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Selfish Gene

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Darwin One of the aspects of David Dobbs’ Aeon Magazine piece has been a significant backlash, mostly playing out on Twitter. A biologist who did quite like Dobbs’ article was P. Z. Myers. Ultimately I’m not quite sure that Myers disagrees as much with the people I follow on Twitter as you might think from the cautious and qualified tone of the endorsement, but it is clearly an endorsement (“must read today”). He observes that developmental biologists in particular might welcome Dobbs’ exposition of deviations from standard Mendelism (though this is clearly not the case for Armand Leroi, an evolutionary developmental biologist). Dobbs has clarified the thrust of his article, but the general takeaway by many was that the science has passed Richard Dawkins by, and he’s something of an old-fashioned dinosaur. That might not have been the intent, but that’s basically going to be the implication seen by a lot of non-scientists, and people outside of evolutionary biology. I know this because my whole life I’ve run into people who know the “real deal” about evolutionary biology, and aren’t shy about telling me. When I was 13 years old I remember my science teacher explaining that he didn’t buy into Darwinism. Why? Because he accepted Stephen Jay Gould’s punctuated equilibrium, which was definitely the wave of the future. Twenty years later I don’t think much has changed. Standard evolutionary biology is being modified on the margins and edges, extended and expanded, but in a gradual and incremental fashion. Gould and his acolytes are always a decade away from overturning the established order.

And speaking of Gould, here’s Paul Krugman in 1996:

I am not sure how well this is known. I have tried, in preparation for this talk, to read some evolutionary economics, and was particularly curious about what biologists people reference. What I encountered were quite a few references to Stephen Jay Gould, hardly any to other evolutionary theorists. Now it is not very hard to find out, if you spend a little while reading in evolution, that Gould is the John Kenneth Galbraith of his subject. That is, he is a wonderful writer who is bevolved by literary intellectuals and lionized by the media because he does not use algebra or difficult jargon. Unfortunately, it appears that he avoids these sins not because he has transcended his colleagues but because he does does not seem to understand what they have to say; and his own descriptions of what the field is about – not just the answers, but even the questions – are consistently misleading. His impressive literary and historical erudition makes his work seem profound to most readers, but informed readers eventually conclude that there’s no there there….

This may be harsh, but it gets to the heart of the fact that non-specialists esteem Gould far more than most working within his own purported field (I say purported, because from what I can tell Gould was a fine paleontologist. But he left much to be desired as an evolutionary theorist). An analogy with physics might be the fact that Stephen Hawking has been acclaimed as the “most brilliant mind since Einstein,” mostly due to his elegant and popular series of books for the general public. Hawking is brilliant, but he stands head and shoulders above other prominent physicists (e.g., Ed Witten) in the public mind mostly because of his popular contributions, not his scientific work. This is not necessarily a problem, except when people confuse cultural popularity with intellectual eminence.

Every decade there’s always a new trend which is gaining traction and pushing the edge in terms of what we know about evolutionary biology. In the 1970s there was molecular neutralism, which superseded tired arguments between Fisherian selectionists and Wrightian balancing serlectionists. In the 2000s you had evo-devo. Today it is epigenetics, and what that means for the “Central Dogma.” These are not crankish fads, but, the media often exaggerate the impact they’re having on a given field because that’s news. And at that point the general public gets confused as to the nature of the consensus within a field, because their perception is often filtered through the media (when it comes to cosmology, I’m the general public, so I know whereof I speak). This explains why I regularly get irritated emails and Facebook messages to the effect that my focus on population genetics is totally doing a disservice to my readership, which won’t understand that developmental biology and/or epigenetics has totally changed the game and our understanding of evolutionary genetic process.

