Complexity / Simplicity

Simplicity doesn't necessarily mean simple application. We look at the ability of Google to deliver a simple interface that's technologically complex.
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• Complexity, simplicity, simplexity - ScienceDirect
• BetterExplained Books for Kindle and Print
• The complexity of simplicity
• Simplicity can be Complex and Complexity really Simple

Today I continue to look at the concept of simplicity and its relationship to complexity by focusing on the work of John Maeda, designer, artist and president of the Rhode Island School of Design.

Maeda has devoted much of his career to understanding the role of simplicity in art, design, business, technology and everyday living and his book, The Laws of Simplicity , may be the most cogent analysis of simplicity in a manner that adheres to the very laws it espouses. The Laws of Simplicity outline 10 laws, of which most I agree with.

However, there are three that I see as problematic and, in some cases, actually inspire greater complexity rather than reveal or produce simplicity. I begin with Law 4: In the fourth law, Maeda argues that simple things often require knowledge to fully unlock their potential. One of the examples he gives is the screwdriver and the screw.

Using the examples of learners tackling new and difficult problems, Maeda discusses how the development and application of knowledge creates opportunities to create simple solutions by understanding the basics relative to the more complex parts — something systems thinkers might consider relating the entire system to the components within it.

Using the screwdriver example, this law becomes quite evident and could easily be supported. However, to use tools like screwdrivers as the metaphor, there are problems that require many tools working at the same time to solving them. It is here that a little information helps to a point, but then as starts to fall back on itself because the volume of knowledge required to fully understand things gets too much.

In complexity terms, this is where interactions and feedback enter and the previously independent points of knowledge converge, requiring someone to attend to multiple things at the same time. As the metaphor goes, the vise, the saw, the planer, the drill and the screwdriver all need to be thought of at the same time in order to solve the problem. Indeed, there is a point where more information helps, but my experience as an educator and health researcher suggests that there is a threshold in which knowledge sews confusion rather than yields insight.

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Below is a schematic drawn from my experience paired with insights from cognitive and information science that illustrates what happens when there is too much information. However, before reading this consider the following assumptions in which this model was based:. On page of this issue, Noorduin et al. We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail.

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Manipulation of crystal growth conditions results in the controlled formation of complex micrometer-scale shapes. You are currently viewing the summary.

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Log in to view the full text via AAAS login. Log in through your institution Log in via OpenAthens. Take the concepts of simplicity and complexity, for example. What is it for a thing or a system to be simple, and what is it to be complex?

These are simple enough questions, but the answers are anything but, and the search for answers takes us to the heart of computer science, physics and philosophy. On an intuitive, everyday level, we tend to associate simplicity with things that are uncomplicated and easy to understand; in fact, we assume that even a child can appreciate something if it is simple enough. Complexity, on the other hand, brings to mind the idea of difficult, complicated or sophisticated systems, problems or things.

In modern science, these concepts have taken on technical meanings and definitions, which accord with our intuitions only up to a point.

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The pioneers of artificial intelligence research at MIT were struck by the fact that tasks that appear to be difficult and complex, such as solving calculus problems, turned out to be relatively easy to do from a computational point of view, whereas things we would regard as simple and requiring little intelligence, such as tying shoelaces, still thwart even the most advanced robots.

Given all this, how do we even begin to get a handle on the notions of simplicity and complexity? One way is through the concept of algorithmic complexity. For any piece of information, programmers are interested in finding the shortest program that could print out that piece of information or string and then stop.