Two different attempts to state the problem of creation of meaning in biological systems are concentrated on, one of which makes use of automata network theory and the other has to do with the so-called complexity from noise principle. In talking about self creation of meaning, a general introduction is in order to state the scope of the problem – since it would be self defeating to talk about meaning without noting the different meanings of meaning in different contexts of investigation and in different disciplines. I will then concentrate on two different attempts to state the problem of creation of meaning in biological systems. The first has to do with the so-called complexity from noise principle and makes use of probabilistic information theory to state formally, and in a kind of neg…
Two different attempts to state the problem of creation of meaning in biological systems are concentrated on, one of which makes use of automata network theory and the other has to do with the so-called complexity from noise principle. In talking about self creation of meaning, a general introduction is in order to state the scope of the problem – since it would be self defeating to talk about meaning without noting the different meanings of meaning in different contexts of investigation and in different disciplines. I will then concentrate on two different attempts to state the problem of creation of meaning in biological systems. The first has to do with the so-called complexity from noise principle and makes use of probabilistic information theory to state formally, and in a kind of negative way, (stemming from the fact that Shannon's information theory does not explicitly take into account the meaning of information), what the necessary conditions are for self-organization with increase in complexity (i.e., creation of information). The second approach which I will discuss makes use of automata network theory. It is applied to: (i) computer simulations of phenotypic expressions of genomes viewed as collective behaviors of large numbers of interacting genes; (ii) a computer model of a machine which is built randomly, i.e., with no purpose in mind, and whose behavior is that of a pattern recognizer where the pattern to be recognized is the outcome of the functioning of …