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Getting the most out of Shannon information

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Abstract

Shannon information is commonly assumed to be the wrong way in which to conceive of information in most biological contexts. Since the theory deals only in correlations between systems, the argument goes, it can apply to any and all causal interactions that affect a biological outcome. Since informational language is generally confined to only certain kinds of biological process, such as gene expression and hormone signalling, Shannon information is thought to be unable to account for this restriction. It is often concluded that a richer, teleosemantic sense of information is needed. I argue against this view, and show that a coherent and sufficiently restrictive theory of biological information can be constructed with Shannon information at its core. This can be done by paying due attention some crucial distinctions: between information quantity and its fitness value, and between carrying information and having the function of doing so. From this I construct an account of how informational functions arise, and show that the “subject matter” of these functions can easily be seen as the natural information dealt with by Shannon’s theory.

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Notes

  1. This measure is analogous to the thermodynamic measure of entropy (Bais and Farmer 2007).

  2. This isn’t to be confused with the differently-motivated “parity principle”, which can refer to an argument by proponents of Developmental Systems Theory (DST) that any sensible conditions met by DNA as an “information source” in development are also met by other resources (see e.g. Griffiths and Knight 1998). This is typically a criticism of chauvinism about DNA’s role in the developmental process, at the expense of other non-genetic developmental factors. Here I use “causal parity” to refer to arguments that a statistical definition of information fails to filter any “information-carriers” in biology from causes in general.

  3. Although a “starvation state” intended to limit the damage caused by damage might be.

  4. This is true in the case of “idiosyncratic risk”, in which every individual in the population experiences a different randomly-generated environmental state. However, when all individuals experience the same randomly-selected environment the fittest strategy will be the one with the highest geometric mean payoff, or expected log payoff. Thanks to an anonymous referee for pointing this out.

  5. The question of how to define prior probabilities in biological cases is a thorny issue. Since the question here is which of two phenotypes will do the best in the long-term, a frequentist interpretation based simply on how often each environmental state will be realised in the organism’s environment seems uncontroversial.

  6. Donaldson-Matasci et al. (2010) observe that some “proportional betting” strategy would be ideal in some circumstances, such as when one environment is lethal to one phenotype. For clarity purposes I deal with pure strategies alone.

  7. E needn’t necessarily refer to an aspect of the external, abiotic environment. It may refer to any system whose state has a bearing on the fitness value of the phenotypic state in question, such as a state of another organism, a tissue or organ elsewhere in the same body, or even in the same cell.

  8. These are adaptive claims in which the organism’s developmental environment is said to lack sufficient information to specify the state to which the trait is adapted, thus necessitating explanation in terms of inherited biases towards that trait acquired by selection, rather than adaptive phenotypic plasticity.

  9. It is uncontroversial that even signals typically considered to have semantic or intentional content also correlate with whatever they are intended to indicate. Aside from any debate about how words like “fire” come to mean fire in a semantic sense, it is still true that hearing someone shout the word should still make the presence of fire more likely, at least if it is to be worth acting on by listeners.

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Acknowledgments

I am grateful to Samir Okasha for advice and guidance through every stage of this research, and to Nicholas Shea, Christopher Burr, Kim Sterelny, and one anonymous referee for helpful comments on various versions of this paper. This work was supported by the European Research Council Seventh Framework Program (FP7/2007-2013), ERC Grant Agreement No. 295449.

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Correspondence to Oliver M. Lean.

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Lean, O.M. Getting the most out of Shannon information. Biol Philos 29, 395–413 (2014). https://doi.org/10.1007/s10539-013-9410-2

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