Thinking Fast and Slow in Design
How many times have we heard that design thinking is a buzzword and that practical applications are subjective and vague? Well, there is no universal remedy but a methodology.
Executive Summary
Many of you are probably familiar with Daniel Kahneman's popular book Thinking Fast and Slow. This book explains how our brains use two types of thinking. System 1 is fast and automatic, helping us make quick decisions without much effort, like recognizing a face. System 2 is slow and takes more effort, but it is used for solving complex problems or making important decisions, like doing math or planning.
A similar concept exists in Design science when we’re trying to solve complex design problems. Design thinking is a term that's often discussed in design literature, based on research into how designers think and work. Similarly, computational thinking comes from studies on how computer scientists think and solve problems.
Design thinking and computational thinking stand out from other forms of thinking in two key ways. First, each originates from a specific field—design, and computer science, respectively—and has been adapted for broader use beyond its original context. This makes them different from other well-known ways of thinking, like critical thinking and creative thinking, which have long histories dating back to ancient Greece and are broadly applicable. Second, design thinking and computational thinking are unique in that they are the only forms of thinking to become widely recognized since the early 21st century.
This work shows that design thinking and computational thinking work well together to solve complex problems and suggests that teaching both together can improve problem-solving skills.
Key Takeaways
Complementary Thinking Modes: The paper proposes that design thinking and computational thinking are complementary processes. Design thinking involves expanding the problem frame to capture its complexity, while computational thinking focuses on narrowing the frame to remove unnecessary complexity.
Dual Process Model: The authors introduce a dual process model where design thinking and computational thinking are seen as mirror images of each other. This model suggests that both modes are necessary and that thinkers can move fluidly between them to address complex problems effectively.
Practical Applications and Tools: The model encourages the development of tools that support both design thinking and computational thinking. For instance, in fields like architecture and UX/UI design, tools that facilitate movement between conceptual designing and detailed coding can enhance problem-solving capabilities.
Fluency Between Thinking Modes: Developing fluency in switching between design and computational thinking is seen as a valuable skill. This ability, termed epistemic fluency, allows professionals to be adept at various ways of understanding and solving problems, which is essential in many modern professions.
Design Thinking
Design thinking has many interpretations but generally refers to the way designers think and solve problems. It has its roots in the scientific study of design cognition and methods, which focuses on how people reason when facing design challenges. This way of thinking is often described as 'designerly ways of knowing, thinking, and acting.'
There are different perspectives on what design thinking means. Some view it as a cognitive style, others see it as a general theory of design, and some consider it an organizational resource. Despite these varying perspectives, a common thread is the situated nature of design thinking, meaning it is deeply embedded in the context of the problem being addressed.
Design thinking involves framing the problem, which means defining the boundaries and context of the design activity. This framing helps designers create solutions that are specific to the unique problem at hand. The idea of framing has multiple origins, including situated cognition, where knowledge is seen as context-dependent, and social constructs that help organize experience. This complex assemblage of interrelated knowledge forms the lens through which designers understand and approach problems.
Insights into design thinking reveal that designers have the ability to frame problems and reframe them to find desirable solutions. They work with both a problem space, which involves understanding the problem, and a solution space, which involves generating possible solutions. These spaces evolve together as designers refine their understanding and solutions simultaneously. This dynamic process is evident in practices like sketching, where unexpected discoveries can shift the direction of the design process.
In educational settings, design thinking is taught across various design disciplines, such as architecture, industrial design, engineering, fashion, and web design. Students learn to consider the user and context and conduct research to guide their design process. This approach is beneficial for addressing complex, "wicked" problems where variables are unknown, and solutions are not immediately apparent.
The solutions created through design thinking are usually specific to the problem and not directly transferable to other contexts without adaptation.
"For example, the architectural design of a house needs to be specific to the site where it is located – aspect, topography, landscape, surroundings and history – as well as its inhabitants – the specific needs of the people who will be living in it – to be considered an example of good design. The reuse of the design of a house from one design situation to another in cookie cutter fashion generally results in poor design outcomes. This tendency for design solutions to be specific to a design problem is common across different design disciplines."
This specificity underscores the unique and context-dependent nature of design work. Examples of outstanding design, like Jørn Utzon’s Sydney Opera House or Frank Lloyd Wright's Fallingwater, demonstrate exceptional capability in framing and reframing the problem to achieve innovative solutions.
Overall, design thinking is a complex, multifaceted approach to problem-solving that requires a broad and adaptable mindset. It emphasizes the importance of context, user needs, and iterative refinement of both the problem and potential solutions.
