Representing information in different ways changes people's senses of reality. IDs have a suite of various authorware tools to present data - whether it be in tables, graphical representations, drawings, 3D models, audio-video, and a range of other data.

The Wikipedia offers a formal and informal definition of fractals. For my purposes, I'll quote their informal definition: "In colloquial usage, a fractal is a shape that is recursively constructed or self-similar, that is, a shape that appears similar at all scales of magnification and is therefore often referred to as "infinitely complex."

One of my former colleagues (now retired) gave me a mathematical poster of his when I admired it. This poster showed various fractals from nature. It also showed computer-generated fractals that represented bodies of statistical information. One, for example, showed the epidemiological tracking of the human spread of the HIV-virus. A small caption described the trails by which this virus spread - in very predictable and humanly social ways - with bars and social hangouts as nodes...roads of travel. I think this was evoking a spatial distribution. Given the exponential spread of the virus and incidence rates, the eye-catching geometric pattern was fascinating and frightening.

Today, to switch pace, I saw another fractal - this one created by HP Labs. This one appeared in a June 2006 issue of Computer World. This magazine ran an image of email paths at HP. Overlaid on top of the traffic was the lab's formal organizational structure. Not surprisingly, people often interact according to their place in a hierarchy, and there were some crossover communications, but likely along lines of social connection.

People do respond to what they know about the world - to a degree, and not always logically, and not always in their best interests. That said, how information is represented may result in "Eureka!" or "Aha!" moments of learning.

The two above examples, while diverse in subject matter, may well provoke various thoughts about how one's own choices and behaviors compare to the aggregate. I would hope that such information doesn't feed into the "regression to the mean" that so often happens with outliers...(averaging people's individuality). However, seeing one's behaviors in the context of the larger context may be helpful. There can be deep transferable learning depending on how information is presented.