## 5 Conclusions  
In this study we developed an abstract ABM of the interactions of a principal and several agents in a hypothetical context where the principal offers the agents recognition and good reputation in return for their cooperation in a behavior that is costly to the agents. Our simulation results showed that in such a setting, the emergence of the desired behavior as a norm is possible. If the agents perceive that the cost of the behavior is low, then emergence of that norm is possible even without guidance of the agents by the principal. If the perceived cost of the behavior is not low, then cases of emergence of that norm are rare. However, we demonstrated through comparison with a random baseline that RL algorithms can effectively guide the agents towards adopting the said behavior. Among the six TD RL algorithms that we tried--namely, SARSA, Q-Learning, Expected SARSA, Double SARSA, Double Q-Learning, and Double Expected SARSA--we noted that Double Learning algorithms obtained better results in the setting of this study. We conclude that with a proper learning algorithm it is possible to create norms of costly behaviors, by using recognition as a reward for participation in the behavior, even in the absence of social sanction and enforcement. Conceptualization, S.H., L.P. and R.M.-H.; methodology, S.H., L.P. and R.M.-H.; software, S.H. ; validation, S.H. ; formal analysis, S.H., L.P. and R.M.-H.; investigation, S.H., L.P. and R.M.-H.; resources, L.P.; data curation, S.H. ; writing--original draft preparation, S.H., L.P. and R.M.-H.; writing--review and editing, S.H., L.P. and R.M.-H.; visualization, S.H. ; supervision, L.P. and R.M.-H.; project administration, L.P.; funding acquisition, L.P. and R.M.-H. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada through the Discovery Grant number RGPIN/05396-2016 awarded to L.P., R.M.-H. received financial support from the European Union's Seventh Framework Programme through NEWFOREST programme number PIRSES-GA-2013-612645. Not applicable. Code of the model is available at [[https://github.com/s-harati/model-Cooperation](https://github.com/s-harati/model-Cooperation)]([https://github.com/s-harati/model-Cooperation](https://github.com/s-harati/model-Cooperation)) (accessed on 23 July 2021). Datasets of results of simulations as well as flowcharts of the RL algorithms used are available at [[https://osf.io/jyuq7/](https://osf.io/jyuq7/)]([https://osf.io/jyuq7/](https://osf.io/jyuq7/)) (accessed on 23 July 2021). We are thankful to two anonymous reviewers who read the manuscript and provided helpful feedback towards its improvement. We are also thankful to the Universite de Montreal's International Affairs Office (IAO) for their financial support through the International Partnership Development program, which allowed the collaboration between researchers from UdeM and CREAF. The authors declare no conflict of interest. * [PERSON] (1973) [PERSON] _Perspectives on Social Order_; McGraw-Hill: New York, NY, USA, 1973. * [PERSON] and Cranefield (2011) [PERSON]; [PERSON] Norm creation, spreading and emergence: A survey of simulation models of norms in multi-agent systems. _Multiagent Grid Syst._**2011**, \(7\), 21-54. [CrossRef]
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