Decoy Effect” on the Engineering Design Process

AC 2012-5240: THE INFLUENCE OF THE ”DECOY EFFECT” ON THE ENGINEERING DESIGN PROCESS

Dr. Joseph C. Musto, Milwaukee School of Engineering

Joe Musto is a professor of mechanical engineering and Director of the Mechanical Engineering Program at Milwaukee School of Engineering, He holds a B.S. from Clarkson University (Potsdam, N.Y.), and both a M.Eng. and Ph.D. from Rensselaer Polytechnic Institute (Troy, N.Y.).

Dr. Alicia Domack, Milwaukee School of Engineering Page 25.1316.1

c American Society for Engineering Education, 2012 The Influence of the “Decoy Effect” on the Engineering Design Process

Abstract

Engineering students are educated in formal design methodologies to aid in the decision-making processes involved in the creation of new products and systems. These formal methodologies make use of such design tools as decision matrices to aid engineering teams in the evaluation and selection of a design solution from the various alternatives considered for development.

The Decoy Effect (or Asymmetrically Dominated Alternative Effect), is a well-studied phenomenon that affects decision-making. In essence, it describes the experimentally-verified effect that occurs when an inferior choice is introduced to the available alternatives. This inferior choice is said to be “dominated” by one of the original alternatives, which then prompts individuals to choose the dominating alternative 1,2 . The effect of this so-called “decoy” choice often leads the decision-maker to the selection of a suboptimal alternative.

The decoy effect has been shown to adversely affect decision-making capabilities involving consumer product purchases, gambling, and job offers 3,4,5 . While the impact of this effect would certainly extend to decision-making among technical professionals, there has been little attempt to address the impact on the decision-making process used by engineers in design.

This paper introduces the decoy effect and describes the implications it can have on the engineering design process. Initial small-scale experiments to validate and quantify the effect as it relates to engineering design will be described. Development and modification of design tools to mitigate the impact of the decoy effect in the engineering design process will be described.

The Decoy Effect

Individuals are often faced with problems that have uncertain solutions. In such cases, the context of the situation becomes influential on the decision outcome 6. Such context effects could be the number of choices available, the quality of the choices, or the value that the individual places on the qualities of the choices. One may assume that individuals make choices based upon how they can best maximize value, but it has been found that is often not the case. Many studies have found that individual choice can easily be manipulated by the inclusion of a “decoy” alternative 3,5,7 .

The Decoy Effect (or Asymmetrically Dominated Alternative Effect), has been found to influence the decision making process when choice is uncertain. If two options are considered equally viable and a third option is introduced (the decoy), which is asymmetrically dominated Page 25.1316.2 by only one of the original options, individuals will likely choose the option that is easily compared to the decoy. Therefore, it is important to assess the context within which the decision is being made in order to understand the rationale behind the decision making process.

For example, imagine you are buying a car and you have decided there are two qualities that are of equal importance to you, cost and fuel economy. You have narrowed down your search to the following vehicles:

Car Fuel Economy Price A 28mpg $17,500 B 38mpg $24,500

It is expected that either alternative could be chosen because there is a balance between cost and fuel economy. Now imagine another alternative thrown into the selection pool:

Car Fuel Economy Price A 28mpg $17,500 B (target) 38mpg $24,500 C (decoy) 38mpg $26,500

The choice is now made easier because the decoy is asymmetrically dominating one alternative, in this case, choice B. Individuals are now more likely to choose car B, because it is clearly superior to car C and this reduces the cognitive strain of having to compare equally viable options. The Decoy Effect has been found to affect decisions in a variety of situations but has not been investigated in engineering decision making 2,3 4,5 .

The Decoy Effect can be seen as a way for individuals to minimize cognitive strain. The brain has limited resources and decision making requires the use of memory, attention, and prior knowledge, which could be inaccurate or inaccessible. Therefore, individuals often rely on simple and easy to access information, which leads to biases and the use of heuristics 8. Heuristics and biases, which can be seen as “short-cuts”, may have led us to appropriate decisions in the past, but they do not guarantee a successful outcome. On the other hand, when using an algorithmic approach, an individual will use a step-by-step, often complex, set of rules to come to a conclusion. If the set of rules are followed correctly, the individual will be lead to the correct result. Several studies have found that when an algorithm is taught, this process will be used more often than a heuristic when individuals are faced with complex decisions 9,10 . It is hypothesized that individuals enrolled in an academic engineering program will learn appropriate algorithmic ways of dealing with uncertain decisions (i.e. decision matrices or morphological charts) and use the algorithm instead of relying upon the heuristics and biases that untrained individuals may use. Page 25.1316.3

