This paper uses an overlapping generation model with uncertainty and endogenous fertility to study households¡¦ educational and investment choices. Individuals are assumed to be selfish and the intra-family deals are ruled by a self-enforcing ¡¥family constitution¡¦. Within this framework, parents finance their children¡¦s education inasmuch as they receive a return (a share of the increased earnings accruing to the children) and degree of risk aversion. And we show that the effect of social security on fertility and saving is analyzed both in the absence and in the presence of a perfect capital market. The impact on family's decision of the ability of the bargaining power is one of the focal points that this text is discussed, too. We also show that under this arrangement, individuals purchase less education than socially optimal. This yields a rationale for public action, either via public provision or via subsidization. We analyses both policies and find that they have different implications for households¡¦ fertility decisions. In particular, subsidization should be preferred if we wish to keep the rate of population growth as high as possible.

Due to the development of business process reengineering¡]BPR¡^ and Internet, global logistics management and supply chain management¡]SCM¡^ have been unresistible trend for companies. Supply chain is a complex hierarchical network which links members from upstream to downstream industries. Enterprise can improve process through these linkages to cut down cost or shorten its response time to market. As the scope of inter-organizational activities enlarges, there exist many uncertainty factors in the supply chain network. The existence of such factor not only reduces the performance of supply chain as a whole but also the competitive advantage of individual companies in the supply chain. In other words, understanding and controlling SCM uncertainty factors will mitigate their deleterious impact. The concern of past research only addressed a single dimension of supply chain uncertainty, which is either focused upon production process on the manufacturing side or upon product characteristics on the demand side. This research proposed supply chain uncertainty constructs including demand, manufacturing, and supply aspects, which was based upon the perspective of leading company in a supply chain.This set of constructs was based upon literature survey and examination of focus group formed by academic and industry experts. Exploratory factor analysis was called upon to find out representative factors and correlation analysis was conducted to verify independence of the constructs. The research results indicated that channel, product characteristics, demand forcast, and demand change were included in demand uncertainty. Especially, the channel construct is the most representative one. In the supply aspect, supplier¡¦s ability, type of procurement, material characteristics and stable relationship with suppliers were included in supply uncertainty. Especially, the supplier¡¦s ability is the most representative. In the manufacturing aspect, product complexity, process complexity and engineering change were included in manufacturing uncertainty. Especially, the product complexity construct is the most representative one. This research has established supply chain uncertainty factors. It will be helpful for companies using this scale to evaluate their uncertainty situations. Comparision among heterogeneous industries is also possible because this scale can be applied to discover their differences.

Finding and developing oil and gas resources requires accurate geological information with which to formulate strategies for exploration and exploitation ventures. When data are scarce, statistical procedures are sometimes substituted to compensate for the lack of information about reservoir properties. The most modern methods incorporate geostatistics. Even the best geostatistical methods yield results with varying degrees of uncertainty in their solutions. Geological information is, by its nature, spatially limited and the geoscientist is handicapped in determining appropriate values for various geological parameters that affect the final reservoir model (Massonnat, 1999). This study focuses on reservoir models that depend on geostatistical methods. This is accomplished by quantifying the uncertainty in outcome of reservoir simulations as six different geological variables are changed during a succession of reservoir simulations. In this study, variations in total fluid produced are examined by numerical modeling. Causes of uncertainty in outcomes of the model runs are examined by changing one of six geological parameters for each run. The six geological parameters tested for their impact on reservoir performances include the following: 1) variogram range used to krig thickness layers, 2) morphology around well 14, 3) shelf edge orientation, 4) bathymetry ranges attributed for each facies, 5) variogram range used to simulate facies distribution, 6) extension of the erosion at top of the reservoir. The parameters were assigned values that varied from a minimum to a maximum quantity, determined from petrophysical and core analysis. After simulation runs had been completed, a realistic, 3-dimensional reservoir model was developed that revealed a range of reservoir production data. The parameters that had the most impact on reservoir performance were: 1) the amount of rock eroded at the top of the reservoir zone and 2) the bathymetry assigned to the reservoir facies. This study demonstrates how interaction between geological parameters influence reservoir fluid production, how variations in those parameters influence uncertainties in reservoir simulations, and it highlights the interdependencies between geological variables. The analysis of variance method used to quantify uncertainty in this study was found to be rapid, accurate, and highly satisfactory for this type of study. It is recommended for future applications in the petroleum industry.

