It’s a well-established fact that natural selection favors inter-kin cooperation. If you help your sister or cousin when they’re in need, this will increase the chances your gene pool will get passed on. Yet, natural selection shouldn’t favor the aid of distant kin, so why do we do it? A new mathematical framework developed by Doug Jones, a University of Utah anthropologist, takes into account “socially enforced nepotism” to account for the disparity between what the theory says and what really happens.
“Socially enforced nepotism” is a fancy way of saying society pressures us to be kind with our distant relatives because that’s how the rules work, despite there’s no genetic payoff. In other words, there’s a genetic and moral dimension to our decision-making as far as helping other people is concerned.
Classic kin-selection theory is based on a famous game theory formula called Hamilton’s rule. In short, this formula specifies the conditions under which reproductive altruism evolved:
r × B > C;
Where B is the benefit (in number of offspring equivalents) gained by the recipient of the altruism, C is the cost (in number of offspring equivalents) suffered by the donor while undertaking the altruistic behaviour, and r is the genetic relatedness of the altruist to the beneficiary. From this equation, it’s clear that as you stray away from your own gene pool the costs outweigh the benefits.
What Jones set out was to prove that Hamilton’s rule can be overridden if we also account for the reputation benefit gained from helping a distant kin.
“Two brothers have a chance to help a third brother,” he says. “If the two decide independently of one another whether to help, Hamilton’s rule applies. But if one approaches the other with an offer, ‘I’ll give extra help if you do too,’ then the level of altruism toward kin may be higher than the simple version of Hamilton’s rule predicts,” Jones said, referring to his earlier work on the Brothers Karamazov Game, based on the novel with the same name.
For his study, Jones devised a model then ran simulations with the numbers of players ranging from ten to hundreds, each of variable ability to help. In these games, various virtual actors “choose different strategies and get payoffs depending on what strategies they choose and what strategies other people choose,” Jones wrote in his paper published in the journal PLOS ONE.
In the simulation, people would value two rules as follow:
- “Almost-balanced reciprocity, where you help other people only a much as they help you.”
- “Generalized reciprocity, where you might be very helpful even to someone with no ability to pay you back because other people see this, they like what they see, it boosts your reputation and they reward you for it.”
“Both rules are floating around and you see how they compete with each other in a simulation,” Jones says. “Some people follow almost-balanced reciprocity, some people follow generalized reciprocity and some compromise. Some players are really strong and can easily help other people, and others are weak and cannot.”
After running multiple simulations for a wide range of values, Jones came to the conclusion that those players who went for generalized reciprocity came out as winners in the grand evolutionary game.
“If you’re helping distant relatives and they’re not paying you back, then all the balanced reciprocity guys [those expecting return payment for help] are looking at you and saying, ‘What a loser,'” Jones says. But if you help others and expect something in return part of the time and not at other times, “then you do better evolutionarily.”
Jones’ study notes: “Some anthropologists argue that human kinship, insofar as it is socially enforced, is divorced from biology. The argument here, on the contrary, is that kinship is uniquely elaborate and important in our species because norms that push people to treat distant kin like close kin have been favored by natural selection.”
