Indirect reciprocity is a key mechanism to understand the evolution of cooperation among unrelated individuals. It involves reputations and complex information processing, arising from social interactions. By helping someone, individuals may improve their reputation, which may be shared in a population and change the predisposition of others to reciprocate in the future. The reputation of individuals depends, in turn, on social norms that define a good or bad action, offering a mathematically appealing way of studying the evolution of moral systems. Over the years, theoretical and empirical research has unveiled many features of cooperation under IR, exploring norms with varying degrees of complexity and information requirements. Often, however, it is assumed that reputations spread for free, and individuals can adopt arbitrarily complex norms and strategies. In this presentation, I will discuss a way to quantify the complexity of norms and strategies in indirect reciprocity and the challenges introduced by complex rules and costly reputation sharing. We will observe that cooperation can prevail with simple norms and costly reputation sharing, provided that individuals can anticipate the reporting intentions of their opponents. I will conclude by discussing future research directions and the prospects of applying mathematical biology methods to design prosocial artificial systems.