As an actuary preparing for the SOA Exam C, you’re likely familiar with the importance of Markov chains in modeling complex systems. These chains are a powerful tool for understanding how events evolve over time, and they’re particularly useful in actuarial science for predicting insurance outcomes, managing risk, and optimizing policyholder transitions. The concept of a Markov chain is simple yet profound: it assumes that the future state of a system depends only on its current state, not on any of its past states. This simplification allows us to model and analyze systems that would otherwise be too complex to handle.