How balancing a homemade strategy game led me from simulated matches to the incentive problems shared by game design and economic policy.
Why I like starting engineering software with a small model before building the full system.
How to rank simulated F1 strategy futures, add uncertainty with Monte Carlo, and keep the recommendation explainable.
How to generate legal F1 strategy branches, why itertools is the right first tool, and where constraint programming starts to fit.
A first Python race simulator, why the lap loop is useful but limited, and how SimPy gives the model better event structure.
A curious overview of why Formula 1 race strategy is less about one pit call and more about searching a branching space of constrained futures.