This study proposes a new, easily applied method to quantify uncertainty in production forecasts for a volumetric gas reservoir based on a material balance model (p/z vs. Gp). The new method uses only observed data and mismatches between regression values and observed values to identify the most probable value of gas reserves. The method also provides the range of probability of values of reserves from the minimum to the maximum likely value. The method is applicable even when only limited information is available from a field. Previous methods suggested in the literature require more information than our new method. Quantifying uncertainty in reserves estimation is becoming increasingly important in the petroleum industry. Many current investment opportunities in reservoir development require large investments, many in harsh exploration environments, with intensive technology requirements and possibly marginal investment indicators. Our method of quantifying uncertainty uses a priori information, which could come from different sources, typically from geological data, used to build a static or prior reservoir model. Additionally, we propose a method to determine the uncertainty in our reserves estimate at any stage in the life of the reservoir for which pressure-production data are available. We applied our method to San Juan reservoir at Santa Rosa Field, Venezuela. This field was ideal for this study because it is a volumetric reservoir for which the material balance method, the p/z vs. Gp plot, appears to be appropriate.

Large complex construction projects such as building an interstate highway, a dam, a chemical plant, an off-shore oil rig and a waste-to-energy plant often include unpredictable geological conditions, labor supplies, material deliveries, and weather that cause uncertainty. Effective and efficient acquisition and construction require the proactive management of these and other uncertainties to meet performance, schedule, and cost targets. Flexibility in the form of real options can be an effective tool for managing uncertainty and thereby adding value to construction projects. But flexibility can be expensive to obtain, maintain, and implement. Real options theory suggests a general approach and has developed precise valuation models. But these models of simplified real options (compared to managerial practice) have failed to significantly improve practice, partially because of a lack of knowledge of real options use by practicing managers. In contrast, the majority of managerial real options applications are identified, designed, valued, and implemented tacitly by construction managers. Understanding current practice and its similarities and differences with theory is critical for developing operational real options theories that can improve construction practice. Few descriptions of managerial real options practice exist as a basis for improvement. To address this need the current research has experiment subjects manage a simple but uncertain installation project with managerial flexibility. Subjects repeatedly value an option to avoid a slow and expensive system integration failure. Real options theory is used to explain their behaviors by customizing the model of uncertainty to reflect themanagement context. To further analyze managerial real options practice, a system dynamics simulation model of the experimental installation project is developed. Policies for using flexibility to manage uncertainty that are applied by subjects are modeled and performances are simulated across a range of uncertain conditions to evaluate and compare policy effectiveness. All 21 subjects that participated in the research perceived flexibility as an effective tool in managing uncertain projects. But they are not aware of the factors that impact flexibility value. They correctly identified the relationship of some factors with flexibility value but not all of them and not the magnitude of impaction. Further research and development needs for expanding real options theory into the operational management of construction are discussed based on experiment and simulation results.

Accurately estimating hydrocarbon reserves is important, because it affects every phase of the oil and gas business. Unfortunately, reserves estimation is always uncertain, since perfect information is seldom available from the reservoir, and uncertainty can complicate the decision-making process. Many important decisions have to be made without knowing exactly what the ultimate outcome will be from a decision made today. Thus, quantifying the uncertainty is extremely important. Two methods for estimating original hydrocarbons in place (OHIP) are volumetric and material balance methods. The volumetric method is convenient to calculate OHIP during the early development period, while the material balance method can be used later, after performance data, such as pressure and production data, are available. In this work, I propose a methodology for using a Bayesian approach to quantify the uncertainty of original gas in place (G), aquifer productivity index (J), and the volume of the aquifer (Wi) as a result of combining volumetric and material balance analysis in a water-driven gas reservoir. The results show that we potentially have significant non-uniqueness (i.e., large uncertainty) when we consider only volumetric analyses or material balance analyses. By combining the results from both analyses, the non-uniqueness can be reduced, resulting in OGIP and aquifer parameter estimates with lower uncertainty. By understanding the uncertainty, we can expect better management decision making.

Analysts increasingly have used probabilistic approaches to evaluate the uncertainty in reserves estimates based on a decline curve analysis. This is because the results represent statistical analysis of historical data that usually possess significant amounts of noise. Probabilistic approaches usually provide a distribution of reserves estimates with three confidence levels (P10, P50 and P90) and a corresponding 80% confidence interval. The question arises: how reliable is this 80% confidence interval? In other words, in a large set of analyses, is the true value of reserves contained within this interval 80% of the time? Our investigation indicates that it is common in practice for true values of reserves to lie outside the 80% confidence interval much more than 20% of the time using traditional statistical analyses. This indicates that uncertainty is being underestimated, often significantly. Thus, the challenge in probabilistic reserves estimation using a decline curve analysis is not only how to appropriately characterize probabilistic properties of complex production data sets, but also how to determine and then improve the reliability of the uncertainty quantifications. This thesis presents an improved methodology for probabilistic quantification of reserves estimates using a decline curve analysis and practical application of the methodology to actual individual well decline curves. The application of our proposed new method to 100 oil and gas wells demonstrates that it provides much wider 80% confidence intervals, which contain the true values approximately 80% of the time. In addition, the method yields more accurate P50 values than previously published methods. Thus, the new methodology provides more reliable probabilistic reserves estimation, which has important impacts on economic risk analysis and reservoir management.

Thesis (Ph. D.)--University of Chicago, Dept. of Statistics, June 2001. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Includes bibliographical references (p. 147-159).