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Orbital Maps Change Information Comprehension

Perhaps the most surprising consequence of designing information in orbital configurations is that we suddenly perceive the same information differently. Some information we understand using only words; other information is best experienced using words + objects + orbits. In fact, we realize some things—often the most important things—are hidden from view by using only an alphabetic matrix to present information. The way to prove this to ourselves is to jump in and experience information in more than one conceptual framework.

In short, to change the way we perceive information we must change the way we present that information. Until it is seen, this change of perception will frequently provoke more skepticism than belief. This is so, despite that fact that...

  1. Our inherited mental maps are no longer adequate.

  2. The amount of data is overwhelming our current alphabetic presentation models.

  3. The amount of data is increasing rather than decreasing.

  4. By visualizing the same data with new tools, we will enhance both our understanding of that data and its usability.

  5. By visualizing data, key stakeholders can better understand and use the information.

  6. The ultimate utility of information relies upon its ability to establish or reveal relationships. Current presentation models lack this ability.

Three other factors take our understanding to new levels so that the perceptual shift we are envisioning becomes clearer:

  1. Staged or stepped information orbits capture the layered, interconnected, simultaneous nature of information movement and management. This type of information map enhances connectivity and integration and is in stark contrast to the flat, linear, sequential dynamic of today's typical information presentation.

  2. In a fashion similar to the movement of our solar system, information orbits capture the time-and-change dimensions of information flow. At the same time, as they set up relational dynamics they also capture the three-dimensional depth of the information.

  3. While visualized similar to our solar system, these information orbits ultimately behave as neural networks. These information orbits can be expressed in levels of density from 1 through n. At each level of information density, the mapping function is recapped, appropriate to the particular stage.

    "An Artificial Neural Network is a system loosely modeled on the human brain. The field goes by many names, such as connectionism, parallel distributed processing, neurocomputing, natural intelligent systems, machine learning algorithms, and artificial neural networks. It is an attempt to simulate within specialized hardware or sophisticated software the multiple layers of simple processing elements called neurons. Each neuron is linked to certain of its neighbors with varying coefficients of connectivity that represent the strengths of these connections."

    From Daniel Kerfors, "What Are Artificial Neural Networks," St. Louis University School of Business Administration.

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