Acknowledgements Chris O'Donnell helpfully provided access to Gauss code that he had written for estimation of latent class models so it could be translated into Shazam. Leighton Brough (UQ), Ariel Liebman (UQ) and Tom Pechey (UMelb) helped by organising a workshop with Nimrod/enFuzion at UQ in June 2006. enFuzion (via Rok Sosic) provided a limited license for use in the project. Leighton Brough, tools coordinator for the ARC Centre for Complex Systems (ACCS), assisted greatly in setting up an

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... nFuzion grid and collating the large volume of results. Son Nghiem provided assistance in collating and analysing data as well as in creating some of the figures. ABSTRACT Production functions that take into account uncertainty can be empirically estimated by taking a state contingent view of the world. Where there is no a priori information to allocate data amongst a small number of states, the estimation may be carried out with finite mixtures model. The complexity of the estimation almost guarantees a large number of local maxima for the likelihood function. However, it is shown, with examples, that a variation on the traditional method of finding starting values substantially improves the estimation results. One of the major benefits of the proposed method is the reliable estimation of a decision maker's ability to substitute output between states, justifying a preference for the state contingent approach over the use of a stochastic production function.