A mathematical model has revealed a genetic basis for selfishness and altruism

Credit iStock
Credit iStock

 

A mathematical model could help us understand why some people evolve to be nice and why others seem to be inherently nasty.
The model, designed by Sasha Dall from the University of Exeter, offers a framework for “examining in a range of different species” to help us understand how social relationships have evolved over time.

The model is based on the idea of “kin selection”, which seeks to explain why some animals are altruistic – sometimes at their own expense – for the benefit of their pack or family as a whole. One example used by Dall is worker . These bees are willing to die for the benefit of the Queen , but why this behaviour evolved was previously unknown.

The Exeter team examined microbial colonies to explore the basis of altruism.

The found that there was a genetic tendency in some species that could predict whether an animal was likely to be altruistic or selfish. This tendency was more pronounced than other factors, including relatedness to members of community and surroundings.

“As humans, our behaviours are flexible and we base what we are meant to do on what we are meant to do on what we see after processing information about our world,” said Dall.

“However, some species rely on inherited instructions on what to do – individuals behave differently according to which specific genetic variants they are born with. What we have been able to show is how you can get a situation where you end up with distinct levels of genetically determined niceness coexisting within populations.”

Olof Leimar, a professor at Stockholm University who also worked on the paper, said that social evolution theory had never previous addressed “genetic polymorphism”.
“We have developed a model that allows us to explore this further within a general framework alongside other behavioural influences. Our hope and aim is to do further work in this area to test out our model experimentally.”

The study has been published by PLOS Computational Biology.

 

 

 

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