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Information, complexity and generative replication

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Abstract

The established definition of replication in terms of the conditions of causality, similarity and information transfer is very broad. We draw inspiration from the literature on self-reproducing automata to strengthen the notion of information transfer in replication processes. To the triple conditions of causality, similarity and information transfer, we add a fourth condition that defines a “generative replicator” as a conditional generative mechanism, which can turn input signals from an environment into developmental instructions. Generative replication must have the potential to enhance complexity, which in turn requires that developmental instructions are part of the information that is transmitted in replication. Demonstrating the usefulness of the generative replicator concept in the social domain, we identify social generative replicators that satisfy all of the four proposed conditions.

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Notes

  1. The authors are very grateful to Marion Blute, David Hull, Pavel Luksha, Peter Richerson, Kim Sterelny, Viktor Vanberg, anonymous referees and several others for comments and discussions.

  2. See Dawkins (1983), Hull (1988), Dennett (1995), Hodgson (2004, 2005) and Hodgson and Knudsen (2006a).

  3. Wimsatt (1999) rejects the replicator-interactor distinction but retains the genotype-phenotype concepts. This is inconsistent if we accept the former distinction as a generalization of the latter (Brandon 1996).

  4. Aunger (2002) refines Sperber’s (2000) definition by adding a fourth condition of “duplication”, meaning that replication of one entity gives rise to two or more. We are unconvinced that Aunger’s fourth condition of “duplication” is necessary or useful. The critical issue in understanding the process of replication is the nature and function of the structure that is passed on, including any information that it might hold.

  5. Note Hull’s (1988, p. 408) definition of an interactor as “an entity that directly interacts as a cohesive whole with its environment in such a way that this interaction causes replication to be differential.” See Hodgson and Knudsen (2004b) for a refinement of this definition.

  6. Even so-called benign computer viruses can be mildly destructive for the computer systems they infect, by taking up computer memory, causing erratic performance or triggering system crashes.

  7. We use a binary string as a useful simplification for expositional reasons.

  8. Prior research on self-replicating automata (Molofsky 1994; Wolfram 1984, 2002) has typically excluded such errors.

  9. Note that, if it did exist, “Lamarckian” inheritance could have similarly negative cumulative effect. Lamarckism upholds that the acquired characteristics of the interactor may affect the replicator, leading to the inheritance of these acquired characteristics. In biology, Lamarckian inheritance is widely ruled out because of the Weismann barrier. Although social evolution is widely described as Lamarckian, this idea is contested (Hodgson and Knudsen 2006b).

  10. We used sequences of length 100, but additional simulations show that our results are robust for much longer sequences.

  11. Similarity in neural connection design, even with similar genes and environment, is very unlikely (Edelman 1989). Underlying neuronal configurations supporting similar habits are likely to differ between individuals.

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Authors and Affiliations

  1. The Business School, University of Hertfordshire, De Havilland Campus, Hatfield, Hertfordshire, AL10 9AB, UK

    Geoffrey M. Hodgson

  2. Department of Marketing and Management, University of Southern Denmark, Odense Campus, 5230, Odense M, Denmark

    Thorbjørn Knudsen

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  1. Geoffrey M. Hodgson
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Correspondence to Geoffrey M. Hodgson.

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Hodgson, G.M., Knudsen, T. Information, complexity and generative replication. Biol Philos 23, 47–65 (2008). https://doi.org/10.1007/s10539-007-9073-y

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