Assume you were to use values of 0.1, 0.5, and 0.9 in a simple exponential smoothing

Assume you were to use α values of 0.1, 0.5, and 0.9 in a simple exponential smoothing model. How would these different a values weight past observations of the variable to be forecast? How would you know which of these α values provided the best forecasting model? If the α = 0.9 value provided the best forecast for your data, would this imply that you should do anything else? Does exponential smoothing place more or less weight on the most recent data when compared with the moving-average method? What weight is applied to each observation in a moving-average model? Why is smoothing (simple, Holt's, and Winters') also called exponential smoothing?