The gene machine
Survival machines began as passive receptacles for the genes, providing them with little more than protection. They "fed" on the organic molecules in the primeval soup but eventually, this source of "nourishment" ran dry. As they became more elaborate and developed due to the environmental pressures, they evolved into many-celled bodies, each cell containing a complete copy of the genes. Dawkins likes to refer to this state of the vehicle as "a colony of genes, and of the cell as a convenient working unit for the chemical industries of the genes". However, he is careful to point out that bodies have undeniably acquired an individuality of their own; animals move as a co-ordinated whole, as a unit and not as a colony. Dawkins argues that this is to be expected as selection favours genes that co-operate with others so that the communal body is as successful as possible. Indeed, nowadays the mutual co-evolution of genes has proceeded to such an extent that the colonial nature of an individual is unrecognisable. Therefore, in this chapter he starts to use "the language of convenience" and refers to the individual body as an agent "trying" to increase the number of all its genes for future generations.
He describes how the survival machines have evolved nerve cells (or neurones) and brains to control and co-ordinate the contractions of muscles. They have also become equipped with sense organs, to translate patterns of physical events in the outside world into the pulse codes of the neurones, and memory, so that this timing of muscle contractions can be influenced by events both in the immediate past and distant past. All of these functions are ultimately under the control of the brain.
The relevance of all this for the topic of gene selfishness is to explain how animal behaviour, both altruistic and selfish, is ultimately under the control of the genes, albeit indirectly. By dictating the way survival machines and their nervous systems are built, genes exert ultimate power over behaviour, but the moment- to-moment decisions about what to do next are taken by the nervous system. "Genes are the primary policy-makers; brains are the executives". The genes do not control their survival machines directly in the way that fingers control puppet strings because of time-lags. As Dawkins puts it:
"Genes work by controlling protein synthesis. This is a powerful way of manipulating the world, but it is slow. It takes months of patiently pulling protein strings to build an embryo. The whole point about behaviour, on the other hand, is that it is fast. It works on a time-scale not of months but of seconds and fractions of seconds…Genes don't have reaction-times like that."
Therefore, the best thing that genes can do is to build a fast executive computer, in advance, and program it with rules and "advice" that will help it cope with as many eventualities as they can "anticipate". One way genes have dealt with this problem of having to anticipate the unpredictable environments they are placed in, is to build into their survival machines a capacity for learning. In this way, genes have programmed their survival machines to ensure that they stand the best possible chance of surviving and reproducing. As Dawkins sums it up (p62):
"The genes are the master programmers, and they are programming for their lives. They are judged according to the success of their programs in coping with the hazards that life throws at their survival machines, and the judge is the ruthless judge of the court of survival."
Aggression: stability and the selfish machine
When considering aggression in animals, it is important to remember that the individual is a selfish machine programmed to do whatever is best for its genes as a whole. Every individual is trying to survive as best it can, which leads to competition between individuals for the resources necessary for a successful life. This may lead you to believe that murdering your competitors would be favoured by natural selection because, by eliminating someone who may use up resources that would have been beneficial to you, you are ensuring that there are more available to you and therefore your chances of surviving are improved. However, this is not necessarily the case as there are always costs for such behaviour as well as the benefits. These may include things as simple as time and energy spent murdering your competitors, or it may include more serious costs such as risk of death in the fight.
Therefore, animals engage in a serious fight if the benefit of doing so outweighs the costs of potential injuries. Otherwise, if the resource is not considered so important to them, or the cost to them is too high, they may adopt behaviour such as threatening and bluffing or not initiating a fight at all.
Maynard Smith used Game Theory to explain why animals "choose" the fighting strategy that they do. He defines a "strategy" as a pre-programmed behavioural policy, for example: "Attack opponent; if he flees pursue him, if he retaliates run away" and he introduces the concept of the evolutionary stable strategy (ESS). This is defined as a strategy which, if most members of a population adopt it, cannot be bettered by an alternative strategy. Therefore, this is the strategy that will persist in a population because every individual is trying to maximise his own success and the ESS will be the strategy that will do this for all individuals as it cannot be bettered by any deviant individual.
When applying this theory to aggression, Maynard Smith uses a simple hypothetical case where there are two sorts of fighting strategies in a population, named "Hawk" and "Dove". Hawks always fight to injure and kill their opponent, continuing until the opponent retreats or one of the combatants is seriously injured, though in the process they will risk injury themselves. Doves simply threaten and never engage in serious fights and will retreat when attacked. In this evolutionary game, let the winner of a contest score +50 and the loser 0. The cost of a serious injury is -100 and the cost of wasting time in threatening is -10. Now consider the possible types of encounters in this game. Hawks always beat Doves (because the dove runs away) but if they fight another hawk, they stand equal chance of winning or being injured. If two Doves fight, each has an equal probability of winning; neither is injured but they both spend a lot of time threatening.
