Meritocratic matching solves the problem of cooperation by ensuring that only prosocial agents group together while excluding proselfs who are less inclined to cooperate. However, matching is less effective when estimations of individual merit rely on group-level outcomes. Prosocials in uncooperative groups are unable to change the nature of the group and are themselves forced to defect to avoid exploitation. They are then indistinguishable from proselfs, preventing them from accessing cooperative groups. We investigate informal social networks as a potential solution. Interactions in dyadic network relations provide signals of individual cooperativeness which are easier to interpret. Network relations can thus help prosocials to escape from uncooperative groups. To test our intuitions, we develop an ABM modeling cooperative behavior based on a stochastic learning model with adaptive thresholds. We investigate both randomly and homophilously formed networks. We find that homophilous networks create conditions under which meritocratic matching can function as intended. Simulation experiments identify two underlying reasons. First, dyadic network interactions in homophilous networks differentiate more between prosocials and proselfs. Second, homophilous networks create groups of prosocial agents who are aware of each other’s behavior. The stronger this prosociality segregation is, the more easily prosocials cooperate in the group context. Further analyses also highlight a downside of homophilous networks. When prosocials successfully escape from uncooperative groups, non-cooperatives have fewer encounters with prosocials, diminishing their chances to learn to cooperate through those encounters.
|Tijdschrift||Journal of Artificial Societies and Social Simulation|
|Nummer van het tijdschrift||1|
|Status||Published - 2022|