Complexity is an intrinsic part of life; it is normal and necessary, explains Norman (2011) in his book Living with Complexity. However, complexity is frequently pointed out as challenging and a product of our current society which makes communication and understanding harder. But is complexity the reason for poor sensemaking and misinformation design?
Complexity is a relative phenomenon with many factors involved:
Experience. So, first, where ever you are reading this post, stop reading. Pause and look around you. What can you see? How many examples of complexity can you see? In my case, I could point out loads: from the technology that makes my laptop work, the Internet connection, the grammar rules of each language, to how to operate my Black Berry and how to choose the best chocolate truffle from the box sitting on the kitchen table. All those situations have different levels of complexity, but none of them is less an example of complexity. However, for sure many of those situations are not as complex for others as they are to me. This is because our previous knowledge, i.e. how much or how little we know about something, determines the level of complexity we perceived and experienced about a situation, a problem, an idea, or a topic.
Motivation. Our motivation to understand something also plays an important role in our perceptions. For example, if we have a particular reason to make sense of a situation, we would probably not describe that situation as complex as if we would not have any motivation at all. Our efforts to gain understanding of that complex situation are driven by our desire to achieve a goal, therefore our perception of those efforts changes too.
Culture. Culture influences our perception of events and situations. In the case of complexity e.g. something perceived as complex in the West, might not be perceived with the same level of complexity in the East, and vice versa.
In other words, complexity is an ordered and reasoned combination of various interrelated factors (e.g. context, motivation, and experience, among others), and can be described as:
- Not the same as complicated (Maeda, 2006)
- Necessary and relative (Norman, 2011)
- Neither good nor bad (Norman, 2011)
- Unpredictable (2CO, 2013)
Simplicity refers to the right amount or balance of those factors. When all parts (e.g. goals, details, difficulties) are assembled together in a way that are fully understood, complexity is perceived as simple. A sense of clarity is achieved as each part makes sense and adds meaning. There is no information or part perceived as unnecessary. Interestingly, Norman (2011) suggests that there is no simplicity, as even the simplest device, idea or subject has some unknown aspects which generate uncertainty, therefore a level of complexity.
One way to make sense of complexity is by creating visualisations, but many of them aren’t doing a great job. Most of them have unclear messages which demand an excessive use of cognitive actions which make intended audiences challenge their attention, understanding and learning processes (Pettersson, 2000; Roberts, et al., 2013). This excess results in an increase of cognitive load and harder processing, as many cognitive actions are required to occur at the same time, i.e. understanding all components of a visualisation (graphic elements, textual elements), how they relate to each other (coding rules, structure), and the message (what does the visualisation communicate).
Therefore, complexity is not the problem, but it becomes a problem when it is not well-handled. Sometimes, visualisations can add more complexity, than clarity or simplicity. Poorly thought out decisions generate confusion, a recurrent information design problem frequently attributed to complexity (of content, ideas, languages, tools, etc). However, complexity does not equal confusion, or ambiguity or lack of understanding.
But don’t visualisations enhance understanding?
The mere visualisation of information does not improve understanding. There is a process behind the visualisation of (complex or simple) information that needs to be thoroughly followed to achieve effective results. The process starts with constructing understanding of the situation or content by making sense of the situation. In this initial phase we are unravelling the underlying structure of the situation, which is different from visualising any type of content (e.g. complexity). Learning the underlying structure of a situation provides order and reason to an apparent chaos, and helps distinguish relevant from irrelevant parts, and identify the way to (re)arrange them in order to obtain clarity.
Confusing information design causes confusion
There is confusion in information design practice, i.e. how to apply principles and guidelines. Information design principles, methods, guidelines and theories have been introduced in prior studies (e.g. Wurman, Tufte, Pettersson, Neurath, Bertin, just to name a few), but information designers either don’t adopt them as daily practice or are not familiar with them.
Regardless the media or the level of complexity, when information is visualised without addressing principles or responding to informed decisions, they create confusion and ill-understanding. Confusion is minimised and even neutralized when there is thorough understanding of the problem and audience’s needs, and the application of design principles and rules is not a blind or arbitrary process.
NB: This post emerged from the paper I co-presented at and learnings from the 2CO – Communicating Complexity International Conference (Alghero, Italy, 2013). The conference tackled the concept of complexity in information design, and talks discussed ways of visually managing, understanding and communicating complexity.
– Maeda, J. (2006) The Laws of Simplicity. Cambridge, MA: MIT Press
– Norman, D. (2011) Living with Complexity. Cambridge, MA: MIT Press
– Pettersson, R. (2000) Attention: An information design perspective. Document Design, 2(2): 114-130(17)
– Roberts, M.; Newton, E.J.; Lagattolla, F.D, Hughes, S. and Hasler, M.C. (2013) Objective versus subjective measures of Paris Metro map usability: Investigating traditional octolinear versus all-curves schematics. Human-Computer Studies, 71, 363–386.