For the purpose of this book, we are not interested in whether hawks will tend to beat doves when they fight - we already know that hawks will always win. What is important is whether either of these strategies are evolutionary stable strategies. As we can see, neither of these two strategies would be an ESS. If all individuals in a population are doves, every contest is between a dove and another dove and the payoff is on average +15 (winner = +40, loser = -10. Probability of winning = ½. Average payoff = ½(40-10) = +15). In this population, any mutant hawk would do very well (+50) and the Hawk strategy would soon spread. Likewise, in a population of all hawks, the average payoff is -25 (½(50-100) = -25) so any mutant dove would do better because when a Dove meets a hawk, it gets 0. Therefore, the Dove strategy would spread if the population consisted mainly of hawks.
Thus, it can be seen that both strategies, if present in their pure form, are vulnerable to invasion by the other strategy. The stable equilibrium occurs when the average pay-offs for a hawk are equal to the average pay-offs for a dove. For the particular arbitrary points system that we are using in this example, the stable ratio in a population turns out to be 5/12 doves and 7/12 hawks. However, in practice, individuals do not play either one strategy or the other. Instead every individual is capable of behaving either like a hawk or like a dove in each particular contest and the ESS is achieved when the probability of an individual behaving like a hawk is 7/12 and the probability of them behaving like a dove is 5/12.
However, hawk and doves are not the only possible strategies to adopt, there are alternative strategies called Retaliator, Bully, and Prober-retaliator. Likewise, the Hawk-Dove scenario described above is an example of a symmetric contest, where we have assumed that the contestants are identical in all respects except their fighting strategy. There are in fact several asymmetries, such as individuals differing in size and fighting ability, which will all "be taken into account" when an individual "chooses" what strategy to adopt.
Therefore, depending on the relative costs and benefits of aggressive behaviour, an individual will choose from the broad range of different fighting strategies, or indeed play a complex mixture of several, that will maximise his net benefit for his genes.
Genemanship
A selfish gene is not just one single physical bit of DNA. It is all replicas of a particular bit of DNA whose aim is to get more numerous in the gene pool. Not only could it do this by reproducing itself so that it's offspring carried it's genes into the next generation, it could also do this by assisting replicas that are sitting in other bodies. Therefore, Dawkins suggests that individual altruism is in fact brought about by gene selfishness.
However, to be able to do this, the gene must be able to "recognise" their copies in other individuals. Theoretically, it is possible that a gene could arise which produced an external, visible label and, at the same time, a tendency to be nice to other bearers of that label. Dawkins called this idea the Green Beard Altruism effect which, although unlikely, proposes that one and the same gene produces a green beard and a fondness for other green beards.
Another, more plausible, way of recognising their copies is in close relatives, who have a greater than average chance of sharing the same genes. It is widely accepted that this must be why altruism by parents towards their young is so common, but the same applies for other close relatives such as brothers, sisters, nephews, nieces, close cousins etc. What is important in this theory is that even a gene that is rare in the population as a whole is common in a family. Therefore, the chances are good that sisters contain the same particular rare gene.
For this, an index of relatedness can be created that expresses the chance of a gene being shared between two relatives. For example the relatedness between two brothers is ½ since half the genes possessed by one brother will be found in the other. This can then be used to explain apparently altruistic behaviour towards kin. For altruistic behaviour to evolve, the net risk to altruist must be less than net benefit to recipient multiplied by the relatedness - (benefit / cost) x relatedness > 1. For example, a gene for suicidally saving 5 cousins would not become more numerous in the population (because 0.125(5/1) = 0.625) but a gene for saving 5 brothers (0.5*5 = 2.5) or 10 cousins (0.125*10 = 1.25) would. This is because chances of the suicidal altruistic gene in these cases living on in the bodies of the individuals saved are great enough to compensate for the death of the altruist itself. However, having said this, the individual does not actually do the sums before carrying out a particular altruistic act. The individual is just pre- programmed to behave as if it has.
But there is still the problem of how animals know who their kin are? Humans know who their relations are because we are told and because we give them names. Animals, on the other hand, have to be given by their genes a simple rule for action, a rule that does not involve all-wise cognition but that works nevertheless at least in average conditions. One way that has been suggested is to behave altruistically towards individuals who physically resembled you. Another is to behave altruistically to the members who live in the same group as you. This latter method works for species whose members do not move around much, or whose members move in small groups because the chances are good that any random individual you came across is fairly close kin to you.
