Robots reveal why human beings help each other
Using simple robots, scientists have shed light on why do most social animals, including humans, go out of their way to help each other. Read onUpdated: May 04, 2011 17:12 IST
Using simple robots to simulate genetic evolution over hundreds of generations, Swiss scientists have shed light on why do most social animals, including humans, go out of their way to help each other.
If natural selection means that only the fittest individuals survive to pass their genes on to the next generation, then selfless behavior should not exist.
Yet, altruistic gene expression is found in nature and is passed on from one generation to the next.
Biologists have a theory to explain such altruistic behavior: Animals will help one another if they have strong genetic ties, since doing so preserves genes they have in common.
Known as Hamilton's rule of kin selection, the theory simply states that whether or not an organism shares its food with another depends on its genetic closeness (how many genes it shares) with the other organism.
Testing the evolution of altruism using quantitative studies in live organisms has been largely impossible because experiments need to span hundreds of generations and there are too many variables.
However, Floreano's robots evolve rapidly using simulated gene and genome functions and allow scientists to measure the costs and benefits associated with the trait.
The new study by EPFL and UNIL researchers adds a novel dimension: once a foraging robot pushes a seed to the proper destination, it can decide whether it wants to share it or not.
The researchers created groups of relatedness that, in the robot world, would be the equivalent of complete clones, siblings, cousins and non-relatives. The groups that shared along the lines of Hamilton's rule foraged better and passed their code onto the next generation.
The quantitative results matched surprisingly well the predictions of Hamilton's rule even in the presence of multiple interactions.
The study is forthcoming in the online, open access journal PLoS Biology.