Agent-based modeling in e-commerce

In the rapidly evolving field of e-commerce, conventional research methods struggle to keep pace with the dynamic landscape characterised by exponential growth and changing user behaviours. To address this challenge, agent-based modelling and simulation offer a promising research paradigm. This chapter explores the potential of agent-based modelling and simulation in capturing the intricate dynamics of the e-commerce environment and advancing our understanding of this complex domain. We provide an overview of agent-based modelling and simulation applications in various e-commerce domains and identify three compelling avenues for future research. Firstly, exploring the emergence of network structures helps actors understand communication and information-sharing patterns among them, revealing their impact on e-commerce dynamics. Secondly, considering individual differences in personality and culture unveils how these factors influence behaviours, preferences, and decision-making processes in e-commerce. Lastly, analysing longitudinal dynamics and asynchronous timelines captures evolving patterns and long-term effects seen in e-commerce phenomena. Agent-based modelling allows researchers to track the evolution of these dynamics over time. To showcase the power of agent-based modelling and simulation in e-commerce, the chapter presents a case study that focuses on the longitudinal dynamics of multistakeholder recommendation systems. It highlights the versatility and effectiveness of agent-based modelling in capturing heterogeneous consumer preferences, diverse objectives of recommendation providers and users of recommendation services, and the longitudinal dynamics of a set of recommendation strategies.