A Small Scale Study to Quantify the Decoy Effect in Engineering Design The engineering design process is essentially a process of structured decision-making under uncertainty; the engineer seeks to select a design solution from a set of alternative designs that meets a set of performance specifications and is optimal in some respect. 11 While many versions of the design process have been published, each includes some version of the following basic steps 12,13,14 :

1. A design problem is identified 2. The problem is defined and quantified using formal specifications 3. Many alternative design solutions are synthesized in response to the specifications 4. One “best” solution is selected from the various alternative solutions 5. Detailed design and optimization is performed to fully develop the selected design alternative 6. The completed design solution is evaluated, to ensure that specifications are met 7. The design is communicated to the customer

An important aspect of the formal design process is that while many alternative designs may be synthesized, there is generally only sufficient economic resources to fully detail one candidate design solution. Therefore, Step 5 indicates that only one “best” design can be selected from the list of design alternatives.

The selection of a single “best” design involves comparison and evaluation of the alternatives; since the Decoy Effect has been shown to influence such decisions in a variety of other topical areas, it is reasonable to believe that design engineers may be swayed in their evaluation of candidate designs by the presence of asymmetrically dominated alternatives. A reliable engineering design process would be one that would minimize the impact of the decoy effect on engineering decision-making, and allow for justifiable selection of an optimal alternative.

In order to quantify the impact of the Decoy Effect on the selection of an alternative design, the results of a design exercise used in two engineering design courses were evaluated. In each of these courses, the students were provided with the design case study shown in Figure 1.

The case study was designed such that while all three designs meet the specifications, Design A is asymmetrically dominated by the similar Design Alternative B in all categories related to the decision criteria (cost, size, weight, and reliability). Design Alternative C was chosen such that there was no clear choice between Alternatives B and C; tradeoffs between cost, size, and weight would need to be weighed to determine the “best” choice from these two alternatives. In order to validate the case study, the three design alternatives were presented to four mechanical engineering faculty members, two at a time. In all four cases, the faculty members judged Alternative B to be preferable to Alternative A, Alternative C to be preferable to A, and Alternatives B and C to be equally viable solutions. Page 25.1316.4 ______Assume the role of a Project Engineer working on the development of a new high-speed printing press. Three design alternatives have been proposed by the project team to act as the primary power transmission mechanism to drive the main line shaft with a 0.5 horsepower electric motor. All three designs meet the requirements for the system. You must select the best design alternative from the three proposals, based on a balance of cost, weight, size and reliability (all are equally important in this application). The three designs proposals are summarized on the following page.

Select one and only one of the three design alternatives as the choice for the machine. Write a brief (one-sentence ) rationale for your decision

Alternative A: Beltronics Sealed Belt Drive (uses toothed belt and pulleys)

Manufacturer: Beltronics Power Transmissions, Missasagua, Ontario Cost: $475 Weight: 41 lb. Size: 27” x 19” x 9” Reliability: 92% at 5,000 hrs. of operation Power Rating: .55 horsepower

Alternative B: TransPower Sealed Belt Drive (uses toothed belt and pulleys)

Manufacturer: TransPower Drive Systems, Cupertino, CA Cost: $300 Weight: 30 lb. Size: 22.0” x 16.0” x 7.1” Reliability: 97% at 10,000 hrs. of operation Power Rating: .75 horsepower

Alternative C: GearMax Sealed Gear Drive (uses helical gears)

Manufacturer: GearMax Transmission Components, Davis, CA Cost: $310 Weight: 29 lb. Size: 21” x 17” x 7” Reliability: 97% at 10,000 hrs. of operation Power Rating: .75 horsepower

