Abstract: |
We discuss a number of fundamental areas in which biologically inspired computing has so far failed to mirror biological reality. These failures make it difficult for those who study biology (and many other scientific fields) to benefit from biologically inspired computing. (1) The apparent impossibility of finding a base level at which to model biological (or most other real-world) phenomena. Although most computer systems are stratified into disjoint and encapsulated levels of abstraction (sometimes known as layered hierarchies), the universe is not.(2) Our inability to characterize on an architectural level the processes that define biological entities in both enough detail and with sufficient abstraction to model them.(3) Our inability to model fitness except in terms of artificially defined functions or artificially defined fitness units. Fitness to an environment is not (a) a measure of an entity's conformance to an ideal, (b) an entity's accumulation of what might be called "fitness points," or even (c) a measure of reproductive success. Fitness to an environment is an entity's ability to acquire and use the resources available in that environment to sustain and perpetuate its life processes.(4) Our inability to build models that allow emergent phenomena to add themselves and their relationships to other phenomena back into our models as first class citizens.These failures arise out of our inability as yet to fully understand what we mean by emergence. As an initial step towards surmounting these hurdles, we attempt to clarify what the problems are and to offer a framework in terms of which we believe they may be understood. We also offer a definition of emergence as the appearance of a process that produces a persistent area of relatively reduced entropy. |
