Decision-making is commonly thought of as the cognitive process one undergoes before choosing a particular course of action. Engineers are trained to approach problems from a relatively more pragmatic and logical perspective. Engineers value efficiency, utility, and minimizing cost over aesthetics and flair. Understanding how the engineering mind internalizes information and reacts accordingly can provide insight on how the human mind makes decisions and how one can make better decisions.



A decision is a commitment to use resources (Ullman, 2001, p. 4). Decision-making is one of the most fundamental yet complex psychological processes performed regularly by human beings. Because individuals perform a variety of tasks and therefore execute several different types of decisions every day, formulating an exact definition for decision making can be difficult. According to Jonassen, a decision represents an ill-structured problem where one has to evaluate multiple options and commit to one of them (Jonassen, 2012). There are two different ways to understand how decisions are made. From a rational perspective, decisions are made to maximize utility. From a naturalistic perspective, decisions are made with less practicality but more influence from personal beliefs and prior experiences. Decisions are made under three types of circumstances (Roth, 2007):

  1. Risk: the information is unavailable, but probabilistic models can be used because the distributions of random variables are known.
  2. Uncertainty: the probability distributions are unavailable, but other obstacles are known (such as radiation affecting transmission from an antenna)
  3. Ambiguity: functional form is unknown, and trial-and-error testing may be needed even to determine inputs and outputs

All decisions can be sorted into one of four categories (Yates & Tschihart, 2006):

  1. Choices: selection of a subset from a larger set of alternatives
  2. Acceptances/rejections: the binary decision
  3. Evaluations: assigning worth to an option
  4. Constructions: attempting to create an ideal solution given available resources

The Brown University Division of Engineering (Brown University, n.d.) has defined the typical engineering decision making process as follows:

  • define clearly the objectives of solving a specific problem
  • generate all possible solutions
  • predict the outcome of each solution
  • determine the best solution by balancing the pros and cons along with cost and benefit

Decision Theory

Several decision models have been created to shed light how an individual commits to a decision.

  • Normative models assume that individuals are perfectly rational and seek to optimize resources (Jonassen, 2012):
  • Rational choice models: the individual identifies a set of options, determines the criteria (usually quantitative) for evaluating the options, weighs each option, and selects the option with the highest score
  • Cost-benefit analysis: used by corporations and governments, this type of decision-making is used when weighing business decisions and new policies. Attempts to quantify values associated with each decision
  • Risk assessment models: using probability to analyze games of chance. Evaluating expected values while considering the consequences of both false positives and false negatives

An example of a risk assessment model is when a patient or doctor has to make a medical decision. For example, an ill patient may have the weigh the risk and benefits of the two following treatments: Treatment A has a projected 20% chance of death and 80% chance of 35 years of normal life after the treatment. Treatment B has a 100% chance of survival with a certainty of 18 years of normal life. Naturalistic models are based on research or results obtained from actual humans. These models are influenced heavily by emotions and experiences (Jonassen, 2012). The normative models above assume that individuals make decisions based on the concept of unbounded rationality, which is impossible in practicality. Individuals also unconsciously gauge the benefit of decisions based on values and probabilities inherent to a situation. In other words, individuals are vulnerable to errors in reasoning, judgmental biases, representational faults, and coping defects. For example, an individual often believes that because he/she has already invested so much in making something happen, he/she cannot turn back before the task is complete.

  • Narrative-based decision-making: making decisions based on explanations provided by other unreliable sources (testimonies during trials). According to Jonassen (2012, p. 348), Jurors were twice as likely to find the defendant guilty when the prosecution’s evidence was presented in story form, and the defense’s evidence was not.
  • Identity-based decision-making: making decisions consistent with one’s image of oneself. An individual may ask him/herself: What does a person like me do?

Decision-Making Techniques

Smart decision-making is essential when making decisions on a managerial level that may affect entire companies or even countries. Though humans cannot behave perfectly rationally, several techniques have been developed to minimize resources and maximize benefits (Jonassen, 2012).

    • Decision matrices: list all practical options in rows. Create a set of criteria (qualitative and quantitative) and use them as the columns of your matrix. When creating a decision matrix, the individual essentially draws out a visual representation of the pros and cons of each option.
    • SWOT (strengths, weaknesses, opportunities, threats) analysis: when attempting to make a managerial decision, this model allows the individual to analyze both the internal and external forces at work. Internal forces are the strengths and weaknesses of the company, for example, and external forces are the opportunities and threats. After creating a list of variables central to the problem at hand, break them down into these four categories and quantize them.
    • Force field analysis (Figure 1): examine the forces for and against a certain action.
    • Argumentation: as a project manager, it may be helpful to have different people argue for different options
    • Scenarios: use in times of uncertainty to consider the full extent of the possible consequence of an action. Create stories based on previous experience or data to predict future events.


    Figure 1

    Force field analysis example (Jonassen, 2012, p. 351).

    When making a decision in the real world, the engineer seldom knows all the criteria. One can never have enough knowledge or time to fully evaluate all possible options. As a manager, one can also never count on getting everyone to agree. Ullman argues that a robust product is insensitive to noise factors (Ullman, 2001), or any factor the designer cannot or choose not to control, such as aging, differences in use, and variances in manufacture. Ullman’s robust 12 steps for decision-making (Figure 2) can broken down into six categories: preparing the decision-makers, clarifying the issue, developing the criteria, generating alternatives, evaluating alternatives using criteria, and deciding what to do next. JZ_figure2

    Figure 2

    Ullman’s robust 12 steps for decision-making (Ullman, 2001, p. 7).