Finally, PZ Myers seems to have sarcastically tweeted at me that we should vote on David’s piece after I wondered if any others have supported its thesis (David tells me some others have privately, and also in places like Jerry Coyne’s comment board), alluding to the reality that science isn’t a democracy, but proceeds via a method. Well, that’s the ideal. But as it is practiced science is basically the consensus of specialists. When someone plays up the existence of religious scientists PZ has no problem looking at large samples of data which suggest that conventionally orthodox religious scientists are in a small minority. Similarly, when people skeptical of anthropogenic climate change make their case, others are not shy about noting the consensus among climatologists on that question.

I can accede to the fact that within evolutionary biology the tradition which goes back to the grand triumvirate of theoretical population genetics, R. A. Fisher, Sewall Wright, and J. B. S. Haldane, is not universally accepted as having much important to say. Lynn Margulis comes to mind as someone who was skeptical of this in a vocal manner, and even the ‘orthodox,’ such as Ernst Mayr, have had their qualms with excessively formal model building. But, despite the arguments and attempts of of Margulis, Gould, and yes, Mary Jane West-Eberhard, to sideline this old orthodoxy, I believe it still remains the mainstream view of most practicing evolutionary biologists. That doesn’t mean that the classical Neo-Darwinian tradition is right, but it does mean that the scientific community probably leans toward that position more than any other alternative. And that is an important fact, because many people are confused about this, and unfortunately I’m pretty sure that David’s piece will just magnify that trend.

Addendum: Alwyn Scally suggests much of this is simply dislike of Richard Dawkins’ affect and non-scientific views on the part of those sympathetic to Dobbs’ take (though I don’t think this is David’s motivation, it may be for some of those who are taking succor from its takeaway). That is true. And how unfortunate that we sacrifice science on the alter of personal dislike.

• Category: Science • Tags: Richard Dawkins, Selfish Gene 
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Credit: Shane Pope

The always fascinating Aeon Magazine has a very interesting piece up by my friend David Dobbs, Die, selfish gene, die. As you can tell it is something of a broadside against Richard Dawkins’ ideas promoted in The Selfish Gene. The subheading is straightforward: “The selfish gene is one of the most successful science metaphors ever invented. Unfortunately, it’s wrong.” As I stated on Twitter the writing here was splendid, but at the end of the day I must disagree with the conclusions on the balance. It is true that the selfish gene is wrong as a metaphor and model, but all representations of this sort have an element of stylized artifice which does not stand up to scrutiny. John Dalton’s atomic theory is also wrong, but still highly useful in imparting conceptual truth. But across 5,000 words David surveys the landscape and seems to come away with the lesson that evolutionary biology took a wrong turn at some point, and that the calcified old order is now facing a revolt from below. To me this does not seem like an accurate representation of what I know, though to be fair sometimes it was difficult for me to gauge whether David himself is always of one mind on the issue. The piece is wide ranging and expansive, and has so much detail that it is difficult to start at one particular place.

Naturally I have some technical and scientific gripes which may be irrelevant to most readers. As I observed on Twitter the mention of microarrays as a means to understand gene expression makes me wonder if this piece was written in 2005, as the field has moved to RNA-Seq. David admitted that this article was years in the making, so this peculiarity is easily explained then. But there is another section where he characterizes William D. Hamilton as a statistician. I think this misleads somewhat as to the primary thrust of his career. To my knowledge Hamilton’s forte was not detailed analysis of reams of data, extracting patterns from the noise. Rather, he engaged in modeling, extrapolating from the core truths of Mendelian genetics.

But the above are minor gripes, and more matters of style than substance. There are some issues where I think the piece may be substantively incorrect. Going in order of my concern, first David seems to imply that the genius of genetics in relation to Charles Darwin’s theory of natural selection is that it presented a straightforward mechanism by which one could introduce variation through mutation. This seems wrong to me. Rather, the power of genetics in a Mendelian framework is that it is a discrete manner of transmission where variation does not decay every generation. Natural selection needs variation to operate, and previous “blending models” of inheritance were subject to the problem that variation decays very rapidly in this framework. Second, the piece contends that the modern evolutionary synthesis was “all about the maths.” A formal mathematical framework was probably a necessary condition for the synthesis as we understand it, but it seems too much to say that this was overwhelmingly dominant. Two of the major figures in the synthesis, Theodosius Dobzhansky and Ernst Mayr, were definitely not mathematical. Dobzhansky saw particular empirical results, and leaned upon Sewall Wright’s formal models to support them, but he admitted that the mathematics escaped him. Mayr famously inveighed against mathematical genetics in his later years.

