Design Science: Why, What and How
Ingress
The Design Science Journal is an international open-access journal that publishes original quantitative and qualitative research on the creation of artifacts and systems. This journal is one of the major journals within The Design Society .
In this Newsletter edition, we present an Editorial discussing the purpose and future of Design Science.
We at Intended Future picked several highlights that we believe might be most interesting in the context of this newsletter, but we strongly encourage you to read the original editorial article.
Executive Summary
The "Why" section of the discussion delves into the foundational motives driving the establishment and direction of the Design Science journal. It's rooted in the realization of the unique role that design plays in both solving complex problems and enhancing human experience through innovation.
The "What" section of the discussion serves as a vibrant mosaic of perspectives, painting a broad and dynamic picture of the current landscape and future possibilities within design science. Key themes emerge from this discussion, highlighting the interdisciplinary essence of design science, which straddles technology, human behavior, sustainability, and aesthetics.
The "How" section outlines the Design Science journal's strategic approach to realizing its vision, focusing on fostering a diverse and collaborative research community.
Highlights of Why
"Design as a recognized discipline is a relative newcomer in the research community. Design is both art and science. Approaching design knowledge with the scientific method does not and should not negate art’s presence in design; it is simply a matter of focus. Good design improves our lives through innovative, sustainable products and services, creates value, and reduces or eliminates the negative unintended consequences of technology deployment. Bad design ruins our lives."
Prediction is very difficult, especially if it’s about the future. – Niels Bohr
The difficult task: forecasting the future of design research
Forecasting the trajectory of design research presents a complex challenge. Typically, humans predict the future by linearly extrapolating from past experiences and current trends, which can be effective for short-term forecasts but tends to falter when applied to mid- and long-term predictions. It can be exemplified by the quote from IBM CEO Thomas Watson in the year 1943: ‘I think there is a world market for maybe five computers.’
The difficult past: historical development of design research
Reflecting on the historical evolution of design research reveals its progression through five distinct phases over the past 50 years:
Looking forward, three significant developments are poised to reshape design research:
Why Science
Design science operates at the intersection of various fields, embracing contributions from both the realms of 'Big D' Design and 'little d' design. This distinction highlights the breadth of disciplines involved in the design process, including engineering, industrial design, architecture, software development, and anthropology, to name a few.
The synergistic relationship between human and computer search will enable an iterative approach that is guided by human intuition and optimized by rapid computational exploration. Humans can inform computer strategy and computers can refine those strategies, returning more effective strategies to people.
"Science can be considered as the establishment of a formal body of knowledge through the collective and systematic efforts of a community of researchers. Scientific research seeks to define the basic principles underpinning natural and artificial phenomena, generating knowledge that is applied to support and improve human activities."
领英推荐
Design science holds the potential to make significant contributions to some of the most pressing global issues, such as combating climate change, addressing the needs of aging populations, enhancing health and well-being, and promoting sustainability. To maximize the impact of our collective efforts in building a robust scientific foundation for design research, it's crucial to address two main challenges: firstly, achieving a thorough grasp of the fundamental scientific principles underpinning design, and secondly, successfully synthesizing this knowledge into a cohesive framework.
replicating human intelligence through artificial means requires knowledge of the fundamental mechanisms and principles governing thought.
Design vs Science
Design Science often seems like an oxymoron, juxtaposing the creative intuition of design against the rational clarity of scientific inquiry. Historically, design and science have been viewed through contrasting lenses: science as a realm of repeatable experiments and universal truths, and design as the domain of unique, context-specific innovations celebrated for their creative genius. Yet, beneath these apparent differences, design and science share profound similarities in their processes of discovery and innovation.
Despite the traditional view of their separateness, the similarities between science and design, especially in their creative cores, are undeniable. Advances in technology, such as machine learning and brain imaging, offer unprecedented opportunities to model and observe the intricate processes of creativity and innovation. As these tools begin to unravel the "messy process" of design, the science of design emerges as a pivotal field, poised to unlock deep insights into human cognition and its capacity to shape the world. In this light, understanding design through the lens of science does not just add clarity to the creative process but elevates design to one of the most significant scientific frontiers of our time.
Herbert Simon was explicit in this when he called design the ‘Sciences of the Artificial.’
Probably the most interesting part for Intended Future followers
Here we highlight the opinion of Jordan J. Louviere, author of Best-Worst Scaling methodology that we use in our products. The experience that Jordan J. Louviere described below deeply resonates in our hearts. What happened then is happening today in many or probably in most car design studios.
Jordan J. Louviere
"My expertise is in understanding, modeling and predicting (forecasting) human decision-making and choice behaviour. I do not design things, nor do I participate in the design of things (except occasionally and by accident). However, I have a deep interest in the outcomes of designs and design processes. Over the last 30+ years I have participated in many projects with the objective of forecasting the demand for (i.e., uptake of) and willingness to pay for changes to existing product and service configurations and/or new-to-the-world products and technologies across a wide array of product and service categories ranging from aeroplanes (e.g., the B787) to zoological preservation proposals (e.g., Woodland Caribou in Alberta). I developed the original theory and methods known as Discrete Choice Experiments (DCEs, or more recently, Best–Worst Scaling, Case 3) now routinely used by many companies and government agencies around the world to meet this objective.
During that time I worked with many companies dominated by IT and engineering groups who routinely developed 20–50 or more designs that were launched, with the expectation that perhaps 1–2 would ‘succeed’ (always arbitrarily defined). I also worked with companies that were convinced that they really did not need advanced market information because they ‘led’ the market. The popular press is fond of highlighting such companies, but the ones that succeed in this approach over long periods of time are few and far between. And, quite frankly, it is time to end the practice of somehow believing that one or a small group of persons somehow has the ‘secret sauce’ and will always succeed.
The Nobel Prize was awarded in Economics to finance researchers who conclusively proved that no one could consistently beat or time the market, yet many continue to believe that there are ‘special people’ out there.
It is not true for finance and it is not true for companies. Indeed, I have been studying how well managers in real companies actually understand the choices that their customers make since 1984, when I showed the CEO of a large resource company that his sales staff not only did not know how customers made choices, but they consistently did things the customers did not like. After retraining the sales staff, the company shot to number three in the industry in one year and was acquired by the industry leader shortly after. My observation that managers do not understand customer choices has not changed since 1984, which includes a cross-section of companies and government agencies.
Thus, I conclude that humans need all the help they can get to understand markets and the customers in them. This is especially true of rapidly changing or new and emerging markets. I like to describe the problem with the table below. Despite all the hype, so-called ‘big data’ largely applies to current products in current markets, with the possibility that real-time and/or quasi-continuous updating could provide feedback about markets where data are available. For cases involving new products in new markets, there are by definition no data available; hence, there is a clear need for theory and methods that can help us to better understand and predict these cases."
Conclusions
This editorial was published in 2015 and is still relevant today. While we are witnessing a revolution induced by large language models, the question of methodologies that drive LLM in helping humans make the right decisions is more relevant than ever.
Disclaimer: This Future Insight is the adaptation of the original editorial article entitled: “Design Science: Why, What and How". Written by Panos Papalambros. Originally published by Oxford University Press in “Design Science”
About this paper:
Papalambros, P.Y. (2015) ‘Design Science: Why, What and How’, Design Science, 1, p. e1.