Crowd: Ajan tabanlı sosyal ağ simülasyonuna yönelik bir çerçeve ve uygulamaları

To observe how individual behaviors shape the actions of a broader community, agent-based modeling and simulation (ABMS) has been widely adopted by researchers in social sciences, economics, and epidemiology. While simulations can be executed on general-purpose ABMS frameworks, these tools are not specifically designed for social networks and, therefore, provide limited features, increasing the effort required for complex simulations. In this thesis, we first introduce Crowd, a social network simulator that adopts the agent-based modeling methodology to model real-world phenomena within a network environment. Designed to facilitate easy and quick modeling, Crowd supports simulation setup through YAML configuration and enables further customization through user-defined methods. Other features of Crowd include no-code simulations for diffusion tasks, interactive visualizations, data aggregation, and chart drawing facilities. Designed in Python, Crowd also supports generative agents that rely on LLMs for agents' decisions and connects easily with Python's libraries for data analysis and machine learning. Secondly, we present three case studies to illustrate the application of the framework, including generative agents in epidemics, influence maximization, and networked trust games. Finally, we design and implement a simulation that combines generative agents with social networks to analyze the effects of agents' personalities and social connections in the context of building energy modeling.