Kin selection, not indirect reciprocity, explains helping those who help others in image scoring

Indirect reciprocity is often considered key to human sociality. It occurs when helping others results in an increase in reciprocal benefits from third parties. Indirect reciprocity has been proposed to operate through image scoring mechanisms, whereby those who have been seen to help are more likely to receive help. Here I show that helping those who help others in image scoring models is not a mechanism of indirect reciprocity but is instead due to kin selection. Image scoring strategies are not individually adaptive because they base the decision to help upon whether a recipient has previously helped others, rather than upon whether helping would maximize their own return. Using analysis supported by evolutionary simulation, I show that helping can be favoured in image scoring systems without reciprocation, hence image scoring systems are not models of indirect reciprocity at all. Further, I use simulation of image scoring in meta-population models to illustrate how helping is a function of relatedness. I show that relatedness explains cooperation in image scoring (and associated) systems because helpers get indirect fitness benefits from helping others that share their strategy. A strategy of helping those who help others helps other copies of itself. In this way, helping behaviour ceases to be costly, and is in fact beneficial to the strategy, though not to the individual. Thus, despite some recent doubts about the usefulness of Hamiltons rule, image scoring systems demonstrate the role of relatedness in driving cooperation.