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Analogical Thinking in Design - Best Practices

Analogical reasoning is a thinking process that relies on analogy as a tool to draw inspiration from familiar situations to come up with creative solutions to problems. According to the Stanford Encyclopedia of Philosophy, analogical reasoning cites accepted similarities between two systems to support the conclusion that some further similarity exists. This process therefore allows for the abstraction of knowledge or insights from one space to another and can help drive creative solutions by leveraging prior knowledge of tangential problem/solution spaces. Examples of this type of thinking in design include biomimetic designs that resulted in the creation of Velcro (inspired by plant burrs) and many more. In this literature synthesis, my goal is to gather insights about the conditions and best practices that can prime designers to think more analogically. More specifically, I am interested in learning more about how designers in training like myself can enhance their ability to think more analogically.

When trying to think of analogous problems to generate novel ideas, it is best to focus on structural similarities between the target problem and the analogous than to look for superficial similarities.

When trying to think analogically, one may arrive at a variety of analogies that have varying levels of usefulness. It is therefore important to understand what makes an analogous domain useful in problem-solving/design. In two studies comparing novice and expert designers, Ozkan et al. and Ball et al. describe difference about the ways designers with varying levels of experience think when analogizing (Ball et al., 2004; Ozkan & Dogan, 2013). They describe that while novice designers use concrete prior examples as analogies for their target problem, more experienced designers exhibit more schema-driven approach to their problem-solving whereby they utilize schematized knowledge structures based on the automatic recognition of familiar types or categories of problems and solutions. While novices focus on superficial similarities, experts establish structural similarities between domains. Superficial similarity refers to the resemblance between the objects in the source and target and their properties whereas structural similarities refer to considering relationships between objects at a higher level in terms of their relations.

Similarly, Ahmed et al. sought to understand the use of analogies by design engineers with different levels of experience in an adaptive design domain through real-world data from aerospace design engineers (Ahmed & Christensen, 2009). Novices were found to predominantly transfer information related to the geometric properties without explicit reference to relevant design issues or to the appropriateness of applying the analogy, whereas experienced designers tended to use analogies for problem solving and problem identification. Experienced designers were found to use the analogy to reason about the function of a component and the predicted behavior of the component, whereas the novices seem to lack such reasoning processes.

This indicates that a step towards becoming a better analogical thinker may require a shift in the type of similarities one looks for in building analogies. Kim and al. even proposed a framework to evaluate the novelty of ideas generated using analogical thinking on the basis of their degrees of superficial and structural similarities (Kim & Horii, 2015). A low level of superficial similarity with a high level of structural similarity is associated with increased novelty.

Beyond novelty, an experimental study by Holyoak et al. aimed to examine the usefulness of analogs in problem solving based on their type of similarity (superficial vs. structural) (Holyoak & Koh, 1987). The findings of this study indicated that while both types of similarity the probability that an analogy would be used without an explicit hint, structural similarities had a greater impact on the participants’ ability to actually use the analogy when solving the target problem once they’ve identified its relevance. This means that while surface similarities help us identify analogous situations, structural similarities among high-level elements of both the source and target problems are more useful in problem-solving.

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To enhance one’s analogical thinking, one should therefore focus on expanding their relational reasoning ability (i.e., the ability to consider relationships between multiple mental representations, and thus ability to notice structural similarities.) This recommendation is supported by findings from a study by Dumas et al. where ideation success via TRIZ methodology was found to be correlated with increased scores on cognitive tests aiming to assess relational thinking abilities (Dumas & Schmidt, 2015). Priming oneself to focus on identifying abstract and high-level relational similarities between situations can therefore support one’s ability to utilize analogical thinking in their design/problem-solving work.

When trying to think of analogous problems to a given problem, it is beneficial to seek out distant analogies outside of the domain of the target problem.