______

Figure 1: Engineering design case study

Based on the design criteria, Alternatives B and C are equally viable solutions, and would be equally likely to be chosen as the “best” design when compared to one another. However, since Alternative A is inferior to both and asymmetrically dominated by the similar design solution posed in Alternative B, the Decoy Effect would be expected to disproportionally lead to the selection of Alternative B as the “best” solution. In order to assess the impact of the Decoy Effect, the results of the case study given to two distinct student populations were analyzed. Group 1 consisted of a group on mechanical engineering students in a design-based course who had not yet been exposed to the formal engineering design process in their coursework. Group 2 consisted of mechanical engineering students in a design-based course who had been exposed to the formal engineering design process, and had used the decision matrix as a tool for selection of design alternatives in previous coursework. While the groups were small (16 students participated in Group 1, and 18 students participated in Group 2), this small-scale experiment was designed to determine if the Decoy Effect would influence engineering decision-making, and if the exposure to the formal process of engineering design could mitigate the impact of the effect. The results for the two groups are shown in Table 1.

Table 1: Results of the Case Study

Percent Percent selecting B selecting C Group 1: No exposure to formal design 67% 33% Group 2: Exposed to the formal design process 44% 56%

Examination of the results show distinct differences between the groups. Group 1, which had no exposure to formal design and decision tools, showed a clear preference for the Alternative B; this seems to indicate that the preference of the group was influenced by the presence of the asymmetrically dominated alternative. However, Group 2, which had been exposed to formal design and decision-making tools, showed a more even split between Alternatives B and C. There is some indication that the use of a formal design methodology can mitigate the impact of the Decoy Effect by focusing on objective decision-making criteria instead of subjective assessments.

While the results of this small-scale study lack the statistical significance for objective conclusions (based on the chi square test) due to small sample sizes (n=16 in Group 1, n=18 in Group 2) they do suggest that the Decoy Effect can influence technical decision-making, but that the influence of the effect can be mitigated by education in formal engineering design

methodologies. This has implications in teaching engineering design; it suggests that formal Page 25.1316.6 design education is an important component in rational engineering decision-making. Using the Design Process to Mitigate the Decoy Effect

In the case study described in Figure 1, as in many experimental studies designed to test the decoy effect, great care is taken to include a single asymmetrically-dominated alternative. In practice, the presence of such a design alternative is unlikely. However, in the selection of a design choice from a set of available alternatives, it is possible that the presence of alternatives that are similar in concept could introduce such a decoy effect, and lead the engineer to select a design alternative that is better than similar alternatives , but not the best overall choice.

The small-scale study indicates that the decision matrix , a simple and widely-taught algorithmic tool for design evaluation, may be effective in mitigating the decoy effect. In a decision matrix, weighting factors are assigned to the design criteria, indicating the significance of each criterion in the decision-making process. Each design alternative is then evaluated with respect to each criterion, and an overall quality score for each design alternative is established by summing the product of the weighting factor and evaluation score for each criterion. An example decision matrix is shown in Figure 2.

Weighting Factors Design Cost Reliability Ease of Use Aesthetics Alternatives 100 80 75 30 Total Alternative A 8 4 7 3 1735 Alternative B 5 5 4 8 1440 Alternative C 6 8 8 2 1900 Figure 2: A typical decision matrix

There are numerous published algorithms for both assigning weighting factors and assigning evaluation scores for the design alternatives 14,15 . In most processes, however, the evaluation scores are directly assigned relative to the design criteria , and not with respect to one another. Therefore, it is likely that this “one-at-a-time” evaluation system would minimize the decoy effect, as design alternatives are not directly compared to one another.

A second commonly-used engineering design tool could also be implemented to identify and eliminate asymmetrically dominated alternatives. Morphological charts are often used to visualize the design space associated with an engineering task 13 . In a morphological chart, design attributes are listed in a column, and the various means for achieving those attributes are assigned to rows. By choosing one “cell” from each row, a new design alternative can be categorized. Typical morphological charts showing two design alternatives are shown in Figure 3.

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Feature/Function 1 2 3 4 Power source DC motor AC motor IC engine Hand crank Power transmission Helical gears Spur gears Belt drive Chain drive Bearings Ball Cylindrical roller Journal ***** Housing Sealed Shielded Open *** ** Figure 3: Morphological charts showing two alternative designs From a strict combinatorial standpoint, the morphological chart of Figure 3 implies that there are 144 possible design combinations (four power sources, four transmissions, three bearing styles, and three housing styles). While some combinations may be impractical, a morphological chart does show the basic size of the feasible design space. When addressing the design space with the purpose of identifying asymmetrically dominated alternatives, it is clear that alternatives similar enough to trigger the Decoy Effect would have significantly overlapping morphological charts. Therefore, such designs should be identified, and compared to one another using a simplified version of the decision matrix. Only one design alternative from the set of those identified as having significantly similar morphologies should proceed to the decision phase. Using this technique, any asymmetrically dominated alternative would be identified and eliminated prior to the decision phase.

