| Summary: | Agricultural policy analysis can be visualised as a multiple objective hierarchical optimisation problem whereby sequential non-cooperative interactions between policy makers and farmers take place. The objectives and choices of policy makers will almost always diverge from the objectives and choices of farmers. This thesis shows how multi-objective genetic algorithms (MOGA) in conjunction with mathematical programming can be used for solving this type of problem. A positive mathematical programming (PMP) model is developed to capture the production choices of farmers, and its objective function parameters are estimated using the method of generalised maximum entropy. The PMP model is nested in, and controlled by, a MOGA which captures the process of multi-objective optimisation of policy decisions. The approach is illustrated using a case study taken from Scottish agricultural systems, where several socio-economic and environmental objectives for policy making are considered. Five types of policy instruments are examined: the current single payment scheme, a multi-payment scheme based on land use, an input taxation, a regulatory scheme and, a combination of the last three. For a selection of scenarios alternative Pareto-optimal solutions are discovered and tradeoffs between policy objectives are presented along with their associated production patterns. Two lines of conclusions are drawn: (1) the performance of the method suggests that it is well suited to dealing with real world applications of policy optimisation and, (2) the current agricultural policy may be sub-optimal in relation to most of the policy objectives examined; more effective policies are possible for Scottish agriculture.
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