However, this uncertainty is vulnerable to mistakes and exploitation. Adoption is considered to be misfiring of the built-in rule because the generous female is doing her own genes no good by caring for the orphan. There are also examples of mothers deliberately deceiving naïve young females into adopting their children so that they may get on to reproduce again. One species that uses this deliberate deception is the cuckoos.
Therefore, Dawkins argues that in addition to the index of relatedness, we should also consider something like an index of "certainty" as it seems that it is the best estimate of relatedness that animals use when behaving altruistically. This is probably why parental care is so much more common and more devoted than brother/sister altruism, and also why animals may value themselves more highly even than several brothers. Although the parent/child relationship is no closer genetically than the brother/sister relationship, its certainty is greater.
Family planning
Dawkins believes that animals regulate their birth rates so that any given species tends to have a rather fixed clutch size or litter size: no animal has an infinite number of children. What determines this optimal clutch size is the environmental situation that the mother is in. The selfish individual will choose the clutch size that maximises the number of children she rears. The obvious reason for this is that if she has too many children, the limited resources, such as food, will be stretch so thin that not all of her offspring will survive and she will lose precious genes. Equally, if she devotes all her investment to too few children, her rivals who invested in the optimum number of children will end up with more grandchildren and so again, she will lose precious genes.
Individuals who have too many children are penalised because less of their children survive, and so genes for having too many children are not passed on to the next generation in large numbers. Therefore, evolution will favour those who regulate their clutch size to the optimum. This optimum may depend on factors such as overcrowding, and therefore greatly limited food resources, and the prediction in winter whether the following spring will yield a good crop of the food resources. It is possible that there are some clues that a good prophet could use to adjust her clutch size for year to year to her advantage
Dawkins uses this clutch size argument (originally proposed by David Lack) to explain territorial behaviour and dominance hierarchies. Using red grouse as an example, he describes how they fight over territories early in the season but how, after a while, the losers seem to accept that they have failed and do not fight anymore. Only territory owners breed, despite the fact that non-territory owners are capable of breeding themselves. Why then do the non-territory owners not continually try to expel a territory owner in a desperate attempt to reproduce? The selfish gene theory explains this behaviour by saying that if the odds of an outcast's succeeding to a territory are greater by waiting than the odds of his gaining one by fighting, then it would pay him as a selfish individual to wait in the hope that somebody will die, rather than waste what little energy he has in futile fighting. Similarly, a seal that leaves the harem-holders unmolested to mate is not doing it for the good of the group; he is biding his time, waiting for a more opportune moment.
Battle of the generations
Parental investment is defined as "any investment by the parent in an individual offspring that increases the offspring's chance of surviving (and hence reproducing) at the cost of the parent's ability to invest in other offspring." Other things being equal, a mother should invest most of her resources selfishly in herself because she bears 100 of her genes while her children only bear 50 of them. However, things not equal - her children are younger and more helpless and so would benefit more from each unit of investment than she would.
Furthermore, there is no genetic reason for a mother to have favourites because her relatedness to all of her children is same: a half. Therefore, her optimal strategy is to invest equally in the largest number of children that she can rear to the age when they have children of their own. However, some individuals are better life insurance risks than others are. For example, an under-sized runt bears just as many of his mother's genes as his healthier siblings but his life expectancy is less. Therefore, a mother may either decide to refuse to feed him because it would be a waste of the resources that could otherwise have gone to a child who stood a greater chance of going on to reproduce themselves.
This is from the parent's point of view. On genetic grounds alone, a mother should have no favourites and if she does show favouritism, it should be based on differences in other factors such as life expectancy. However, children will try to get their mother to invest in him more than in any particular sibling because he is twice as closely related to himself as he is to any brother or sister. Thus, if you and your brother are the same age and both are in a position to benefit equally from a pint of mother's milk, you "should" try to grab more than your fair share. However, this behaviour only occurs up to a point. In other words, if you are competing with your brother for a morsel of food, and he is much younger than you are, then he would benefit more from the food than you would. Therefore it would pay your genes to let him have it when the resulting net benefit to him is half the cost of the for-going it yourself.
Therefore, a conflict between the parents and the young arises due to their differing optimal conditions. A child will pretend to be hungrier than it is in order to receive more food than its fair share as it will benefit its genes to become stronger. Parents on the other hand must be alert to this cheating and deceiving, and must try not to be fooled as it does not benefit its genes to have favourites. But this is not as easy as it seems. If a parent ignores a child's screams of hunger, thinking it to be an attempt at deception, but the child in not lying and consequently dies, the parent will have lost some of its precious genes. This is where the battle lies and what will finally emerge is a compromise between the ideal situation desired by the child and that desired by the parent.