This chapter--which still grapples with the third question posed at the beginning of the book; i.e., how to characterize agents that are able to recognize, adopt, and comply with norms--presents and discusses an agent architecture, EMIL-A, implementing the model of norms introduced in the preceding chapter. The simulations presented are intended to check the roles of norm-recognition and normative beliefs in favoring norm emergence and innovation in a highly fragmented social environment. In particular, on one hand, the chapter aims to examine the performance of our normative agents compared to other, cognitively less complex, agents, like imitators following the rule of the majority. On the other hand, it aims to check whether normative systems can reach convergence even when cultural or material artifacts are obstacles to the spreading of beliefs and behaviors. Will norm-detectives succeed in finding out (new) norms? Or will convergence collapse?
