## 1 Introduction
Some characteristics for complex systems are nonlinearity, self-organization, going beyond equilibrium, and existence of attractors other than a state of equilibrium, such that the combination of these characteristics can cause the emergence of new patterns in complex systems [14]. Complex systems literature uses the expression 'aggregate complexity' to refer to the interaction of system components that results in holism and synergy, with key attributes of such aggregate complexity being internal relationships, internal structure (subsystems), relationships with the environment, learning and memory, emergence, and change and evolution [15]. One of the approaches used in the study of complex systems is Agent-Based Modeling. An Agent-Based Model (ABM) consists of multiple agents acting upon their individual objectives [16]. ABMs are constructed in a bottom-up manner and allow us to compute the aggregate and large scale results of the interactions of agents with each other and with their environment [17]. As such, ABMs have been used in a wide variety of disciplines. Some examples of these applications include innovation diffusion [18], theory of cooperation [19], automated negotiation [20], recommender systems [21], migration [22], urban segregation [23; 24; 25] epidemiology [26], forest ecology [27], and species distribution [28]. ABMs have also been applied in sustainable development studies, such as in urban planning [29], sustainable transportation [30], circular economy [31], and in problems related to the tragedy of the commons [6; 32]. ABMs have been extensively used in studies of social norms [8]. Some models apply the Belief-Desire-Intention (BDI) framework in the decisions of their agents [33; 34]. In the BDI framework, agents have mental attributes of belief, desire, and intention, which indicate their state in terms of information, motivation, and deliberation for action, respectively [35; 36]. Some of the mechanisms employed in agent-based normative simulationsare leadership [37], learning by imitation [38], machine learning and reinforcement learning [39], norm recognition [40], and reputation [41]. In a review of the literature, [PERSON] and [PERSON] [12] identify areas for research and improvement. Some of those areas are norm creation and ideation, alternatives for social sanction, and the verification and validation of models [12]. Given the above context, our focus in this study is on the creation and emergence of a new descriptive norm in a setting with central leadership, where there are rewards for performing the behavior that the leadership promotes, but there are no sanctions or punishments for the non-performers. The reward in this setting is reputation and recognition as a responsible member of the society. We take an agent-based simulation approach to explore the possibility of norm emergence in such a setting. Our conceptual framework is as follows: several user agents act upon self-interest, while a governing agent requests the user agents to take a costly action. There is no force in the governing agent's request. If the user agents cooperate with the governing agent, then the governing agent acknowledges them by giving them a'responsible user' label. The governing agent can choose what action it shall request users to do--an action that is easy for all users but useless for the governing agent, or an action that is difficult for users and desirable for the governing agent. In the former case, the governing agent gives free'responsible user' labels to all users. In the latter case, the user agents estimate the benefit of having the label. They do so by considering if the label makes them unique in their group, and if being unique in owning the label has any value. Such value is zero at first and increases with the exposure of the group to the label over time. Ultimately, user agents compare their estimated benefit of gaining the label with their own perception of the cost of the action they are asked to do. This way, they decide if they will cooperate with the governing agent.