Information in biology
In the Stanford Encyclopedia of Philosophy Professor of Philosophy at Harvard University Peter Godfrey-Smith provides a useful definition of Shannon Information and biology.
Professor Godfrey-Smith is also author of “Information and the Argument from Design” which was part of the collection edited by Robert Pennock title Intelligent Design Creationism and Its Critics
In the weaker sense, informational connections between events or variables involve no more than ordinary correlations (or perhaps correlations that are “non-accidental” in some physical sense involving causation or natural laws). This sense of information is associated with Claude Shannon (1948), who showed how the concept of information could be used to quantify facts about contingency and correlation in a useful way, initially for communication technology. For Shannon, anything is a source of information if it has a number of alternative states that might be realized on a particular occasion. And any other variable carries information about the source if its state is correlated with that of the source. This is a matter of degree; a signal carries more information about a source if its state is a better predictor of the source, less information if it is a worse predictor.
This way of thinking about contingency and correlation has turned out to be useful in many areas outside of the original technological applications that Shannon had in mind, and genetics is one example. There are interesting questions that can be asked about this sense of information (Dretske 1981), but the initially important point is that when a biologist introduces information in this sense to a description of gene action or other processes, she is not introducing some new and special kind of relation or property. She is just adopting a particular quantitative framework for describing ordinary correlations or causal connections. Consequently, philosophical discussions have sometimes set the issue up by saying that there is one kind of “information” appealed to in biology, Shannon’s kind, that is unproblematic and does not require much philosophical attention. The term “causal” information is sometimes used to refer to this kind, though this term is not ideal. Whatever it is called, this kind of information exists whenever there is ordinary contingency and correlation. So we can say that genes contain information about the proteins they make, and also that genes contain information about the whole-organism phenotype. But when we say that, we are saying no more than what we are saying when we say that there is an informational connection between smoke and fire, or between tree rings and a tree’s age
Godfrey-Smith also pointed out, as have many ID critics before him, how Dembski and other “ID proponents of “Intelligent Design” creationism appeal to information theory to make their arguments look more rigorous.”
For instance Dembski likes to use the term information, rather than probability because the former can appeal to Information theory even though all he does is apply a transformation to a probability
To assign a measure of information to the event, you just mathematically transform its probability. You find the logarithm to the base 2 of that probability, and take the negative of that logarithm. A probability of 1/4 becomes 2 bits of information, as the logarithm to the base 2 of 1/4 is -2. A probability of 1/32 becomes 5 bits of information, and so on. In saying these things, we are doing no more than applying a mathematical transformation to the probabilities. Because the term “information” is now being used, it might seem that we have done something important. But we have just re-scaled the probabilities that we already had.
That’s the full extent of ID’s appeal to information theory, take the negative base 2 logarithm of a probability.
Despite all the detail that Dembski gives in describing information theory, information is not making any essential contribution to his argument. What is doing the work is just the idea of objective probability. We have objective probabilities associated with events or states of affairs, and we are re-expressing these probabilities with a mathematical transformation.
So what about some of the other terms used by ID proponents such as complexity? Surely that means something?
So far I have discussed Dembski’s use of the term “information.” Something should be said also about “complexity” and “specification,” as Dembski claims that the problem for Darwinism is found in cases of “complex specified information” (CSI). Do these concepts add anything important to Dembski’s argument? “Complexity” as used by Dembski does not add anything, as by “complex information” Dembski just means “lots of information.”
Again we find that ID’s usage of terminology adds nothing and only leads its followers into confusion as they have come to believe that these terms mean something else.
In other words, Complex Specified Information CSI all boils down to the following
That completes the outline of Dembski’s information-theoretic framework. Dembski goes on to claim that life contains CSI – complex specified information. This looks like an interesting and theoretically rich property, but in fact it is nothing special. Dembski’s use of the term “information” should not be taken to suggest that meaning or representation is involved. His use of the term “complex” should not be taken to suggest that things with CSI must be complex in either the everyday sense of the term or a biologist’s sense. Anything which is unlikely to have arisen by chance (in a sense which does not involve hindsight) contains CSI, as Dembski has defined it.
Or in other words
So, Dembski’s use of information theory provides a roundabout way of talking about probability.
Back to the age old creationist argument of improbability.
Richard Wein, Mark Perakh, Wesley Elsberry and many others have shown how Dembski’s ‘novel’ approach is neither novel nor particularly relevant as we lack sufficient resources to calculate the probabilities involved. In other words, the reason why ID is scientifically vacuous is because all it can contribute is a calculation of the negative base 2 logarithm of a probability and it cannot calculate said probability.