Aesthetic pleasure is somehow related to the recognition of order. But what kind of order do we look for? And how much order do we need? Research on aesthetic appreciation shows that we tend to prefer combinations of stimuli that are both familiar and novel. Conversely, we can experience easily that situations in which either the familiar or the novel are predominating tend to be unpleasant, although for different reasons.
When the arrangement of elements in a composition is too predictable and obvious, we see triviality and feel boredom; when we have to deal with too much novelty, we feel overwhelmed and confused. So, order is fine as long as it is not trivial, novelty is welcome as long as we can handle it.
Think of any artistic performance or situation calling for an aesthetic judgment: a story needs to have some kind of recognizable structure, but also some element of surprise to not be easily predictable; a very conservative attire is anonymous but a very eccentric one can be too loud and disturbing; a new job proposal needs to offer us some fresh and exciting career prospects, but we want to make sure it falls in the professional domain and skills set we can master.
So, how do we measure the ideal mix of familiarity and novelty in an aesthetic arrangement? Help comes from a discipline one would not expect to contribute to this discussion (again, some surprise is always welcome): complexity science. Murray Gell-Mann, the American physicist who received the 1969 Nobel Prize in physics for his work on the theory of elementary particles,defines effective complexity as the shortest description of the perceived regularities in a system. He also provided a mathematical formula, but that is even harder to understand. What this academic definition really implies is a powerful as well as quite intuitive concept of efficiency in understanding. How can we express the most by saying the least? If we have to venture into a more straightforward, simplified formula, we could say that:
Effective complexity = Meaning/Information
Art comes into rescue, as always. Think of beautiful poetry or great literature. If you are a superb poet such as Pablo Neruda, you could express how much you love someone with the following verse:
I want to do with you what spring does with the cherry trees
If you are like the rest of us, you can just say “I love you” (precious, but quite boring) or write a very long and complicated love letter to express what loving someone really means to you (maybe exhaustive but overwhelming). What Neruda does, instead, through the careful choice of words and a beautiful visual metaphor, is to express the consequences of love for the loved one with a parsimony of signs.
Think now to products. Aren’t the best products those which fulfill this equation? When the interface is too complicated, it requires too much information processing effort to its users to figure out what they can do with that product. If it’s too simple, the product will be easy to use, but will be quite disappointing when it comes to what it can do.
These two extreme cases show us that there is good complexity and bad complexity. As the well-known designer Donald Norman said, “We need some complexity”. What we call simple, elegant interface is actually an effectively complex interface.
The relationship between complexity and aesthetic judgement is intriguing and is the angle from which we explore aesthetic thinking and its implications for design in this bl. We concluded that what numerous insights from different and apparently unrelated disciplines keep telling us in a variety of ways is that the pursuit of elegance is a way we deal with complexity. When effective complexity of a design is high, it means that the design can offer more complex functionality while requiring less effort for its users.
Our theory can be summarized in terms of entropy and effective complexity as in the picture reported below. Adding more novelty (entropy) can help us to add excitement to our design, but when it exceeds the threshold the users can tolerate, excitement turns into confusion and effective complexity crumbles into chaos. In order to reduce confusion, we can reduce entropy, typically by removing information and functionalities, but if we exceed, the design becomes dull, boring, and under performant. The process unfolds through a trial and error approach, via multiple attempts aimed at either simplifying or complicating the design in search of the sweet spot where effective complexity peaks.
This way of working is particularly visible in the process of artistic creation as well as in the pursuit of innovative, groundbreaking design. Good work of art typically requires painstaking experimentation and production of multiple iterations of intermediate products before the artist gets to the point at which a superior form of compositional equilibrium is achieved. Sometimes, the search for effective complexity develops along the whole life time of an artist, starting from the early imitation of mainstream art and classics, to some form of immature disruption, followed by mature accomplishment, and possibly decay.
Upon closer inspections, these attempts are steps in the search for some ideal form of expression in which intermediate outputs are either discarded because they are missing something (too simple) or because there is too much distracting and unnecessary information (too complicated).
Gell‐Mann, Murray, and Seth Lloyd. “Information measures, effective complexity, and total information.” Complexity, (1996): 44-52.