Then there’s the description of the origin and development of the theory of inclusive fitness. This just seems totally wrong to me (unless it is simply not clear). Though both R. A. Fisher and J. B. S. Haldane alluded to the broader logic of inclusive fitness at various points, the mathematical framework was developed by William D. Hamilton, John Maynard Smith, and George Price in the 1960s. More precisely, two papers in 1964 by Hamilton titled The Genetical Evolution of Social Behaviour laid the groundwork for the formal exploration of the problem of altruism. If I had read the piece without that knowledge I’d have thought it had been developed decades earlier.

Reading through the article I could almost see areas that I felt had to be edited out, or rewritten to be comprehensible to the broader public. I admire David’s effort and doubt I could have pulled something similar off. This is not an easy topic to tackle, the conceptual and empirical landscape is a minefield for someone to explore. Too many of the scientific assertions in the detail I’m not sure I can respond to, because I’m not totally clear on what’s being said, or implied. Many complex ideas and positions are condensed down to a sentence or two, to the point where they become obscure to me.

But there are a few points I’d like to enter into the record. First, as noted by many ideas like genetic assimilation have been around for a long time. C. H. Waddington is not an obscure figure. Evolutionary genetics has not been in stasis since the modern synthesis, or even the 1970s. Genomics means that there is a surfeit of data, and different theories are going to be useful in explaining particular aspects of the shape of biological variation. The emergence of evo-devo in the 2000s was certainly interesting, though I don’t think it “changed everything,” as some are fond of declaring. The narrative that the modern synthesis is being “overthrown” seems to be a persistent one, and always seems t finds support from the latest hot area of study. In the 1970s it was the molecular theory of neutral evolution, which rebutted excessive adaptationism, in the 2000s it was evo-devo, and now it is epigenetics. Science is not like religion, and heretical sects do not just explode and extinguish. New methods and areas of study add and modify the consensus, but only in rare cases do they “overthrow” a paradigm. The current interest in epigenetic inheritance has spawned forth a craze in neo-Lamarckian headlines. This too shall pass.

Finally, there’s the namecheck of several biologists who are presenting an alternative to ‘selfish gene’ model, Massimo Pigliucci, Eva Jablonka, Stuart Kauffman, Stuart A Newman, Stephen Jay Gould, Gregory Wray and Mary Jane West-Eberhard. To the lay reader some of these are familiar names already. It seems that citing them is a way to bolster the case that it isn’t crazy to think that Richard Dawkins’ ideas may not be right. These aren’t all cranks. But, some of them are notable for being heterodox in their thinking. Which leads me to assert that Richard Dawkins’ views are still closer to the center of opinions among evolutionary biologists than Mary Jane West-Eberhard. That doesn’t mean that Dawkins is right and West-Eberhard is wrong, it just means implicitly ‘gene-centric’ models are still popular. There’s a reason it’s calle ‘genetics,’ and not ‘expressionetics.’

I could say much more, but I won’t. After thrashing David a fair amount I have to admit it was a pleasure to read a popular piece which cited the achievements of greats like Fisher, Haldane, Wright, and Hamilton. Though I’d warn you from taking the assertions as gospel, the article is still worth reading for its detail as a starting point for further exploration.

