it would take at least one semester for engineering students to learn both the analytical and facilitation aspects of decision making.

The concept of DSS started to emerge in the 1960s, with Peter Keen and Charles Stabell being the two primary DSS pioneers. The concept of DSS evolved from organizational decision making and the technical work on interactive computer systems (mainly carried out at the Massachusetts Institute of Technology) in the 1960s. In the 1970s, DSS theories were developed, together with executive information systems (EIS) (Power 2003). In the 1980s, DSS and EIS were implemented in industry; artificial intelligence and expert systems technologies were relevant to developing DSS. In the early 1990s, data warehousing and online analytical processing (OLAP) began broadening the realm of EIS and DSS. Nowadays, if a software program runs on a PC and can help people make decisions, we refer to it as a DSS. EIS, OLAP, geographic information systems (GIS), software agents, knowledge discovery systems, and group DSS can all be lumped into the category of DSS. Currently, using different software (e.g., TreeAge Pro, Analytica, etc.) to support decision making is in the mainstream of DSS, but DSS is heading toward delivering insights via business intelligence (BI) services to guarantee the returned business value that DSS promises. Obviously, the accumulated knowledge in DSS and the emergence of BI creates another dimension of difficulty for engineering students to master decision-making skills.

The difficulties in making engineering decisions stem from each engineering field but are mainly due to the nature of the complexity involved in decision making. Engineering solutions usually focus on the entirety of the decision-making problem, which stretches from the most objective (technical) to the most subjective (ethical) and often are as much matters of policy as they are of technology. More often than not it is difficult to draw a clear line where technology ceases to play any meaningful role in decision-making and where policy becomes the determinant. This is particularly true for environmental decision making, and the paradoxes of environmental policy are good examples in this context (Smith 2004). For example, there is strong evidence that a shift to organic farming would increase farm income and reduce soil erosion and nutrient depletion while meeting American food needs and reducing oil imports. However, the incentives operating on policymakers, such as the influence of pesticides manufacturers, keep U.S. farm policy unchanged. Keep in mind that fundamental to the regulatory process governing environmental decision making is the concept of pluralism (Smith 2004). The bargaining that organized groups engage in when attempting to influence environmental policy is different because all groups are not created equal. Moreover, none of the decision-making models and software is sophisticated enough to handle complicated decision-making problems. This is because the success of a decision often depends on factors other than pure logic or reason such as emotions, beliefs, values, attitudes of engineers, policymakers, or communities. All these uncertainties make it difficult for us to develop new curriculums in the context of decision making.

Furthermore, many important concepts relating to engineering decisions have evolved over time. For example, the conventional top-down decision-making process relating to the environment has been transforming toward a community-based decision-making process since the latter half of the 1990s. While community participation in decision making can thrive and produce the greatest gains in effective citizen governance, it may be costly and ineffective under certain conditions (Irvin and Stansbury 2004). As another example, risk assessment, a powerful tool for environmental decision making, has been criticized as an “innocent until proven guilty” approach to destructive activities; that is, risk-based management allows us to plunge ahead on activities that would cause only “acceptable damage.” The cumulative effects of our risk-based decisions have severely degraded many of our ecosystems. A new approach based on a “principle of precautionary action” is proposed, giving decision makers improved tools. The principle says the following: when an activity raises threats of harm to human health or the environment, precautionary measures should be taken even if some cause and effect relationships are not fully established scientifically. In this context, the proponent of an activity, not the public, should bear the burden of proof (O’Brien 2000). Obviously, it may be easy to introduce the principle to engineering students, but it would be difficult to develop students’ decision-making skills based on the principle because the principle lumps together risk assessment and risk management.

In light of the aforementioned discussions, the body of knowledge in the context of decision making is very large. Each engineering field may need to design a way to incorporate the knowledge into its curriculum. Taking environmental engineering as an example, we can introduce decision-making concepts and methodology into the first course “Introduction to Environmental Engineering” as Vesilind and Morgan did in their textbook (Vesilind and Morgan 2004) (surprisingly, several popular introduction textbooks do not have an individual chapter to deal with these materials). The concepts and skills of decision making can then be incorporated into different courses via case studies, lecture notes, homework, and design projects. Currently, education in water resource engineering emphasizes decision making in many different subjects. Environmental engineering courses at the senior level should have enough places to introduce more in-depth materials relating to decision making such as site investigation, remediation of hazardous wastes, water, wastewater and hazardous wastes treatment, and air pollutions, etc. Some programs have a course relating to environmental law, which should include some materials relating to the regulatory framework and the institutional setting of the policy-making process. Students need to know how incrementalism, decentralization, the incentives operating on policymakers to plan for the short term rather than for long-term ideological bias, and the crisis-response nature of policy making influences the decision-making process. However, the dilemma is still at large that engineering students only have a limited time to study an increasingly large number of pertinent and important topics.

In summary, while decision making should be brought more speedily into the engineering curriculum, we must incorporate it creatively. As Albert Einstein said “the significant problems we face cannot be solved at the same level of thinking we were at when we created them.(Skinner 1999)” More research is needed in the context of this issue.