My understanding is that the theory of evolution is mathematically modeled as a stochastic process. Consequently, its predictions are probabilistic.
From a practical perspective, what does this mean? It might be helpful to look at another well-known, stochastic domain for insight. The domain I choose for this example is sports predictions (not that I wager money on anything personally).
Sports: Another Domain of the Survival of the Fittest
Let’s take a look at the 2018 World Cup. At the onset a month ago, some of the teams (Saudi Arabia, Morocco, Peru, Russia) seemed very weak and unlikely to advance even to the knock-out round. Others looked very strong and likely to go far into the knock-outs (France, Germany, Brazil, Belgium). How did these predictions fare?
They were mostly right, but there some notable exceptions. Tiny Croatia advanced all the way to the final, but mighty Germany did not even reach the knock-out stage. Huge exceptions!
Does these exceptions mean that Germany has a weaker team than Croatia? Almost certainly not; if they played a 20-game match over a 4 month period, I would expect (with near 100% certainty) Germany to prevail. Because the competition is not structured that way, exceptions are expected; outcomes can be predicted only probabilistically in a noisy, stochastic process such as a soccer tournament. (For our international readers, that’s football, not soccer.)
Do these exceptions mean that our basic premise–that stronger teams can be identified in advance and they can be expected to perform better–was wrong? Overall, no. Collectively, the stronger teams (France, Germany, Belgium, Brazil) performed far better than the weaker teams (Saudi Arabia, Morocco, Peru, Russia), in spite of the occasional contradictory outcome. The noise did not erase the signal.
Evaluating Models of Biological Origins
(1) Evolution
Evolution, as I mentioned, proposes a stochastic model. From the practical standpoint, this implies quite curiously that evolution predicts confounding observations–noise–just as sports prognosticators expect that occasionally Germany will fall early and Croatia will advance to the final. Biologists have even identified some of the confounding factors in evolution: for example, convergent evolution in phylogenetic trees, and incomplete lineage sorting in genomic-built trees.
At the same time, evolution predicts that that the forces of drift, mutation, flow, recombination, and natural selection will result in
- transitional fossils;
- the appearance of more homologous endogenous retroviruses in populations as they are situated more closely on a nested hierarchy;
- adaptations, exaptations and vestigial structures will appear throughout the domain of biology;
- pseudo-genes throughout the domain of biology,
- homologous pseudo-genes being more common among populations that are closer in a nested hierarchy; and so forth.
And indeed these predictions are borne out in observation.
(2) Design Model
I am not able to glean any predictions from Ewert’s paper, since he specifically rules out the validity of using his paper to draw comparisons between evolution and dependency graph models:
The focus of this paper has not been to critique common descent, but to the test the predictions of the . dependency graph hypothesis. (p.18)
In my next post, on the shortcomings of Ewert’s methods, we will see why Ewert recognized that his paper could not be used the way that ID proponents would wish to use it.
This does not prevent our friend @Cornelius_Hunter from critiquing common descent on the basis of this paper.
What I would like to see from the mild-mannered Biola professor (who leaps tall buildings in a single bound?) is what predictions a design model would make about data other than component-based vs. tree-based modeling. What would a design model predict with regard to:
- Homologous ERVs - Should ERVs be found in homologous or orthologous locations under ID? Should the ratio of homologous to orthologous ERVs vary based on the taxonomic distance of species?
- Vestigial structures such as the sightless lenses of marsupial moles where eyes are expected?
- Pseudo-genes (i.e., vestigial genes) such as the vitellogenin and vitamin C genes in H. sapiens.
- Noise. Dr. Hunter complains incessantly when conventional biologists attribute confounding observations to noise that nevertheless does not erase the evolutionary signal. Given robust predictions by a design model, would the design model nevertheless predict noise in real-world observations ? If so, why?
Dr. Hunter, I would appreciate very much hearing your thoughts on these questions.
Until we can get these predictions formulated, there is no way to compare evolution with a design model. Suppose for the sake of argument I were to grant Dr. Hunter’s assertion that, at the gene family level, dependency graph is 104 bits superior to a tree model. Given predictions by a design model for other classes of data such as ERVs, vestigial structures, and vestigial genes, we could still discover that the tree model is 1010 bits superior to a dependency graph. This would overwhelm the gene family model-building data.
In the absence of any predictions by a design model with regard to these classes of data, though, it is impossible to make any reasonable comparisons between a design model and evolution, other than to say that a design model is better at predicting one class of data, but evolution is infinitely better at predicting many classes of data.
If this is the choice that scientists must make, I suspect the vast majority would prefer a model that predicts many classes of data well over a model that predicts one class really well but is unable to make any other predictions.
Thanks,
Chris Falter