Computational Thinking
Computational thinking originates from computer science, a field foundational to modern innovation and problem-solving. Due to its significant impact on society and economies, it emphasizes the need for everyone, not just computer scientists, to develop skills in this thinking. Recognizing its importance, many countries have integrated computational thinking into their K–12 curricula, aiming to make it a core part of education worldwide.
Computational thinking involves solving problems, designing systems, and understanding human behavior using concepts fundamental to computer science. However, defining it precisely has been challenging, leading to two primary trends in definitions: one based on reasoning types and the other on the types of solutions produced. It is about using problem-solving approaches typical of computer scientists but useful for everyone.
A key aspect of computational thinking is abstraction—identifying patterns and generalizing from specific instances while ignoring irrelevant details. This skill helps people simplify complex systems and find commonalities across different situations. For example, breaking down the process of making toast into a step-by-step algorithm illustrates the importance of specifying only relevant steps.
Problems suited for computational thinking are usually structured and recurrent, meaning their solutions can be applied to similar problems elsewhere. This makes investing in computational solutions valuable, as they save time and resources by automating repetitive tasks. For example, creating software for automating workflows can have widespread applications beyond the initial problem it was designed to solve.
Overall, computational thinking requires framing problems in ways that allow for generalizable solutions, often implemented through algorithms. These solutions are typically transferable to other similar contexts, demonstrating the broad applicability and practicality of computational thinking in addressing a wide range of problems.
Relationship between both
Design thinking and computational thinking are two different ways of solving problems, and people often use both. Some tasks, like engineering a machine, mainly use design thinking, while tasks like sorting a list mostly use computational thinking. However, some tasks, like designing a UX/UI, need a mix of both approaches.
Two variables differentiate design thinking from computational thinking:
Generality/Specificity of Solutions: Design thinking tends to create solutions specific to the problem at hand, whereas computational thinking aims for general solutions that can be applied broadly.
Generality/Specificity of the Frame: Design thinking involves expanding the problem frame to capture its complexity, while computational thinking focuses on narrowing the frame to remove unnecessary details.
Utility of Ontology
The ontology presented in the paper is a useful tool for understanding how design thinking and computational thinking can be applied to problem-solving. This framework helps explain the relationship between these two types of thinking and how they can be used together effectively.
Figure 1. Space created by graphing the two orthogonal ontological categories, with design thinking and computational thinking located in the space.
For example, a car designer might begin by using design thinking to explore and understand the project's needs. This phase involves expanding the problem's frame and considering various possibilities. It is crucial for gaining a broad understanding of the problem and its context.
"All of this activity sits very much within the upper-left, design thinking quadrant. It is about expanding the frame for the problem by learning more about the client, the context for the problem and the cultural domain within which a solution will need to fit, and it is geared towards the creation of a one-off solution to meet the needs of the client."
Once a clear understanding is established, the designer may shift to computational thinking to develop technical solutions. This phase involves narrowing the problem's frame to focus on specific, actionable solutions.
"For example, a designer – even in the scope of one ‘overarching’ design problem – may develop a shape grammar (computational thinking focussed) and then apply that shape grammar as a part of finding a solution (design thinking focussed)."
The utility of ontology lies in its ability to show that thinkers often move between design thinking and computational thinking while addressing a single problem. This movement allows for a more comprehensive approach to problem-solving, leveraging the strengths of both types of thinking.
Stone Car
The ontology also highlights the importance of balancing generality and specificity in both the solutions and the framing of problems. It suggests that effective problem-solving often requires expanding the frame to understand the broader context (a design thinking approach) and then narrowing it to implement precise solutions (a computational thinking approach).
Conclusions
Design thinking, with its emphasis on expanding the problem frame to capture its complexity and computational thinking, focusing on narrowing the frame to distill actionable solutions, together provide a robust toolkit for tackling a wide range of challenges. This dual process model not only enhances our theoretical understanding of these cognitive approaches but also offers practical insights for their application in both educational and professional contexts.
By integrating these two modes of thinking into professional practice, we can foster more versatile problem-solvers who are capable of addressing the multifaceted problems of the modern world. This comprehensive approach promises to enhance our ability to navigate and resolve complex issues, ultimately contributing to more thoughtful, efficient, and innovative solutions across various domains.
Disclaimer: This Future Insight is the adaptation of the original research paper entitled: “Design thinking and computational thinking: A dual process model for addressing design problems" Written by Nick Kelly and John S. Jero. Originally published by Oxford University Press in “Design Science”
About this paper:
Kelly, N., & Gero, J. S. (2021). Design thinking and computational thinking: A dual process model for addressing design problems. Design Science, 7, e8.