Information and public sector decisions
The theoretical models in this thesis address questions relating to the interaction between information and decisions. The main issues are as follows: i) decisions are based on uncertain parameters, ii) parameter estimates are used for specific policy decisions, iii) policy decisions take the form of sequential reforms whose magnitude and frequency must be determined, iv) there are dynamic interactions between the properties of estimators and the performance of decision rules. The method of investigation is by formulation of algebraic models whose properties are examined by analytic and numerical techniques. The contribution to the knowledge of the subject is as follows: i) a well-known linear control model is extended to incorporate sequential reforms, ii) the properties of a limited class of optimal active learning strategies are described, iii) in Monte Carlo simulations, least squares estimates are not found to have desirable tatist al properties when used in conjunction with active earning decision rules, iv) a number of well-known optimal tax models are extended to incorporate parameter uncertainty.