Expectation formulations and optimal decisions in cattle feedlot problems
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"Decision makers are continually required to pursue certain objectives within frameworks in which expectations play a dominant role. For example, in the agricultural sector of the economy cattle feedlot managers are frequently required to make buying and selling decisions based on anticipated acquisition costs, feed costs, and slaughter prices. Furthermore, in order for a decision maker to develop an optimal strategy (i.e., which will facilitate the attainment of a certain objective such as profit maximization) it is imperative that the role played by expectations in the decision making process be understood. Given that a strategy is based in part on a particular type of expectation formulation (subjective or otherwise) it is possible to consider adjusting the mechanism and thus the strategy if the desired objectives are not being realized. With respect to this role of expectations as a type of instrument in decision models very little has been done in agricultural economic research. This study consists of an investigation of the role of expectations formulations in decision problems of the type just sketched. For purposes of convenience the investigation is carried out in the context of a cattle feedlot operation. Several structural models describing the feedlot operation will be considered. A number of criterion functions will be considered in connection with these structural models in which the expectation mechanism will be regarded as part of the underlying structure. As it turns out, this type of situation poses some interesting problems with respect to "optimal" parameter estimates for the part of the structure describing the expectations generating mechanism. More specifically, the assumed objective function is an important factor in determining the optimal estimators. Since the data available may include observations on endogenous and exogenous variables as well as prior probabilities on the parameters, the estimators examined include classical as well as Bayesian approaches."--Introduction.
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