Doesn’t this suffer from a similar problem as group selection?
Imagine that the first mutant gets lucky and has 20 children; 10 of them inherited the “help your siblings” genes, and 10 of them did not. Does this give an advantage to the nice children over the non-nice ones? Well, only in the next generation… but then again, some children in the next generation will have the gene and some will not… and this feels like there is always an immediate disadvantage that is supposed to get balanced by an advantage in the next generation, except that the next generation also has an immediate disadvantage...
Uhm, let’s reverse it. Imagine that everyone has the “help your siblings” gene, in the most simple version that makes them take a given fraction of their resources and distribute it indiscriminately among all siblings. Now we get one mutant that does not have this gene. Then, this mutant has an advantage over their siblings; the siblings give resources to mutant, not receiving anything in return. Yeah, the mutant is causing some damage to the siblings, reducing the success of their genes. But we don’t care about genes in general here, only about the one specific “don’t help your siblings” allele; and this allele clearly benefits from being a free-rider. And then it reproduces with some else, who is still an altruist, and again 50% of the mutant’s children inherit the gene and get an advantage over their siblings.
So we get the group-selectionist situations where families of nice individuals prosper better than mixed families, but within each mixed family the non-nice individuals prosper better. This would need a mathematical model, but I suspect that unless the families are small, geographically isolated, and therefore heavily interbreeding, the nice genes would lose to the non-nice genes.
Your siblings is not a reproductively isolated population (hopefully=)). The relevant question is if the helpers are more or less fit relative to the population as a whole. So in your example, where the helpers give up something and get back less, the gene goes extinct.
But start instead of just zero-sum redistribution with something like that trust game where you send money through a slot and whatever amount you send the other guy gets triple. But it’s multiplayer and simultaneous. So the helpers give up some amount, let’s say x each and every family member gets three times what the average participant gave up. If half of the family members are helpers then everyone gets 3x/2. Which is more than x, so now the gene gives a fitness advantage.
Let’s ignore the details of genetic reproduction, and simply assume that if both parents have a trait, all children have it; if no parent has a trait, no children have it; and if one parent has it, exactly 50% of children have it. Let’s assume all families have the same size. (These are quite unrealistic assumptions to make calculation simple.)
Let’s suppose that being nice to all your siblings has a cost c (for example, if without reciprocation it would reduce your survival rate by 5%, then c = 0.05), and that being supported by all your siblings provides a benefit b (for example, if without helping any your siblings but being helped by all of them would increase your survival rate by 10%, then b = 0.10). We can assume 0 < c < b.
So, the current generation contains a fraction p of adult individuals who have the sibling-helping trait. Let’s assume they form pairs randomly (because the trait is so new they haven’t developed its detectors yet). On average, there will be p^2 “helper-helper” families, 2×p×(1-p) “helper-nonhelper” families, and (1-p)^2 “nonhelper-nonhelper” families.
In “nonhelper-nonhelper” families, children’s survival rate will be 1 (the default survival rate before the helper mutation appeared). In “helper-helper” families, children’s survival rate will be 1+b-c. In “helper-nonhelper” families, the 1⁄2 of helper children will have survival rate 1+b/2-c (they only get half the help, but pay the full cost), and the 1⁄2 of nonhelper children will have survival rate 1+b/2 (they get galf the help at no cost). Now all these values together have to be normalized to 1, to get the proportions in the next generation.
next generation helpers ratio = (p + pb/2 - pc/2 + ppb/2) / (p + pb/2 - pc/2 + ppb/2 + 1 - p + pb/2 - ppb/2) = (p + pb/2 - pc/2 + ppb/2) / (1 + pb—pc/2) … which for obscure mathematical reasons is always greater than p
Well, assuming that I made no mistake during the calculation, and that my simplified assumptions about heritability of traits didn’t diverge from reality too much (two reasons why I hesitated to do the calculations myself)… I am more or less convinced this could work.
More broadly: consider genetic drift and the probability of reaching fixation. For neutral mutations, their probability of fixation is the rate at which they are introduced, and they will reach fixation at 4*population-size generations. For primate species, the population size is always pretty small, low hundreds of thousands or millions; generation turnover tends to be something like 10 years, and early primates can date back as much as 60 million years, so it can encompass a lot of drift. If we imagine that kin altruism is neutral until you have at least a few relatives and the relevant mutation keeps happening once in every few hundred thousand individuals, it’s not at all unlikely that it will appear repeatedly and then drift up to the threshold where fitness gains start appearing, and then of course, now that it’s no longer neutral, it’ll be quickly selected for at the rate of its gain.
Doesn’t this suffer from a similar problem as group selection?
