This chapter focuses on three questions: what norms are, how they emerge, and how much and what type of mental complexity they need; and the chapter presents a dynamic model of norms and the corresponding agent architecture (EMIL-A), and shows the results of its application to a stylized environment (a social multi-setting world). The chapter then illustrates a simulator, EMIL-S, on which EMIL-A has fully been implemented, showes its effects on the emergence of a new norm in a more complex artificial context (artificial Wikipedia), and compares the results with data from a survey on the real-world domain of reference (Wikipedia). This chapter describes EMIL-I-A (EMIL Internalizer Agent), an extension of EMIL-A designed to account for a deeper form of norm immergence than addressed so far; i.e., norm internalization. Then it presents simulation results aimed at testing how EMIL-I-A performs in dynamic, unpredictable scenarios.
