What is the goal of 'variance reduction techniques' in simulation?

The goal of variance reduction techniques in simulation is primarily to improve estimations by decreasing the variability in results. When conducting simulations, particularly those based on stochastic processes, the outputs can exhibit a high degree of variability due to the randomness inherent in the processes being modeled. This variability can obscure the underlying trends or patterns and make it difficult to achieve accurate estimations from the simulation outcomes.

By using variance reduction techniques, such as control variates, antithetic variates, and importance sampling, you can effectively smooth out the randomness in the simulation results. These techniques aim to make the estimates more stable and reliable, which enhances the quality of the insights gained from the simulation without requiring an excessively large number of iterations. This ultimately leads to a more precise understanding of the system being studied, allowing for better decision-making based on the simulation outputs.