doi: 10.1177/003754977702800312pmid: N/A
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Loewer, O.J.; Colliver, D.G.; Duncan, G.A.; Mynear, D.K.; Cochran, W.J.
doi: 10.1177/003754977702800303pmid: N/A
This paper discusses the simulation of a housing system in which most of the food and energy used by the occupants is produced locally. Energy is pro duced by solar collectors and methane generators. Food is produced in greenhouses, gardens, and fish tanks. Wastes are recycled as fertilizer and methane gas. The model uses solar energy submodels, engineer ing descriptions of heating and cooling systems, weather and construction parameters, tables of dietary needs, and food-production rates to obtain estimates of system self-sufficiency. The model uses the methods of system dynamics and the DYNAMO simulation language.
Howard, Richard A.; Wessler, Eliot J.
doi: 10.1177/003754977702800304pmid: N/A
Simulation is used to give students of resources management an opportunity to form and evaluate a water resources plan. The focus of the planning problem is a mythical New England city called Aquar ius. In the first stage of the simulation, students work as a planning group and design a fifty-year comprehensive water plan. The students assume dif ferent roles and report to a director as they would in a real planning agency.In the second stage of the simulation, students use a computer model to simulate the operation of the water resources plan. They can observe the impacts of their plan over a period of years and revise the plan as needed.
doi: 10.1177/003754977702800307pmid: N/A
Using elementary statistical theory, we discuss sta tistical techniques that can be used to initialize a simulation run and to determine its length, distin guishing between terminating and nonterminating sys tems and between stationary and nonstationary time series. Confidence intervals and hypothesis tests are included (see Section 2). In the case of k sys tem variants (at least 2), multiple comparison proce dures are presented which can be used to obtain simultaneously valid confidence intervals and to select a subset containing the best population, assuming a fixed number of simulation runs. Other wise ranking procedures can be used to determine the number of runs required to select the best system (Section 3). If many parameters and variables exist, selecting a limited number of combinations requires an experimental design, which must be analyzed with a regression metamodel (Section 4). The metamodel of main effects and interactions applies to both quantitative and qualitative factors; its adequacy can be tested (Subsection 4.1). Experimental design is discussed in three steps: (1) screening designs for finding the important factors, namely, 2k-Pdesigns, random, supersaturated, and group-screening designs (Subsection 4.2); (2) augmented 2K-P designs for further exploration (Subsection 4.3); (3) response surface methodology for optimization (Subsection 4.4). Three practical variance reduction techniques are summarized, namely, control variates, antithetics, and common random numbers (Section 5). Some of the statistical methods discussed are also applicable to deterministic models of continuous systems. This paper was written for readers with only an elementary background in statistics.
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