Category: Design Thinking

(4/4) DesignX

(See part 1part 2 and part 3 of 4)

In the spirit of muddling through, designers can deal with complex systems by “dividing and conquering”, ie, working in modules, numerous small and incremental steps.

Incrementalism as a strategy for dealing with large, complex systems has a respectable history. The major argument was put forward by the political scientist Charles Lindblom, made popular in his papers entitled “muddling through.”15 Incrementalism is the process of moving forward in small, considered steps, fitting the opportunities offered by each successive present, rather than by tackling the entire problem all at once with a single leap into an unknown future. Why? Because major projects involve so many cultural issues, changes in work practices, and changes in the division of work across different professional categories of workers, as well as strong contrasting viewpoints that make the political issues dominate, either leading to stalemate or requiring so many compromises that it is not feasible to make a solid prediction of the future state on the basis of current knowledge, so the future vision is extremely likely to overlook important emerging effects, and the project is slated for failure.

Muddling through” means acting opportunistically, taking whatever action is possible at the moment. Small steps do not ignite the passions as much as large ones, so they can often be approved. Moreover, success in small steps simplifies the approval process for future steps, whereas failure of a small step does not lead to failure of the entire effort. The operations don’t have to be perfect: they simply need to be approximations to the desired end result, to be “good enough,” or in Simon’s terms, they should “satisfice” rather than optimize.

So. All in all, what can designers do?

  • Modularity
  • Attention to social, cultural and political issues

(3/4) DesignX

(See part 1 and part 2 of 4)

HCD however, can be the design contribution to complex systems, in the sense that it treats the human part of these systems. It can observe the “social, regulatory, and economic pressures upon the people involved”. Still, there are four current limitations:

  1. Design is a tool and should work collaboratively with others
  2. Designers lack experience and methods in understanding inter-relationships (they are great with components, though). According to the authors, this is where designers must develop new ways of dealing with complex systems.
  3. The lack of consideration for human psychology and human factors in these complex systems
  4. Designers tend to focus upon the front of the development cycle, developing a clearly defined end-result, leaving implementation to others. With complex systems and services, as we discuss later in this paper, this is no longer a viable solution: designers must continue through the implementation stage.”

Although what has been discussed is how there are different perspectives looking and interpreting the same problems and how each intends to deal with them, the biggest, major problem lies in the implementation of large scale sociotechnical systems.

Even when all technical issues are solved – and I think all HMI professionals can identify with this – it is hard to implement the recommendations. The authors distinguish four properties as the source of most difficulties:

  1. System Design that Does Not Take into Account Human Psychology
  2. Human Cognition: The Human Tendency to Want Simple Answers, Decomposable Systems, and Straightforward Linear Causality
  3. Multiple Disciplines and Perspectives
  4. Mutually Incompatible Constraints

When some of these are combined (3 and 4, for example), compromises need to be made, and while some technical, social and cultural adjustments can be made, sometimes they can result in an absolute blockade of the resolution.

All in all, if designers don’t address these issues right at the beginning of the design stages, the implementation will very likely fail.

(2/4) DesignX

(See part 1 of 4)

These complex problems can be characterized by nine properties, divided into three categories (please read the article for full details on each):

 The Psychology of Human Behavior and Cognition

  1. System Design that Does Not Take into Account Human Psychology.
  2. Human Cognition: The Human Tendency to Want Simple Answers, Decomposable Systems, and Straightforward Linear Causality.

The Social, Political, and Economic Framework of Complex Sociotechnical Systems

  1. Multiple Disciplines and Perspectives
  2. Mutually Incompatible Constraints

 The Technical Issues that Contribute to the Complexity of DesignX Problems

  1. Non-Independence of Elements
  2. Non-Linear Causal Relations: Feedback
  3. Long and Unpredictable Latencies
  4. Multiple Scale Sizes
  5. Dynamically Changing Operating Characteristics

In 2015 Don Norman made a keynote talk at the Relating Systems Thinking and Design 4th Symposium.

Here, besides referring how designers are ill-trained for today’s problems, he made an interesting metaphor relating to those who are trained with a human centered design (HCD) perspective. The HCD method, because of its constant iteration and improvement, is like climbing one mountain. The mountain is the problem. HCD normally knows exactly what is the mountain, where’s its fixed place and its environs. The problem with complex systems, is that sometimes it’s not obvious which is the mountain, and due to their dynamic nature, their environs are always changing.

