From biological models to economic optimization.

This article addresses the additional challenges being faced when biological models are used as a basis for decision support in livestock herds. The challenges include dealing with uncertain information, observation costs, herd dynamics and methodological issues in relation to the computational methods applied particularly in the dynamic case. The desired key property of information included in models is that it can be used as the basis for unbiased prediction of the future performance of the animals. Often there will be a tradeoff between uncertainty and costs in the sense that the level of uncertainty can be reduced (for instance through additional tests) at some cost. Thus, the decision about which (and how many) tests to perform can be seen as an optimization problem in itself. Another way of expressing the tradeoff is to talk about the value of information which can sometimes be assessed by modeling different approaches and levels of detail in data collection. Various optimization methods of relevance to herd health management are discussed with the main emphasis on decision graphs in the static case and Markov decision processes (dynamic programming) in a dynamic context.