Showing items related by title, author, creator and subject.

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  Establishing a Process Framework for Land Use Planning (invited)

  Lundeen, Lloyd J.; Watershed Systems Development and Application Unit, U. S. Forest Service, Berkeley, California (Arizona-Nevada Academy of Science, 1974-04-20)

  The operational aspects of land use planning, to be effective, must be tied to a well defined planning process. The framework for this process includes a set of main components which are important in solving land use planning problems. These components are linked together in a design related to the basic concepts of decision analysis which has been oriented to natural resource problems. Detailed description can be added to the .process framework to tailor it to a specific problem, study area, or study level. Some of the major components in the framework are an objectives and goals spectrum, problem formulation, physical characterization of the land, social and economic demands analysis, identification of management alternatives and specific activities, simulation of resource response, allocation of resources, visual quality analysis, transportation system analysis, and a data management system. This process framework is d »sinned to he dynamic, user oriented, and compatible with the type of problems encountered in land use planning.
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  AN EXPERT SYSTEM USING FUZZY SET REPRESENTATIONS FOR RULES AND VALUES TO MAKE MANAGEMENT DECISIONS IN A BUSINESS GAME.

  Ferrell, William R.; DICKINSON, DEAN BERKELEY. (The University of Arizona., 1984)

  This dissertation reports on an effort to design, construct, test, and adjust an expert system for making certain business decisions. A widely used approach to recurring judgmental decisions in business and other social organizations is the "rule-based decision system". This arrangement employs staff experts to propose decision choices and selections to a decisionmaker. Such decisions can be very important because of the large resources involved. Rules and values encountered in such systems are often vague and uncertain. Major questions explored by this experimental effort were: (1) could the output of such a decision system be mimicked easily by a mechanism incorporating the rules people say they use, and (2) could the imprecision endemic in such a system be represented by fuzzy set constructs. The task environment chosen for the effort was a computer-based game which required player teams to make a number of interrelated, recurring decisions in a realistic business situation. The primary purpose of this research is to determine the feasibility of using these methods in real decision systems. The expert system which resulted is a relatively complicated, feed-forward network of "simple" inferences, each with no more than one consequent and one or two antecedents. Rules elicited from an expert in the game or from published game instructions become the causal implications in these inferences. Fuzzy relations are used to represent imprecise rules and two distinctly different fuzzy set formats are employed to represent imprecise values. Once imprecision appears from the environment or rules the mechanism propagates it coherently through the inference network to the proposed decision values. The mechanism performs as well as the average human team, even though the strategy is relatively simple and the inferences crude linear approximations. Key aspects of this model, distinct from previous work, include: (1) the use of a mechanism to propose decisions in situations usually considered ill-structured; (2) the use of continuous rather than two-valued variables and functions; (3) the large scale employment of fuzzy set constructs to represent imprecision; and (4) use of feed forward network structure and simple inferences to propose human-like decisions.
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  The Influence of Pain Self-Management Education on the Prevalence of Opioid Prescription among Patients with Chronic Non-Cancer Pain: An Agent-Based Modeling Simulation

  Shea, Kimberly D.; Samuel-Ojo, Catherine Olubunmi; Shea, Kimberly D.; Shea, Kimberly D.; DuBois, Janet C.; Martin-Plank, Lori M. (The University of Arizona., 2015)

  Chronic pain has no cure. It is a lifelong condition presenting a growing concern due to its high occurrence and effects on every facet of life. It cost about $635 billion each year in medical treatment and lost productivity (IOM, 2011). The management of chronic pain using prescription painkiller opioids has increased drastically in the last two decades, leading to a consequential increase in deaths from chronic opioid use. This Plan-Do-Study-Act quality improvement project investigates the problem of the prevalence of opioid prescription using agent-based computational modeling method. The simulation models the interaction of 50 patient-agents with pain self-management messages in an episode of 50 patient iterations (visits) for 10 simulated years. This interaction generates health benefit and risk outcomes represented by wellness data obtained when messages are processed. As the simulation runs, data are dynamically captured and visualized using wellness charts, time series plots, and benefit and risk regression plots. The result of the project provides evidence for research and practice on the process of achieving more impact of programs based on administering pain self-management education to patients with chronic non-cancer pain who are currently on opioid therapy and on the process of customizing interventions that might take advantage of the conditions of behavior change driven by pain self-management messages. The tools and the evidences in this project are highly recommended to nurse practitioners primary care providers involve with providing care to the vulnerable groups of patient with chronic non-cancer pain. These evidences might inform the formation of self-management interventions that might lead to a decline in opioid use and prescription and accelerate the acceptance of self-management practices.