In this talk, he mentions how these systems are always “kind of working”, and how we normally don’t create systems, but manageable organizations which don’t interact with each other. “They don’t work necessarily well, but they muddle through quite well”.

(Continues in Part 3 of 4)

(1/4) DesignX

In 2014, The Design Collective authored a manifesto entitled “DesignX: A future path for design”. It focused on the propagation of complex problems to solve, and on where could designers play a role while still having a crafts-oriented education, focused on subject specificity.

Since then, much has been discussed as to the available tools to solve complex sociotechnical problems, like healthcare and education, and whereas one could argue DesignX is a new perspective on the subject.

In 2016, the paper “DesignX: Complex Sociotechnical Systems” was published in She Ji: The Journal of Design, Economics, and Innovation, written by Donald A. Norman and Pieter Jan Stappers. It is a very interesting piece of writing, as it summarizes this perspective of dealing with very complex problems, identifies current issues – like deficient communication – and details the core of most complex systems’ problems: implementation.

Complex societal systems include healthcare, transportation, automation, environmental protection, among others. These have a number of different technical or scientific components that make the system move and work when interacting. Fortunately, thanks to the late UX hype, designers have been included in the design of these complex systems, applying methods such as design thinking and human-centered design. Nevertheless, most designers’ education if focused on craft-like skills (2D, 3D). But when confronted with these big problems, it seems really hard to understand where that helps or fits.

The concept of a complex system is later exemplified with a project developed by UCSD’s Design Lab and Health Departments aiming at the enhancement of the care of cancer patients in Radiation Oncology. This treatment requires interaction with multiple specialists and a multi-disciplinary review board. Often these interactions are illustrated in complex flowcharts demonstrating relations and hierarchies. Boxes and arrows. Often as well, these arrows hide some of the most frequent problems: differences between disciplines, perspectives and priorities, schedules, available facilities, etc.

(Continues in Part 2 of 4)

Kansei Engineering

Kansei Engineering intends to translate people feelings and emotions into product parameters via empirical and analytical methods. The method was invented in the 70’s by professor Nagamachi, who recognized that companies often wanted to quantify their customer’s impressions on their products. Kansei Engineering can “measure” these feelings and correlates them with given product properties. This information will result in a product designed to elicit the intended  feelings.

“Kansei” means affection, or “sensuality” in the sense of…senses. It is also referred to as “Sensorial Engineering”, “Emotional Usability” or “Affective Engineering”. It captures feelings and translates them into a design solution.

When faced with a product or an object, a sensory input is firstly presented to the consumer. The first impression this input elicits on the consumer is the “kansei”, which reflects itself on feelings, affections and emotions. Shortly after, this “kansei” is processed and translated into “chisei”, that is, cognition. This step allows the consumer to apply logic, recognize and understand the input.

What’s interesting is that against all logic, sometimes consumers decide solely based on kansei, ie, their first impression and emotion.

This method presents people with a stimuli, and asks for an immediate response, both kansei as chisei – not necessarily alike.

A proposed model I found on project is states that firstly a manufacturer must provide the researcher with a well defined domain. This domain will be composed of “Semantic Domain”, that defines the target group, which words should one be looking for (eg. cool, elegant, nice). The component of the main domain is “Space of Application”, where the manufacturer identifies important properties for the user.

A synthesis of both components would be validated, and a mathematical model would be built, resulting in indications for product design (Category Identification; Regression Analysis /Quantification Theory Type I; Rough Sets Theory; Genetic Algorithm; Fuzzy Sets Theory).

Kansei data collection and analysis is often complex and connected with statistical analysis. Depending on which synthesis method is used, different computer software is used. Kansei Engineering Software (KESo) uses QT1 for linear analysis. The concept of Kansei Engineering Software (KESo) Linköping University in Sweden ( The software generates online questionnaires for collection of Kansei raw-data

Another Software package (Kn6) was devleoped at the Politechnical University of Valencia in Spain. Both software packages improve the collection and evalutation of Kansei data.

“Stupid Smart Stuff”

Don Norman chronicles are always interesting as he shares personal experiences with new interfaces, but quite often the last appeal is rather dramatic.

This time I agree, and wonder how come market sales are ignoring research on human factors and human safety (well, it doesn’t surprise me, of course).

Automation brought to the car can be tragic.

“In the airplane, the pilots are not attending, but when trouble does arise, the extremely well-trained pilots have several minutes to respond. In the automobile, when trouble arises, the ill-trained drivers will have one or two seconds to respond. Automobile designers – and law makers – have ignored this information.”

Read the full article here.