Measurement of Evolutionary Activity, Teleology, and Life

We consider how to discern whether or not evolution is taking place in an observed system. Evolution will be characterized in terms of a particular macroscopic behavior that emerges from microscopic organismic interaction. We de ne evolutionary activity as the rate at which useful genetic innovations are absorbed into the population. After measuring evolutionary activity in a simple model biosphere, we discuss applications to other systems. We argue that evolutionary activity provides an objective, quantitative interpretation of the intuitive idea of biological teleology. We also propose using evolutionary activity in a test for life.

[1]  H. H. Newman The Theory of Evolution , 1917, Botanical Gazette.

[2]  T. Dobzhansky The Biology of Ultimate Concern , 1967 .

[3]  Francisco J. Ayala,et al.  Teleological Explanations in Evolutionary Biology , 1970, Philosophy of Science.

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  S. Gould,et al.  The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[6]  A. Cairns-smith Genetic takeover and the mineral origins of life , 1982 .

[7]  Elliott Sober,et al.  Conceptual issues in evolutionary biology , 1984 .

[8]  Graham Cairns-Smith Seven clues to the origin of life , 1985 .

[9]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[10]  S. Kauffman,et al.  Autocatalytic replication of polymers , 1986 .

[11]  S. Kauffman,et al.  Adaptive Dynamic Networks as Models for the Immune System and Autocatalytic Sets , 1987, Annals of the New York Academy of Sciences.

[12]  Bernardo A. Huberman,et al.  The ecology of computation , 1988, Digest of Papers. COMPCON Spring 89. Thirty-Fourth IEEE Computer Society International Conference: Intellectual Leverage.

[13]  Christopher G. Langton,et al.  Computation at the edge of chaos: Phase transitions and emergent computation , 1990 .

[14]  W. Fontana,et al.  Algorithmic chemistry: A model for functional self-organization , 1990 .

[15]  M. Bedau Where's the Good in Teleology? , 1992 .

[16]  J. Maienschein Growth of biological thought , 1994, Nature.

[17]  Fumihiko Hashimoto,et al.  Can Machine Think , 1995 .

[18]  G. Price The nature of selection , 1995 .

[19]  J. Maynard Smith The units of selection. , 2021, Novartis Foundation symposium.