• Category: Science • Tags: Evolution, Genetics, Selfish Gene 
🔊 Listen RSS With the recent huge furor over the utility of kin selection I’ve been keeping a closer eye on the literature on inclusive fitness. The reason W. D. Hamilton’s original papers in The Journal of Theoretical Biology are highly cited is not some conspiracy, rather, they’re a powerful framework in which one can understand the evolution of social behavior. They are a logic whose basis is firmly rooted in the world of how inheritance and behavior play out concretely. But because of their formality and spareness inclusiveness fitness has also given rise to a large literature derived from simulations “in silico,” that is, evolutionary experiments in the digital domain.

375px-Green_Beard_GeneOne can elucidate inclusive fitness through Hamilton’s Rule, but it is also rather easy to exposit verbally via a “gene’s eye view.” Imagine for example a dominant mutation in a diploid organism which produces the behavior of altruism toward near kin. Initially the altruist will have offspring whose probability of carrying the dominant mutation is 50%, because there is also the probability that they will carry the ancestral non-altruistic variant. Imagine an altruistic behavior which incurs a small, but not trivial, cost to the individual performing the behavior, and a large gain to the individual who is on the receiving end of the altruism. The logic of favoring near kin is such that in the initial generation the parent which behaves altruistically toward near kin is increasing their own “inclusive fitness” because their offspring share 50% of their genes identical-by-descent (in the case of a diploid sexually reproducing organism). But from a gene’s eye perspective what is really occurring is that there is a 50% chance that the gene which fosters altruism is promoting the fitness of a copy of itself. So inclusive fitness operates by modulating the parameters of costs and gains to focal individuals as a function of their relatedness, but it is the genes, the “replicators,” which persist immortally across the generations. We “vehicles” are just the ocean through which genes sail.

But like Darwin’s theory of evolution through natural selection the fruit of these logics are in the details. A new paper in The Proceedings of the Royal Society puts the focus on different means by which inclusive fitness may be maximized. In particular, the paper offers up a reason for why what Richard Dawkins termed the “green-beard effect” is not more common. Selective pressures for accurate altruism targeting: evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory:

Inclusive fitness theory predicts that natural selection will favour altruist genes that are more accurate in targeting altruism only to copies of themselves. In this paper, we provide evidence from digital evolution in support of this prediction by competing multiple altruist-targeting mechanisms that vary in their accuracy in determining whether a potential target for altruism carries a copy of the altruist gene. We compete altruism-targeting mechanisms based on (i) kinship (kin targeting), (ii) genetic similarity at a level greater than that expected of kin (similarity targeting), and (iii) perfect knowledge of the presence of an altruist gene (green beard targeting). Natural selection always favoured the most accurate targeting mechanism available. Our investigations also revealed that evolution did not increase the altruism level when all green beard altruists used the same phenotypic marker. The green beard altruism levels stably increased only when mutations that changed the altruism level also changed the marker (e.g. beard colour), such that beard colour reliably indicated the altruism level. For kin- and similarity-targeting mechanisms, we found that evolution was able to stably adjust altruism levels. Our results confirm that natural selection favours altruist genes that are increasingly accurate in targeting altruism to only their copies. Our work also emphasizes that the concept of targeting accuracy must include both the presence of an altruist gene and the level of altruism it produces.

Using the Avida software platform the researchers ran trials of the evolution of populations of artificial life which varied in fitness, coefficient of relatedness, as well as their phenotypes. In one set of trials the organisms operated through conventional means of kin selection, whereby the heuristic was to favor those to whom an individual was closely related. This will result in a fair amount of “false positives,” as everyone knows that near kin can be selfish and “cheat.” Remember that in the toy example above 50% of the offspring who will gain from altruism will themselves lack the altruism gene. A second set of organisms look to total genetic similarity. This is the sort of thing which humans could engage in if they had immediate knowledge of the genomic sequences of those around them. Even among near relatives genetic similarity is only correlated with, not perfectly correspondent with, coefficients of relatedness. Some full siblings may share more identity-by-descent than others. This is trivially obvious in the initial illustration, as there will be a great deal of intra-familial variance on the gene which produces altruism. To focus on the dynamics of the specific gene, the authors also looked at a green-beard effect, whereby a there is a correlation between altruism, a gene, and a visible phenotype. In other words, you know altruists by a correlated physical trait. If the correlation between a phenotype and a genotype is close enough you don’t need do a typing of their genome because you know the state of their genotype, and so have expectations as to whether they’re truly altruists or not. Presumably using the green-beard effect one could side-step the usage of kinship or relatedness as a proxy. In many cases those more distantly related could be more phenotypically similar on the traits of interest than those who are genetically closer.

