Abstract

W. D. Hamilton (1964) famously showed that an altruistic mutant would be favoured by selection if the cost of altruism was outweighed by the benefits it provides to other copies of the mutation. This kin selection framework, therefore, identified relatedness as the critical property of interactions, providing a link between the costs and benefits of altruism from the perspective of a mutation. Less appreciated, however, is the part of Hamilton’s original analysis focused ‘discrimination in social situations’, where he argued that a mutation would be even more favoured if, instead of relying on relatedness, it could directly discriminate carriers of the mutation from non-carriers, and preferentially direct benefits towards the carriers. To explain the logic of this scenario, Richard Dawkins devised his ‘greenbeard’ thought experiment, where a single gene produces a signal (a green beard), identifies that signal in others, and modifies behaviour to direct help towards other green bearded individuals. These manifold requirements made greenbeard genes appear biologically implausible, but they have, nonetheless, been described in a diverse array of organisms. To understand how greenbeard genes evolved, why they are typically hyper-polymorphic, and how they are able to achieve all of these properties, we have been combining theoretical, computational, and empirical approaches. I will provide a general overview of our work to date and discuss our ongoing work along these lines.