In various markets customers behave loyal to specific sellers.
The purpose of this thesis is to replicate an agent-based model that explains the emergence of loyalty between buyers and sellers, where buyers learn to become loyal and sellers learn to offer advantages to loyal buyers. Both, sellers and buyers, use reinforcement learning to adapt their behaviour towards an optimal one for them. Furthermore, different network structures as well as mechanisms for modelling endogenous interactions are described and an overview of agent-based modelling is provided. Finally, this thesis examines how information spread between buyers affects the formation of loyalty. First, the agent-based model about loyalty was implemented in NetLogo to verify its results with the findings of the original model. Afterwards, the model was extended by allowing the buyers to spread and receive information about sellers, which influenced the seller-choosing process of the buyers. Therefore buyers were endowed with 'temporal spatial' social networks, which were formed by their actual neighbours of the sellers queue. Furthermore, buyers had the possibility to learn the importance of received information to incorporate them accordingly into their sellers-choosing process. The replicated model successfully reproduced the outcomes about loyalty. The results of the extended model showed that positive information about other sellers reduced the loyalty, whereas negative information about other sellers had no effects on the emerged level of loyalty. Moreover, buyers learned to put high attention to received positive information.