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In this paper we combine two points made in two previous papers on negative correlation learning (NC) by different authors, which have theoretical implications for the optimal setting of λ, a parameter of the method whose correct choice is critical for stability and good performance. An expression for the optimal λ is derived whose value λ* depends only on the number of classifiers in the ensemble. This result arises from the form of the ambiguity decomposition of the ensemble error, and the close links between this and the error function used in NC. By analyzing the dynamics of the outputs we find dramatically different behavior for λ<λ*, λ=λ* and λ>λ*, providing further motivation for our choice of λ and theoretical explanations for some empirical observations in other papers on NC. These results will be illustrated using well known synthetic and medical datasets.