    Case Study

    This case study is taken directly from Garger (2010). A firm that manufactures paper products considers a new project that replaces part of the electricity purchased form the local electricity company, with their own electricity produced from solar energy. The firm works 24 hours/day, 7 days/week, and consumes 5 MW/hour. The management is considering a 2.5 MWP solar system that would produce half of the daytime electricity consumption, interacting with the utility power grid. We assume that yearly cash flow savings will depend on the cost of purchased electricity and that the efficiency of the system is reduced by 0.3% per year. Let net present value (NPV) = sum of the present values of the individual cash flows of the same entity, CF = Cash Flow, CF0 = Initial outlay for the project, CF1 = Cash received at the end of year 1, and I = discounting rate of cash flow. Scenario A – If the cost of electricity (daytime rate) will climb to 0.15$/KWH, with a probability of 30%, the estimated yearly savings will rise to $1,938,000. NPV scenario A:

    • CF0 = -$12,053,000;
    • CF1 = $1,938,000;
    • The frequency of cash received in 1 year is repeated for 20 years.
    • I= 8.2%;
    • NPV = $6,694,682.

    Scenario B – with the present cost of electricity (daytime rate) of 0.1O$/KWH, estimated savings are some $1,212,000/year. Probability of scenario B is 40%. NPV scenario B:

    • CF0 = -$12,053,000;
    • CF1 = $1,212,000;
    • The frequency of cash received in 1 year is repeated for 20 years.
    • I= 8.2%;
    • NPV = -$328,443.

    Scenario C – If the cost of electricity will decline to 0.05$/KWH, with a probability of 30%, the estimated yearly savings will drop to $486,000. NPV scenario C:

    • CF0 = -$12,053,000;
    • CF1 = $486,000;
    • The frequency of cash received in 1 year is repeated for 20 years.
    • I = 8.2%;
    • NPV = -$7,351,569.

    If we proceed with the investment today, we can calculate the Expected Net Present Value (NPV) as follows: E[NPV] = 0.30($6,694,682) + 0.40(-$328,443) + 0.30(-$7,351,569) = -$328,443 The conclusion is that if we invest in the project today, we can expect a negative NPV, which will lead us to reject the project.

    Application to Senior Project

    To illustrate the diversity of decision-making, one can analyze the logic behind decisions made for a Tufts University electrical engineering senior design project. As three ambitious young engineering students with limited time in the semester, Team Blue could not afford to waste project sessions doing less meaningful tasks. They were attempting to create a noise-canceling iPhone application that predicts and cancels low-frequency ambient noise using only an iPhone and the default Apple EarPods headset. Because raw materials and employee salaries were non-factors, Team Blue’s most valuable resource was time. A crucial part of the semester was choosing tasks to devote time to, which was a decision-making process. First, the team had to decide what they wanted to accomplish by the end of fall semester. With the guidance of their advisor, they scaled down our project as much as possible within reason and focused on learning new competencies, such as digital signal processing, controls, communications, and probability concepts along with how to build iPhone applications. To maximize their human resources, they decided that at least two people must be proficient with each competency in the event that one of them could not be present for a project session. After weighing in each person’s strengths and weaknesses, individuals were assigned to work on an aspect of the project that the team thought was most suitable given his background. The result at the end of the semester was a working MATLAB algorithm and the skeleton of an iPhone application with basic recording and playback functionality. Though Team Blue fell short of their initial goal, the decisions they made allowed them to lay the foundation for the work they will do next semester.


    The engineer makes decisions for a living by creating new requirements, selecting design concepts, choosing components, and optimizing parameters (Roth, 2007). The engineer bridges ideas and reality through several iterations of decision-making, and each decision can be thought of as a marker of the design from initiation to implementation to termination (Marston & Mistree, 1997). The Accreditation Board for Engineering and Technology (ABET) says that design is “a decision making process (often iterative) in which basic sciences, mathematics, and the engineering sciences are applied to convert resources optimally to meet stated needs” (Roth, 2007, p. 239). Roth warns that decision-making should not simply be a number-crunching exercise. For an engineer to make truly great decisions, he/she must also pursue innovative thinking, idea management, and corporate learning.

    Cited References

    • Brown University. (n.d.). Brown University Division of Engineering, Mechanical Engineering Basics Website. Retrieved from http://www.engin.brown.edu/undergrad/mechengin/basics.htm
    • Garger, J. (2010). Real Options for Engineering Management Decision Making. Â Proceedings IE & EM 2010 2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management, 237-239. DOI: 10.1109/ICIEEM.2010.5646656
    • Jonassen, D. H. (2012). Designing for Decision Making. Association for Educational Communications and Technology, 60(2), 341-359. DOI: 10.1007/s11423-011-9230-5
    • Marston, M., & Mistree, F. (1997). A decision based foundation for systems design: A conceptual exposition. Optimization in Decision-Based Design. Retrieved from http://dbd.eng.buffalo.edu/pdf/CIRP.10.97.PDF
    • Roth, G. L. (2007). Decision making in systems engineering: The Foundation. In Collaborative Technologies and Systems 2007. CTS 2007 (pp. 236–246). DOI: 10.1109/CTS.2007.4621761
    • Ullman, D. G. (2001). Robust decision-making for engineering design. Journal of Engineering Design, 12(1), 3-13, DOI: 10.1080/09544820010031580
    • Yates, J. F., & Tschirhart, M. D. (2006). Decision-making expertise. In The Cambridge handbook of expertise and expert performance, 15, 421-438. OCLC WorldCat Permalink: http://www.worldcat.org/oclc/63297647