It is traditional in game theory to model cooperation as the play of a given strategy in a social dilemma. This approach is subject to the criticism that cooperation has to be separately defined for each new situation in which it is considered.
Recently, collaboration — the ability to participate in collective decision making and optimization, has been proposed as an alternative approach to cooperative behavior.
Collaboration has the benefit that it can be defined independently of any game. In a paper published in PLOS – Computational Biology, Simon Angus and Jonathan Newton bring these two approaches together, showing that even relatively rare opportunities for collaboration can support robust levels of cooperation, especially when interaction networks are sparse.
This result is significant as human networks are often sparse and so our results support the wide distribution and persistence of cooperation across human populations.