Besides the type of similarities that analogies are built on, another important aspect relating to the quality of a given analogies lies in the distance between the source and target domains. Design teams will often use both in-domain and between-domain analogies. In a study by Christensen et al., the authors were interested in the interaction between analogical distance (within- vs. between-domain) and the stages of the early phases of design (problem identification, problem solving and concept explanation) (Christensen & Schunn, 2007). In vivo methodology was used to collect data base off meetings from one R&D subgroup at a medical plastics company. Problem identifying analogies were mainly within domain, explanatory analogies were mainly between domain, and problem-solving analogies were a mixture of within- and between-domain analogies. Based on these findings, analogies of varying distance are used at different stages of the design process in practice. It is however interesting to examine the effects of analogical distance on the utility and appropriateness of the analogy in design and ideation. An experimental study by Chan et al. aimed to answer this question (Chan et al., 2011). Senior-level engineering students generated solution concepts for an engineering design problem with or without provided examples drawn from the U.S. Patent database. Examples were crossed by analogical distance (near-field vs. far-field), commonness (more vs. less-common), and modality (picture vs. text). A control group that received no examples was included for comparison. Results showed positive effects of far-field and less-common examples on novelty and variability in quality of solution concepts. These effects are not modulated by modality. The combination of far-field, less-common examples resulted in more novel concepts than in the control group. Based on these findings, distant analogies provide the benefit of enhancing the novelty of generated concepts. Findings by Dahl et al. agree with this and indicate that even encouraging the access of multiple base domains during the idea generation stage can improve design originality in the absence of external primes (Dahl & Moreau, 2002).

In addition, Vendetti et al. report that generating solutions to far out analogous problems enhance relational thinking when compared to generating solutions to problems that are closer in terms of analogical distance (Vendetti et al., 2014). Relational thinking is associated with increase success ideation as mentioned above.

Deliberation, iteration and trial-and-error can be useful tools in enhancing the appropriateness of an analog

Finding the right analogy when solving a problem is a key step in successful analogical thinking. Seeking out structurally similar analogs from far and diverse domains is inherently a challenging process that requires effort and trial-and-error. Embracing this by giving oneself the time and and spece to actively engage in deliberation and iteration when building an analogy is therefore important in achieving success.

A study by Tseng et al. aimed to assess the effect of timing and analogical distance on the numbers of ideas generated and their novelty (Tseng et al., 2008). Based on an experiment where participants were prompted to solve the same design problem and were presented with relate information pre-problem presentation or after (during a break) it was found that highly similar information impacted problem solving even before problem solving began, but distantly related information only affected problem solving when it was presented during a break. These results indicate that taking the time to explore tangential domains after being exposed to the target problem increased the likelihood that distantly related information become incorporated into problem solving. These distantly related ideas may spur innovative or creative solutions to design problems.

In addition, two studies by Kim et al., have explored factors relating to deliberation during analogical thinking and their relation to ideation success as assessed by the novelty framework they developed in a pervious study mentioned above (Kim & Horii, 2015)  (Kim, 2017; Kim & Horii, 2016). During a workshop with 22 engineering students, the degree of trial and error during ideation by analogical thinking was measured by the number of domains considered for generating the final new idea. If a subject considered more than three domains, the degree was assessed as “high.” Moreover, if subjects deleted their previous notes more than five times before converging on the domain from whence their new idea was generated, they were regarded as having “high” trial and error. The results of these study suggest that a high degree of deliberation before reaching the creative leap moment and use of the trial-and-error tactic while finding the domain for a new idea enhances the appropriateness of generated ideas.

Experiential learning through failure and practicing the generation of solutions can support and enhance one’s ability to spontaneously think analogically when solving a problem.

One of the main difficulties in analogical problem-solving and ideation is that good analogies do not necessarily occur to designers spontaneously and require effort & time to arrive to. Experimental research by Spencer et al. indicates that analogical transfer requires that subjects be prompted to use a previously presented situation in solving a given problem, for them to utilize analogies (Spencer & Weisberg, 1986). When presenting subjects with the Dunker radiation problem (Duncker, 1945) after presenting them with two analogous stories, researchers found that analogical transfer occurred among subjects who were explicitly prompted to use the stories in solving the problem but the frequency of transfer was not different in subjects who received the stories but were not prompted and controls who didn’t hear the stories at all. These findings may raise some concerns with respect to the usefulness of analogical thinking in design, given that most real-world design settings offer no clear prompts or hints to use specific situations as analogs when solving a design problem. So how could designers use analogies in the often-unguided settings of design if thinking analogically doesn’t even occur to them unless explicitly prompted? This is why it is important to examine the conditions that not only prime analogical thinking but spontaneous analogical thinking to allow designers to best utilize analogies in their design work.