An alternative technique for design decision-making that could be useful to minimize the Decoy Effect is to randomly pair design alternatives, and use a standard decision-making tool (e.g. decision matrix) to select the “best” of the pair. Alternatives that are not selected are eliminated; alternatives that are selected proceed to another round of random pairings. Eventually, through successive pairing and elimination, a single design alternative will remain. Since designs are only considered two at a time, the ability for an asymmetrically dominated alternative to influence decision-making is minimized. This “tournament-style” decision making has been implemented by student design groups at Milwaukee School of Engineering with success.

Conclusions

Engineering pedagogy has focused on teaching students algorithmic ways of coming to design decisions based upon multiple factors. When instructors do not take into account the context in which the alternatives are presented, the students may be influenced by that context. Although the results of the study did not reach a level of statistical significance, likely due to the small sample size (n = 16 in group 1, n = 18 in group 2), they do suggest that individuals trained in Page 25.1316.8 formal engineering design methodology are less likely to be influenced by the Decoy Effect. Further research is needed to assess whether the Decoy Effect is being mitigated by the engineering design decision-making training, or if there are other factors at work, such as a general improvement in critical thinking skills gained while attending college. When instructors are aware of heuristics and other contexts which influence thinking, the engineering decision- making process can be manipulated to mitigate those effects.

References:

1. Ariely, Dan, Predictably Irrational: The Hidden Forces that Shape Our Decisions , Harper Perennial, 2010. 2. Slaughter, J.E., E.F. Sinar, and S. Highhouse, “Decoy Effects and Attribute-Level Inferences”, Journal of Applied Psychology , 84(5), 1999, pp. 823-828. 3. Heath, T.B. and S. Chatterjee, “Asymmetric decoy effects on lower-quality versus higher quality brands:: Meta-analytic and experimental evidence”, Journal of Consumer Research , Vol. 22, January 1995, pp. 268- 284. 4. Wedell, D.H., “Distinguishing among models of contextually-induced preference reversals”, Journal of Experimental Psychology: Learning, Memory, and Cognition , Vol. 17, pp. 767-778. 5. Highhouse, S., “Context-dependent selection: The effects of decoy and phantom job candidates”, Organizational Behavior and Human Decision Processes, Vol. 65, 1996, pp. 68-76. 6. Simonson, I. and A. Tversky, “Choice in Context: Tradeoff Contrasts and Extremeness Aversion”, Journal of Marketing Research, 29 (3), 1992, pp. 281-295. 7. Huber, J., J. W. Payne, and C. Puto, “Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis”, Journal of Consumer Research, 9, 1982, pp. 90-98. 8. Kahneman, D., “A Perspective on Judgment and Choice: Mapping Bounded Rationality”, American Psychologist, 58(9), 2003, pp. 697-720. 9. Fong, G. T. and R. E. Nisbett, “Immediate and Delayed Transfer of Training Effects in Statistical Reasoning”, Journal of Experimental Psychology: General, 120(1), 1991, pp. 34-45. 10. Lehman, D. R., R. O. Lempert, and R. E. Nisbett, “The Effect of Graduate Training on Reasoning: Formal Discipline and Thinking About Everyday-Life Events”, American Psychologist, 43(6), 1988, pp. 431-442. 11. Musto, J.C., W.E. Howard, and R.R. Williams, Engineering Computations: An Introduction Using MATLAB and Excel , McGraw-Hill, 2009. 12. Budynas, R.G. and J.K. Nisbett, Shigley’s Mechanical Engineering Design , 9 th Ed., McGraw-Hill, 2011. 13. Dym, C.L. and P. Little (with E.J. Orwin and R.E. Spjut), Engineering Design: A Project-Based Approach , 3rd Ed., John Wiley & Sons, Inc., 2009. 14. Voland, Gerard, Engineering By Design , 2 nd Ed., Pearson Prentice Hall, 2004. 15. Anderson, D.O., “Making Engineering Design Decisions”, Louisiana Technical University technical report, 2000. Page 25.1316.9