Algorithmic specified complexity.

Information theory is a well developed field, but does not capture the essence of what information is. Shannon Information captures something in its definition of improbability as information. But not all improbable events convey information. Kolmogorov complexity captures the idea of information as something easily described. But not all easily described objects are information. The proposed Algorithmic Specified Complexity takes into account both Shannon Information and Kolmogorov complexity to gain a fuller evaluation of information. We demonstrate this concept and develop several examples. We show the low probability of high Algorithmic Specified Complexity. We apply the concept to both images and functional machines from the Game of Life.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2013

Author dc:creator

• Ewert, Winston.

Advisor dc:contributor.advisor

• Marks, Robert J., II (Robert Jackson), 1950-

• Information.
• Algorithmic specified complexity.
• Kolmogorov complexity.

Statement dc:rights

• Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/8829

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/8829