General Replicator Theory

Special Replicators

Kevin Aylward B.Sc.


Index

Definitions

Overview

This paper identifiers 3 special Replicators. 

Note-  it is immaterial to the theory how these Replicators work in detail. All that matters is that they achieve their function. For example, the Trait Selector might have a complex algorithm or a simple one. 

Trait Replicators

Assumption - there exists Trait Replicators, that can replicate traits.

Support for assumption:

Consider a trait of a Replicator, that increases its replication rate. Since this is a trait desired by Replicators, if a Replicator could copy that trait, i.e. replicate that trait, it would increases its own replication rate, provided that Replicator could determine whether or not such a trait did so for itself. 

It is held therefore, that given sufficient evolution generations, such Trait Replicators actually evolve that can replicate the traits of other Replicators.

Note: a Replicator that can replicate traits necessarily requires a physical structure that allows these traits to be stored to and extracted from, i.e. a read/write function.

Trait Selectors

Assumption - there exists Replicators with Trait Selectors, that select other traits.

Support for assumption:

Since traits of a Replicator are randomly generated, they may or may not tend to increase a Replicator's numbers. Clearly, it would be advantages if a Replicator was so generated such that it had the property of selecting out from these random traits, the specific traits that increased a Replicators numbers.

It is held therefore, that given sufficient evolution generations, such Trait Selectors actually evolve that can select the traits of its Replicator.

Note: a Replicator that can select traits necessarily requires a physical structure to implement an algorithm that allows these traits to be selected.

Trait Generators

Assumption - there exists Trait Generators, that generate traits.

Support for assumption:

Since the traits of a basic Replicator are externally randomly generated, there is no guarantee that such traits will aid in that Replicator's replication, or if there will be enough of them. Clearly, it would be advantages to a Replicator if it could generate its own traits.

It is held therefore, that given sufficient evolution generations, such Trait Generators  actually evolve that can generate its own traits. This obviously, requires that the Replicator has some physical method, such as an internal oscillator to generate traits. It is noted, for reference, that the human brain appears to have such a 40Hz oscillator.

There is no restrictions implied as to how these traits are generated. Traits might well be be generated simply by varying other traits of its host Replicator.

Freewill Trait Generator

It is suggested that the traits generated by the Trait Generator of the Intelligent Replicator, may be identified as a key component of the perceived freewill of a Conscious Replicator.

Definition - Freewill, is an internally generated trait of a Replicator, that is not deterministically derived from the external or internal environment.

It is, noted that the human Replicator, appears to generate traits not directly deduced from any prior held traits or trait inputs from its senses.

That is, the random trait generated by the Trait Generator is perceived to to be a trait not directly related to prior meme and gene programming.

However, in reality, it would appear that there is no real "I". Apparently, we are simply result of all our prior memetic and genetic programming, with some randomness thrown in for good measure. So, although we can be perceived as something that truly generates new ideas in principle, there is no entity that we can actually perceive as an "I" that has any control over them, as, by assumption, the newly generated random traits are random. If an "I" had any control over the randomness, than the trait would not be random. 

For reference, the argument here requires the no magic axiom


These papers may be freely copied only for non commercial use,

provided full credit is given to the author.

Kevin Aylward 2003 - all rights reserved