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Summary and Recommendations: This brief has discussed several approaches to modeling Social Security: deterministic modeling, sensitivity analysis, and stochastic modeling. Each has its strengths and weaknesses. For example, deterministic models are the easiest to analyze and explain, but they provide no insight regarding the relative importance of the independent variables to the final result. Sensitivity analysis provides insight into how the final result can vary as independent variables are systematically changed but offers no convenient mechanism to assess the likelihood of the scenario being examined. Finally, stochastic modeling allows us to make probabilistic statements regarding the likelihood of various outcomes, but at the cost of a fairly elaborate mathematical and statistical infrastructure, and, even more importantly, with the additional risk of model misspecification. Sensitivity analysis will always be a useful “what if ” methodology in assessing the impact of a change in one or more underlying assumptions. We believe stochastic modeling is useful in performing additional analysis, especially the type of analysis required to evaluate the likelihood of particular scenarios.