Sbagliando s'impera

Dawkins, Richard (2019). Outgrowing God: A Beginner’s Guide. London: Transworld. 2019. ISBN: 9781473563513. Pagine 294. 11,99€.
[Diventare più grandi di Dio: Una guida all’ateismo per principianti. Trad. it. Laura Serra. Milano: Mondadori. 2019. ISBN: 9788852097515. Pagine 265. 11,99€]

Non il migliore dei libri di Dawkins, ma comunque una lettura affascinante.

Ho letto molti libri di Dawkins, a partire dal folgorante The Selfish Gene (Il gene egoista) del 1976, ma su questo blog – oltre ad alcuni riferimenti sporadici – troverete soltanto la recensione di The Greatest Show on Earth, qui.

Preferisco, nella sua produzione, le opere intese a spiegare il darwinismo a quelle dedicate al “nuovo ateismo”. Questo Outgrowing God appartiene al secondo di questi filoni e ripete molte delle tesi, delle argomentazioni e degli esempi già enunciati in The God Delusion (L’illusione di Dio) del 2006. Anche se non lo si dice del tutto esplicitamente, è destinato ai più giovani (“A Beginner’s Guide” nel titolo, “this new book, written for a new generation” nella quarta di copertina, “For […] all the young people when they’re old enough to decide for themselves” nella dedica). Loro, i giovani, avranno trovato il libro fresco, ed esplicativo senza essere pedante (“theos is Greek for ‘god’ and poly is Greek for ‘many’”, p. 4). Io, che giovane non sono, ho dovuto a tratti tenere a freno la mia impazienza.

Benché le argomentazioni a favore dell’ateismo siano chiare e convincenti, le ho trovate un po’ scontate. Quelle di riassunto e spiegazione dell’evoluzione, invece, offrono qua e là spunti nuovi e nuovi esempi: sono queste le pagine che ho letto con più interesse.

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Qualche citazione:

The smaller the change, the closer the probability gets to 50 per cent that it’s an improvement.
[…]
Darwin realized that successful mutations are nearly always small. But the mutations that scientists study are usually large, for the obvious reason that small ones are hard to detect. (p. 232)

Think of the economic calculation that a plant, such as a potato, has to do. A plant is a good example, because while we might be tempted (wrongly) to think that a gazelle or a cheetah or a horse does calculations in its head, nobody could seriously imagine that a plant does sums. And doing calculations consciously is exactly what we are not talking about. The equivalent of calculations is done by natural selection over the generations. So, back to the potato plant. It has a limited amount of ‘money’ to play with. ‘Money’ here means the energy resources that ultimately come from the sun, turned into the currency of sugar and often stored as starch, for instance in a potato tuber. The plant needs to spend some money on leaves (to take in sunlight to make yet more money). It needs to spend some money on roots (to take in water and minerals). It needs to spend some money on underground tubers (to store money for next year). It needs to spend some money on flowers (to attract insects to pollinate other potato plants and spread the genes – including genes for getting the spending decisions right). Potato plants that get their ‘calculations’ wrong – perhaps not spending enough on tuber storage for next year – are less successful in passing on their genes. As the generations go by, plants that get their economic sums wrong become less numerous in the population. And that means that genes for getting economic sums wrong become less numerous. The population ‘gene pool’ becomes more and more full of genes for getting the economic sums right. (p. 238)

Yes, DNA is a digital code, just like computer code. And yes, DNA transmits digital information from parents to children and so on down countless generations. But no, the information transmitted is not a blueprint.
[…]
If DNA is not a blueprint of a baby, what is it? It’s a set of instructions for how to build a baby, and that’s a very different matter. It’s more like a recipe for making a cake. Or like a computer program whose instructions are obeyed in order: first do this, then do that, then if so-and-so is true do … otherwise do … and so on for thousands of instructions. A computer program is like a very long recipe, complicated by branch points. A recipe is like a very short program, with only a dozen or so instructions. And a recipe is not reversible, like the building of a car or a house is. You can’t take a cake and reconstruct the recipe by taking measurements. And you can’t reconstruct a computer program by watching what it does. (p. 269)

When ‘design’ emerges from the obeying of simple rules, where there is no overall plan in existence, anywhere, it is called ‘bottom-up’, as opposed to ‘top-down’, design. (p. 271)

The only sure way to demonstrate cause is an experiment. You have to manipulate the situation. (p. 290)