Spontaneous analogical transfer is the use of information from one problem to solve another problem, without an explicit hint to use the previous information In a study by Needham and Begg five experiments were run whereby subjects were tasked with solving a target problem after they either: (1) tried to solve a training problem before hearing its solution, (2) tried to explain a training story’s solution before hearing the correct explanation, (3) studied the same training passage for memory before hearing its solution or explanation. The results indicated that analogical transfer between the training problem and the target problem occurred spontaneously more frequently among groups 1 and 2 (Needham & Begg, 1991). The findings of this experimental study indicate that the process of attempting to generate solutions or explanations for solutions was helpful in priming participants to spontaneously analogize across problems. It seems as though the effort that goes into producing a solution or a reasoning behind a solution supports the ability to spontaneously recall the problem/solution when presented with an analogous situation. Research by Gick and McGarry goes further to suggest that even the failures experienced when solving an analogous problem can support spontaneous analogical thinking (Gick & McGarry, 1992). Study participants were presented with various representations of two analogous checkerboard problems where similar mechanisms lead to failed solutions. Findings of their experimental research showed that retrieval of the source problem was supported by noticing not only similarities between the two problem representations but also analogies between the failed solutions of the source and target problems.

Together, these findings indicate that in order to support their ability to spontaneously think analogically, designers could leverage their previous experiences if these experiences required them not only to think about problems superficially, but actually to engage in active problem-solving through the effort of generating and testing solutions. Approaching one’s learning in an active manner, by attempting to generate solutions and explanations for solutions even when reading case studies can support and solidify one’s ability to better utilize the learned problem-solving frameworks when confronting similar problems.

Being intentional in our approach to analogical thinking can support our ability to identify and utilize analogies.

In addition to all the different ways that one can train themselves to perform better on problem-solving tasks that benefit from analogical thinking, design science and cognitive science research indicate that by intentionally trying to think creatively about patterns that connect different situations, analogical thinking can be enhanced. In an experimental study by Weinberger et al., researchers aimed to assess the effect of a verbal cue to think creatively and look for abstract connections on participants’ ability to think analogically (Weinberger et al., 2016). Participants who received or did not receive the cue then completed an analogy finding task. Creativity was assessed via the following outcomes measures: (1) the total semantic distance of valid analogies identified, (2) the number of valid analogies identified, which is related but not informationally identical to the total semantic distance, and (3) the number of invalid analogies identified. Researchers found that this simple explicit creativity cue successfully elicited an augmentation of state creative performance in a novel open-ended analogical reasoning task. If a simple verbal cue is effective at improving performance on analogical thinking tasks, designers can use this to approach their design processes by methodically incorporating such cues into their processes. An example of this is presented through the framework below developed by Gassmann et al. is to show how firms enable and use analogical thinking for product innovation (Gassmann & Zeschky, 2008). The authors present 4 cases studies of engineering companies that had engaged in breakthrough product innovation based on the use of analogies. The cases show that analogical thinking does not happen merely by accident but is supported by means of a systematic approach and that “the will to break with conventional boundaries is paramount when searching for solutions that are non‐obvious and highly novel.” Based on the insights from the cases, the authors propose a generic process called  A4‐Innovation Process:

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There is a variety of systematic approaches that support analogical thinking such as synectics, TRIZ, bionics, and the lead user approach. Herstatt et al., proposed a systematic procedure based off of these existing approaches, which intends to support designers in retrieving, evaluating and using analogies in the context of innovation projects (Herstatt & Kalogerakis, 2005). The proposed systematic approach relies on two key processes: abstraction of the problem to highlight its contradictions, general conditions and view of the customer/user, and a networking/search process to find solutions for the abstracted version of the problem before analogical transfer (as seen in the schematic on the right).

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Following such frameworks can provide the structure and cues to expand their perspective and reach out to inter-domain structurally similar problems for inspiration. These systematic approaches and framework can play an important role in supporting the development of a designer’s intuition to think analogically in a spontaneous manner. Although they act as a structured roadmap, they also build the skills and qualities discussed above that can prime the designer to spontaneously think of analogies when approaching design problems.

Bibliography:

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