This is part 3 of a series of 7 posts by Lars Johan Erkell, with comments on each by Ola Hössjer and a reply by Erkell. Part 1 will be found here. They are translations of 2020 posts in Swedish from the Biolog(g) blog of the Department of Biology of Gothenburg University.

Breakthrough for Intelligent design? (part 3)

November 13, 2020

by Lars Johan Erkell (with comment by Ola Hössjer)

Miracles as science?

Thorvaldsen and Hössjer give a number of examples of systems that they think are suitable for the statistical model we discussed in the previous post. One of the examples concerns human evolution.

One may also study the more restricted problem of human/chimp ancestry, and compare a model M2 with common ancestry of the two species, with a unique origin model M1, according to which each species is founded by one single couple (Sanford and Carter, 2014; Hössjer et al., 2016a; 2016b, Carter et al., 2018, Hössjer and Gauger, 2019).

Since Thorvaldsen and Hössjer raise this question as suitable to study with their statistical model, we shall look at it more closely.

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Photograph by Al Denelsbeck.

Photography contest, Honorable Mention.

This is an additional post by Lars Johan Erkell made on September 8, 2021 in Swedish at the Biolog(g) site after the seven posts of his “Breakthrough for Intelligent Design?” series were made. He has requested that we post it here after the second post in that series, to explain his position in that post on what can be considered legitimate science.

A central point in the discussion I have with Ola Hössjer in the posts entitled "Breakthrough for intelligent design?" concerns the limits for what can be considered legitimate science. Since my responses to his comments on this matter are too long for the comments section, I have made a separate post on the subject.

The central question is how to formulate a hypothesis, that is, a suggested explanation for a phenomenon. Hypotheses are not formulated in an arbitrary way; you don't just grab something out of the air. A hypothesis should be based on prior knowledge. It should also be falsifiable, which means that it must be possible to show that it is incorrect. The scientific work then consists of testing the hypothesis. Whether it proves to be true or false, something new has been learned. The important thing here is that the hypothesis is formulated in such a way that it can be tested in practice with a clear-cut result. If you cannot do that, you cannot learn anything new. And if you cannot test your hypotheses, you cannot root your theories in the real world.

However, just because a hypothesis is falsifiable does not mean that it is scientific. For example, "the Moon is a cheese" is a falsifiable (and falsified) hypothesis. But pure nonsense. If we want to give it a scientific touch, we can formulate it as a classical syllogism:

Premise 1: The Moon is yellow, round and has craters

Premise 2: A cut cheese is yellow, round and has pits that look like craters

Conclusion: The Moon is a cheese

This conclusion is not just grabbed out of thin air; it is based on an analogy. But it is still nonsense. My point here is that just because a hypothesis is falsifiable, or is expressed in a formally correct manner, that does not mean it is plausible or even makes sense.

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This is part 2 of a series of 7 posts by Lars Johan Erkell, with comments on each by Ola Hössjer and a reply by Erkell. An eighth post by Erkell is near this one, put here because he refers to it. Part 1 will be found here.

Breakthrough for Intelligent design? (Part 2)

November 13, 2020

by Lars Johan Erkell

How do you calculate design?

What stands out about Thorvaldsen and Hössjer’s work, and what makes me write these blog posts, is that they raise the question of how one could demonstrate fine-tuning and design using scientific methods. In any case, how one could imagine doing so; it doesn’t get more concrete than that.

Thorvaldsen and Hössjer carefully discuss the statistical methods they use, or rather believe that one can use. The main line is – as always with ID – that you try to support ID by primarily criticizing the theory of evolution. But you cannot prove theory A by showing that theory B is bad. The authors are also aware of this. They write in section 6.3:

However, one may argue that the most suitable approach of science is to compare the best explanations found so far within two competing worldviews. This naturally leads to model selection. … It is possible for some problems to suggest a design model M1 that competes with the currently most promising naturalistic model M2, in terms of which model explains the data the best. The authors therefore want to compare an evolutionary model with a design model by using statistical methods to determine which of them is more probable. And it is clear that the intention is to make the comparison based on concrete data. But from which models, and from which data?

In their critique of evolutionary theory, a number of well-known themes are addressed, primarily “irreducible complexity” on several levels; in individual proteins, in protein complexes and in entire networks. Also the “Cambrian explosion” and the so-called the waiting time problem is addressed. What these have in common is that the authors believe that the structures of life are so complex and thus so improbable that they could never arise naturally. However, this is not a view shared by established science – here the arguments are seen as contrived.

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This exchange consists of seven posts, each with a comment by Ola Hössjer and a reply by Lars Johan Erkell, plus one other post by Erkell. It is a translation, mostly by Google Translate, from the original 2020 Swedish posts and comments. Erkell and Hössjer have kindly corrected the translation. You and they are welcome to comment at Panda’s Thumb. Keep in mind that the posts and these appended comments were written two years ago and thus cannot take more recent comments here into account. We hope to publish the sections on seven consecutive Wednesdays (we chose the day of the week without remembering its connection to ancient days in Sweden). Readers and these authors are invited to contribute further comments in our Disqus comment system.

This week’s post starts with an introduction by Lars Johan Erkell, followed by the first of his posts at Biolog(g) and a comment by Ola Hössjer followed by a rejoinder by Lars Erkell.

Introduction

by Lars Johan Erkell

A breakthrough for Intelligent Design?

Two years ago, Steinar Thorvaldsen and Ola Hössjer, professors of Informatics and Mathematical Statistics, respectively, published an article titled “Using statistical methods to model the fine-tuning of molecular machines and systems” in “Journal of Theoretical Biology” a regular scientific journal. Within the Intelligent Design (ID) community the publication was seen as a breakthrough for ID as science. A close reading of the paper, however, is not convincing. In seven blog posts on “Biolog(g)” (a blog run by the staff at the Institute for biology and environmental science at the University of Gothenburg, Sweden) I therefore have detailed my critique on four specific points, and also asked how this paper could be published.

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