dc.contributor.author Bell, Brian A. dc.date.accessioned 2010-09-29T00:51:31Z dc.date.available 2010-09-29T00:51:31Z dc.date.issued 1975 dc.identifier.uri https://hdl.handle.net/10182/2610 dc.description.abstract The basic problem facing decision makers is the allocation of scarce resources among competing users. Such decisions mads are usually in the face of an essentially unknown future environment. This thesis is concerned with making good decisions under uncertainty in relation to public investment proposals in New Zealand agriculture. en A case is put for the evaluation of risk and uncertainty in agricultural projects, but on a review of evaluation techniques in use it is found that many deficiencies exist. In a search for a practical solution to the risk analysis problem it is shown that decision theory shows very little promise. Rather the solution is to take the risk and uncertainty explicit by presenting the present value in terms of its probability distribution. Two methods are discussed; they are Analytical Techniques and Monte Carlo Methods. The analytical technique is developed through the use of probability calculus. Techniques evolve from being able to calculate the variance of simple summed random variables to handling complex combinations of products of random variables using Taylor's Approximation. Incorporated in the analysis are discussions on subjective probability distributions, forecasting techniques and correlation analysis. It is shown that the shape of the probability distribution is not nearly as important as the way in which the variables relate to each other both within and between periods. Earlier criticisms of traditional techniques of handling risk and uncertainty are overcome. In the analytical technique judgment is applied to the underlying assumptions in the project rather than to the results of the analysis. The variability of the project is measured by a single overall indicator (the variance), not by a number of criteria. The technique allows for interaction between the variables which make up the project. A quantitative assessment of risk is made rather than qualitative statements and lastly tile basic framework is laid for consistent analysis project to project and analyst to analyst. Monte Carlo Simulation offers two major advantages over analytical methods. These are firstly, all the characteristics of the probability distribution of the variables can be simulated and secondly the dynamic aspects of project development can be incorporated into the analysis. There are, however, certain drawbacks to implementation. The major disadvantage is the time required to build a simulation model. The task of developing a general package was found to be beyond the scope of this thesis. The main problem encountered was in defining and incorporating the dependency relationships between variables. A rural water supply scheme is analysed under several risk procedures to test the usefulness and practicability of each method. It is concluded that the analytical technique incorporating Taylor’s approximation shows the most promise for implementation. However, before this can be done several factors require further investigation, the most critical factor being the specification of dependency relationships between stochastic variables. dc.language.iso en en dc.publisher Lincoln College, University of Canterbury en dc.rights.uri https://researcharchive.lincoln.ac.nz/page/rights dc.subject public sector en dc.subject risk analysis en dc.subject risk and uncertainty en dc.subject capital investment en dc.subject cost benefit analysis en dc.title A comparison and evaluation of risk and uncertainty techniques for capital projects in the public sector en dc.type Thesis en thesis.degree.grantor University of Canterbury en thesis.degree.level Masters en thesis.degree.name Master of Agricultural Science en lu.thesis.supervisor McArthur, A. T. G. lu.contributor.unit Department of Accounting, Economics and Finance en dc.subject.anzsrc 150205 Investment and Risk Management en dc.subject.anzsrc 010406 Stochastic Analysis and Modelling en dc.subject.anzsrc 140201 Agricultural Economics en
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