Hello
In the context of agent-based modeling (ABM); managing computational complexity and scalability is crucial when dealing with large-scale models that involve many agents and complex interactions. This question seeks insights ; recommendations from the community on strategies and tools for efficiently handling these challenges.
What techniques have you found effective for optimizing agent-based models to run efficiently? For instance; are there specific algorithms or coding practices that help reduce computational load?
How do you approach scaling models to handle thousands ; even millions of agents? Are there any particular tools or frameworks that facilitate scalability in ABM?
What methods do you use to assess the performance of your model; and how do you identify bottlenecks? I have checked Guide to Managing Computational Complexity in Agent-Based Models splunk for reference but still need help.
Have you utilized parallel computing or distributed systems to improve model performance? If so; what has been your experience?
Can you share any case studies ; examples where you’ve successfully addressed computational complexity in large-scale ABM projects?
Thank you 