Status Quo Analysis of the Flathead River Conflict

Status quo analysis algorithms developed within the paradigm of the graph model for conflict resolution are applied to an international river basin conflict involving the United States and Canada to assess the likeliness of various compromise resolutions. The conflict arose because the state of Montana feared that further expansion of the Sage Creek Coal Company facilities in Canada would pollute the Flathead River, which flows from British Columbia into Montana. Significant insights not generally available from a static stability analysis are obtained about potential resolutions of the conflict under study and about how decision makers’ interactions may direct the conflict to distinct resolutions. Analyses also show how political considerations may affect a particular decision maker’s choice, thereby influencing the evolution of the conflict

Water Supply Planning under Interdependence of Actions: Theory and Application

An ongoing water supply planning problem in the Regional Municipality of Waterloo, Ontario, Canada, is studied to select the best water supply combination, within a multiple-objective framework, when actions are interdependent. The interdependencies in the problem are described and shown to be essential features. The problem is formulated as a multiple-criteria integer program with interdependent actions. Because of the large number of potential actions and the nonconvexity of the decision space, it is quite difficult to find nondominated subsets of actions. Instead, a modified goal programming technique is suggested to identify promising subsets. The appropriateness of this technique is explained, and the lessons learned in applying it to the Waterloo water supply planning problem are described

Progress Report to the TNRC for Analysis of the Economics of Atrazine Remediation for Representative Grain Farms in the Aquilla Watershed, Hill County, Texas: Subtasks 4.0-4.4

Four alternative BMPs for atrazine remediation were reported by Harmon and Wang for the study area. The BMPs involved alternative incorporation practices, tillage operations, and sediment ponds. Harmon and Wang reported no statistical difference in corn yields under the alternative BMPs. An economic analysis of four alternative best management practices (BMPs) for atrazine remediation in Hill County, Texas, was performed by the Agricultural and Food Policy Center (AFPC) at Texas A&M University. Using the farm-level economic simulation model FLIPSIM, AFPC scientists analyzed the financial effects of the alternative BMPs on the Texas Blackland Prairie representative farm. This farm consists of 2,000 dryland acres, divided among corn (600 acres), sorghum (750 acres), wheat (250 acres), and native pasture (150 acres). This farm also maintains a small beef cowherd. Regularly updated, the AFPC maintains more than 80 farms across the nation that form the basis for probabilistic-based agricultural policy evaluation.Agricultural and Food Policy, Resource /Energy Economics and Policy,

Real-time in-flight engine performance and health monitoring techniques for flight research application

Various engine related performance and health monitoring techniques developed in support of flight research are described. Techniques used during flight to enhance safety and to increase flight test productivity are summarized. A description of the NASA range facility is given along with a discussion of the flight data processing. Examples of data processed and the flight data displays are shown. A discussion of current trends and future capabilities is also included

DISTRIBUTION CHOICE UNDER NULL PRIORS AND SMALL SAMPLE SIZE

Defining appropriate probability distributions for the variables in an economic model is an important and often arduous task. This paper evaluates the performance of several common probability distributions under different distributional assumptions when sample sizes are small and there is limited information about the data.Research Methods/ Statistical Methods,

Real-time in-flight engine performance and health monitoring techniques for flight research application

Procedures for real time evaluation of the inflight health and performance of gas turbine engines and related systems were developed to enhance flight test safety and productivity. These techniques include the monitoring of the engine, the engine control system, thrust vectoring control system health, and the detection of engine stalls. Real time performance techniques were developed for the determination and display of inflight thrust and for aeroperformance drag polars. These new methods were successfully shown on various research aircraft at NASA-Dryden. The capability of NASA's Western Aeronautical Test Range and the advanced data acquisition systems were key factors for implementation and real time display of these methods

STOCHASTIC EFFICIENCY ANALYSIS USING MULTIPLE UTILITY FUNCTIONS

Evaluating the risk of a particular decision depends on the risk aversion of the decision maker related to the underlying utility function. The objective of this paper is to use stochastic efficiency with respect to a function (SERF) to compare the ranking of risky alternatives using alternative utility functional forms.Research Methods/ Statistical Methods,

Stochastic efficiency analysis with risk aversion bounds: a simplified approach

A method of stochastic dominance analysis with respect to a function (SDRF) is described and illustrated. The method, called stochastic efficiency with respect to a function (SERF), orders a set of risky alternatives in terms of certainty equivalents for a specified range of attitudes to risk. It can be applied for conforming utility functions with risk attitudes defined by corresponding ranges of absolute, relative or partial risk aversion coefficients. Unlike conventional SDRF, SERF involves comparing each alternative with all the other alternatives simultaneously, not pairwise, and hence can produce a smaller efficient set than that found by simple pairwise SDRF over the same range of risk attitudes. Moreover, the method can be implemented in a simple spreadsheet with no special software needed.Risk and Uncertainty,

Using decision-tree classifier systems to extract knowledge from databases

One difficulty in applying artificial intelligence techniques to the solution of real world problems is that the development and maintenance of many AI systems, such as those used in diagnostics, require large amounts of human resources. At the same time, databases frequently exist which contain information about the process(es) of interest. Recently, efforts to reduce development and maintenance costs of AI systems have focused on using machine learning techniques to extract knowledge from existing databases. Research is described in the area of knowledge extraction using a class of machine learning techniques called decision-tree classifier systems. Results of this research suggest ways of performing knowledge extraction which may be applied in numerous situations. In addition, a measurement called the concept strength metric (CSM) is described which can be used to determine how well the resulting decision tree can differentiate between the concepts it has learned. The CSM can be used to determine whether or not additional knowledge needs to be extracted from the database. An experiment involving real world data is presented to illustrate the concepts described

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base