Imagine that the first mutant gets lucky and has 20 children; 10 of them inherited the “help your siblings” genes, and 10 of them did not. Does this give an advantage to the nice children over the non-nice ones? Well, only in the next generation… but then again, some children in the next generation will have the gene and some will not… and this feels like there is always an immediate disadvantage that is supposed to get balanced by an advantage in the next generation, except that the next generation also has an immediate disadvantage...
Uhm, let’s reverse it. Imagine that everyone has the “help your siblings” gene, in the most simple version that makes them take a given fraction of their resources and distribute it indiscriminately among all siblings. Now we get one mutant that does not have this gene. Then, this mutant has an advantage over their siblings; the siblings give resources to mutant, not receiving anything in return. Yeah, the mutant is causing some damage to the siblings, reducing the success of their genes. But we don’t care about genes in general here, only about the one specific “don’t help your siblings” allele; and this allele clearly benefits from being a free-rider. And then it reproduces with some else, who is still an altruist, and again 50% of the mutant’s children inherit the gene and get an advantage over their siblings.
So we get the group-selectionist situations where families of nice individuals prosper better than mixed families, but within each mixed family the non-nice individuals prosper better. This would need a mathematical model, but I suspect that unless the families are small, geographically isolated, and therefore heavily interbreeding, the nice genes would lose to the non-nice genes.
Your siblings is not a reproductively isolated population (hopefully=)). The relevant question is if the helpers are more or less fit relative to the population as a whole. So in your example, where the helpers give up something and get back less, the gene goes extinct.
But start instead of just zero-sum redistribution with something like that trust game where you send money through a slot and whatever amount you send the other guy gets triple. But it’s multiplayer and simultaneous. So the helpers give up some amount, let’s say x each and every family member gets three times what the average participant gave up. If half of the family members are helpers then everyone gets 3x/2. Which is more than x, so now the gene gives a fitness advantage.
Here is a toy model:
Let’s ignore the details of genetic reproduction, and simply assume that if both parents have a trait, all children have it; if no parent has a trait, no children have it; and if one parent has it, exactly 50% of children have it. Let’s assume all families have the same size. (These are quite unrealistic assumptions to make calculation simple.)
Let’s suppose that being nice to all your siblings has a cost c (for example, if without reciprocation it would reduce your survival rate by 5%, then c = 0.05), and that being supported by all your siblings provides a benefit b (for example, if without helping any your siblings but being helped by all of them would increase your survival rate by 10%, then b = 0.10). We can assume 0 < c < b.
So, the current generation contains a fraction p of adult individuals who have the sibling-helping trait. Let’s assume they form pairs randomly (because the trait is so new they haven’t developed its detectors yet). On average, there will be p^2 “helper-helper” families, 2×p×(1-p) “helper-nonhelper” families, and (1-p)^2 “nonhelper-nonhelper” families.
In “nonhelper-nonhelper” families, children’s survival rate will be 1 (the default survival rate before the helper mutation appeared). In “helper-helper” families, children’s survival rate will be 1+b-c. In “helper-nonhelper” families, the 1⁄2 of helper children will have survival rate 1+b/2-c (they only get half the help, but pay the full cost), and the 1⁄2 of nonhelper children will have survival rate 1+b/2 (they get galf the help at no cost). Now all these values together have to be normalized to 1, to get the proportions in the next generation.
Ugh, math...
non-normalized next generation helpers = p^2 × (1+b-c) + 1⁄2 × 2×p×(1-p) × (1+b/2-c) = p + pb/2 - pc/2 + ppb/2
non-normalized next generation non-helpers = (1-p)^2 × 1 + 1⁄2 × 2×p×(1-p) × (1 + b/2) = 1 - p + pb/2 - ppb/2
next generation helpers ratio = (p + pb/2 - pc/2 + ppb/2) / (p + pb/2 - pc/2 + ppb/2 + 1 - p + pb/2 - ppb/2) = (p + pb/2 - pc/2 + ppb/2) / (1 + pb—pc/2) … which for obscure mathematical reasons is always greater than p
Well, assuming that I made no mistake during the calculation, and that my simplified assumptions about heritability of traits didn’t diverge from reality too much (two reasons why I hesitated to do the calculations myself)… I am more or less convinced this could work.
More broadly: consider genetic drift and the probability of reaching fixation. For neutral mutations, their probability of fixation is the rate at which they are introduced, and they will reach fixation at 4*population-size generations. For primate species, the population size is always pretty small, low hundreds of thousands or millions; generation turnover tends to be something like 10 years, and early primates can date back as much as 60 million years, so it can encompass a lot of drift. If we imagine that kin altruism is neutral until you have at least a few relatives and the relevant mutation keeps happening once in every few hundred thousand individuals, it’s not at all unlikely that it will appear repeatedly and then drift up to the threshold where fitness gains start appearing, and then of course, now that it’s no longer neutral, it’ll be quickly selected for at the rate of its gain.