Least change secant methods, for function minimization, depend on finding a “good” symmetric positive definite update to approximate the Hessian. This update contains new curvature information while simultaneously preserving, as much as possible, the built-up information from the previous update. Updates are generally derived using measures of least change based on some function of the eigenvalues of the (scaled) Hessian. A new approach for finding good least change updates is the multicriteria problem of Byrd, which uses the deviation from unity, of the n eigenvalues of the scaled update, as measures of least change. The efficient (multicriteria optimal) class for this problem is the Broyden class on the “good” side of the symmetric rank one (SR1) update called the Broyden efficient class. This paper uses the framework of multicriteria optimization and the eigenvalues of the scaled (sized) and inverse scaled updates to study the question of what is a good update. In particular, it is shown that the basic multicriteria notions of efficiency and proper efficiency yield a region of updates that contains the well-known updates studied to date. This provides a unified framework for deriving updates. First, the inverse efficient class is found. It is then shown that the Broyden efficient class and inverse efficient class are in fact also proper efficient classes. Then, allowing sizing and an additional function in the multicriteria problem, results in a two parameter efficient region of updates that includes many of the updates studied to date, e.g., it includes the Oren-Luenberger self-scaling updates, as well as the Broyden efficient class. This efficient region, called the self-scaling efficient region, is proper efficient and lies between two curves, where the first curve is determined by the sized SR1 updates while the second curve consists of the optimal conditioned updates. Numerical tests are included that compare updates inside and outside the efficient region.