Design research? Design science? Scientific designers?
Design research? Where did this practice come from? As I began tracking this down, I found that the book The Sciences of the Artificial (1969) is often attributed to as the source of design thinking. This caught my attention. I usually focus on recent work. In fact, I was trained to check the expiration date on science, and supposedly scientific information expires every 4 years. But I want to know why this book has been so influential. Why has a fifty-year-old book informed so much of what we do as designers and researchers?
So what is the science of the artificial? This artificial part was confusing to me… what is an artificial science? The keyword here is artifice: which is defined as an ingenious device or expedient.
What makes for an artificial science is not that it is fake, but that design science is a set of tools that allow one to change existing situations into preferred ones, allowing us to examine them scientifically. So rather than simply describing things as they are, we are given the opportunity to investigate how things could be.
This is a profound move away from the natural sciences. It allows for the idea that a system is more than just the sum of its parts. This is science beyond reductionism, moving into opportunities to study messy problems and complex systems. Think of artificial as synthesis — putting things together in new ways and studying possibilities. It’s a science of method, rigor, complexity, and imagination.
So how does one participate in design thinking? Simon describes activities, principles, and tools for the training of a design scientist.
To be fair, I am biased in that I already consider scientists as creatives. I see the design of experiments as a very creative activity. How do we structure a problem as an experiment so that an insight results? How do we avoid garbage in, garbage out? Why should we view experimentation and testing a design activity? You are likely quietly answering these questions in your mind with your internal voice:
tests and experiments must be designed with a hypothesis about how people will respond to a context, interaction, or object. So we design the context, interaction, and object and put people in front of it.
For me, it makes sense to think of design and science together. Experimental design requires imaginative thinking. An experiment can include constructing variations in context, interaction, and objects to elicit and study behavioral response. What is important for Simon are the following four criteria to do this design thinking:
- Understanding: we must seek understanding of the user’s psychology, cognitive behavior, and problem-solving patterns in response to design, or artifice. Think of prototyping. This criterion is the core of user research. For Simon, we design a thing or situation and attempt to accurately understand the end-user, and use method to describe their behavior as a user model. This process becomes for us an effective way of determining how a program or model might be best designed — based upon user response.
- Optimization: a design scientist should have the tools, methods, and mindset to decide between different designs. Analytical techniques can be used to quantify different design alternatives based on their values to different stakeholders. In qualitative research, we can still have a hypothesis test, and if not, the ability to identify themes and patterns to generate and test a hypothesis. This takes into account, in my view, that quantities are qualities. One can quantify and count objects and qualities as frequency, duration, and numerically categorize and order. For me, quantities are qualities.
- Decomposition: This a little confusing. How far can you decompose a system before it is no longer that system? When does a decomposed system become its component parts? Is a tree still a tree if it has been shredded? This is an area of great interest for me. When is it a forest and not a tree? Levels and scale and organization are important. If we are constantly reducing to understand a system, we lose the forest for the trees! Complex Systems are defined based upon scale and level of organization
How does this work with design? As designers, we are asked to take a complex activity and design it so inexperienced users can have a successful, pleasurable experience. To create a simple experience from a complex activity, we must identify the core experience and off-load complexity until the user is ready to reach for it. This is a form of Tesler’s Law, which states that complexity cannot be eliminated from a system and still retain its integrity as a system, but that complexity can be off-loaded to provide a naive or unskilled user an introduction and successful experience in a complex activity, even when it is beyond their capability, knowledge, and skill. Let’s draw an analogy here (an example of the artificial): how does a 100 lb person lift a 200lb object?
A 100 lb person can lift a 200 lb object with a rope and pulley. The rope and pulley offload the large mass so it can be easily lifted. But what if they lose control? If the pulley has a ratchet, it will not fall back. A ratchet offloads the potential force to prevent it from violently falling back to the earth. A ratcheting effect allows the user to take action, and even make a change, and the ratchet holds things steady.
Think of video games. A player starts with simple actions and as the player crosses competency thresholds like navigation the player is presented more new, more complex actions, where complexity is redistributed back to the player as they gain skill and knowledge through progressive disclosure. The hypothetical ratchet may by that player fails multiple times in their goals, but they get do-overs without starting from the very beginning of the game. Progressive disclosure, ratcheting, and automation design work to off-load, or redistribute complexity (Dubbels, 2013; 2017; 2018).
To do this, the designer of a complex system must understand the core goals and intentions of the user:
Why are they doing this, what do they want out of it?
As designers, we study the system and look for ways to off-load complexity without destroying the character of the system. We identify the pain points and provide support. In many ways, this is the philosophy of Lean and Kaizen, where the core goal or intention of the user is identified, and the complexity is off-loaded until the user has the knowledge and skill to accomplish their goal without external support or tools (Ratchets).
Designing a system so that naive users can perform expert tasks requires getting to the root of the problem, or discovering the root cause. As design researchers, we use root cause analysis to identify the right problems to solve, and then groom questions as a form of “functional decomposition”, which is a process that allows a complex system to be split up into chunks. Each chunk can then be independently examined, conceptualized, designed (represented), ranked and delegated. A technique for this is presented below in figure 3.
4. Representation: representation is intended to create simplicity so that a problem can be represented in such a way that a solution eventually becomes transparent. Sketching and playing with models (approaches to a problem) can be more productive than jumping directly into a solution. This is where the science happens — making comparisons between ideas to see which works best for the potential user. This exploration phase is important if we are to experience true discovery and innovation. It is an act of imagination to visualize a problem and consider and potential approaches.
One of my favorite ideas from this section of the book is Simon’s celebration of how science is not intended to destroy or diminish wonder and creativity. He says,
for when we have explained the wonderful, unmasked the hidden pattern, a new wonder arises at how complexity was woven out of simplicity. The aesthetics of natural science and mathematics is at one with the aesthetics of music and painting both inhere in the discovery of a partially concealed pattern.
The world we live in today is much more a man-made, or artificial, world than it is a natural world. Almost every element in our environment shows evidence of human artifice … a significant part of the environment consists mostly of strings of artifacts called “symbols”…. that we receive through eyes and ears in the form of written and spoken language and that we pour out into the environment as I am now doing by mouth or hand. The laws that govern these strings of symbols, the laws that govern the occasions on which we emit and receive them, the determinants of their content are all consequences of our collective artifice, (Simon, 1969, pg 4).
This position helps to celebrate design and offers movement beyond description to creation while introducing scientific methods for replication, measurement, and learning. It is designed to observe behavior as a reaction.
Up next, chapter 2, Economic Rationality: Adaptive Artifice, where we explore the external context, inner experience, and humanity as rational actors.