What did they find? Figure 1 shows the outcomes of various sets of trials:


Their expectations were that in regards to the evolution of altruism kin selection should be inferior to genetic similarity which should be inferior to the green-beard effect. The reasoning is straightforward, as you progress across these sequence of dynamics the false positive rate of aiding those without the altruism conferring gene should decrease. That is not what they found, at least not initially.

What was happening is that they were focusing on the wrong parameters in framing their expectations. That’s why you run the model: human intuition often fails. Green-bearding is very precise as a dichotomous indicator of whether an individual carries a particular gene identical-by-descent, but mutation could produce variation in levels of altruism. What they found was that when green-bearding was dichotomous the levels of altruism tended to converge upon a lower equilibrium as individuals were focused on being just altruistic enough to count as real altruists and so gain advantages from those who were more generous. A concrete example of this would be an “affinity con”. An individual is a member of a group, and they leverage the trust which comes from being a member of the group to exploit the group. Baked into the cake of the original model is that altruists who also had a green-beard had to have donated at least once, and that is the target which green-beards converged upon. In contrast the strategy of genetic similarity resulted in greater donations, and because the model had non-zero sum dynamics (altruism increased everyone’s fitness greatly, though cheaters could exploit this to “free-ride”) the strategy which maximized donations was more successful. The researchers made green-bearding more competitive by simply increasing the donation threshold to match the equilibrium which emerged with the other strategies. So making all things equal the intuition about green-bearding was then vindicated.

Instead of setting a specific threshold there was another way that green-bearding could beat the other strategies to maximize inclusive fitness: vary the green-bearding trait and altruism continuously in a correlated fashion. In other words, the greener the beard, the more altruistic. This is a classic way that one could beat the cheaters: develop detection and discernment mechanisms. Why doesn’t this matter for the two other more “primitive” techniques? Kin selection and genetic similarity are more robust because they’re not fine-tuned, organisms with similar genome content are likely to have similar altruism levels. The genetic relatedness of altruists in green-bearding populations is going to be lower because they’re looking for a very specific genotype and its correlation with a phenotype. Green-bearding is more precise, but it’s also somewhat more complicated, and as a more precisely engineered solution it may not always be as robust.

And that necessity of fine-tuned intelligence in design may be why green-bearding is not more common. The authors note that in theory one could imagine mutations leading to concomitant variations of the magnitude of green-bearding and altruism in the same direction, but in a real evolutionary genetic context with normal parameters of mutation and effective population sizes this may not be plausible. Many people would argue that evolution is littered with kludges because natural selection makes recourse to “quick and dirty” solutions which are simple but effective, and kin selection and genetic similarity are closer to that than green-bearding. In theory selection may lead to a world of green-beards with infinite population sizes and generations, and persistent and consistent selection, but the world may be too protean for this optimal equilibrium to ever arise. So until then, we’ll make do with social evolution’s duct-tape: “I against my brother; my brother and I against my cousin; I, my brother, and my cousin against the stranger.”

Citation: Clune J, Goldsby HJ, Ofria C, & Pennock RT (2010). Selective pressures for accurate altruism targeting: evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory. Proceedings. Biological sciences / The Royal Society PMID: 20843843

Image Credit: Burningrey

Razib Khan
About Razib Khan

"I have degrees in biology and biochemistry, a passion for genetics, history, and philosophy, and shrimp is my favorite food. If you want to know more, see the links at"