Monte Carlo simulation on microcomputers

Uyeno, Dean

doi:10.1177/003754979205800611

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Hall und Mcnz, 1985), Taguchi methods kg. Clemson ct al., 1995), or somc system of random sampling or 'Monte Carlo' analysis (e.g, Uyeno, 1992;Clemson ct ul., 1995).…”

Section: Processlng Of Sensitivlty Unulysis Resultsmentioning

confidence: 99%

Sensitivity analysis of normative economic models: theoretical framework and practical strategies

Pannell¹

1997

Agricultural Economics

300

160

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“…Hall und Mcnz, 1985), Taguchi methods kg. Clemson ct al., 1995), or somc system of random sampling or 'Monte Carlo' analysis (e.g, Uyeno, 1992;Clemson ct ul., 1995).…”

Section: Processlng Of Sensitivlty Unulysis Resultsmentioning

confidence: 99%

Sensitivity analysis of normative economic models: theoretical framework and practical strategies

Pannell¹

1997

Agricultural Economics

300

160

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“…Clemson et al, 1995), or some system of random sampling or 'Monte Carlo' analysis (e.g. Uyeno, 1992;Clemson et al, 1995).…”

Section: Experimental Designmentioning

confidence: 99%

Sensitivity analysis of normative economic models: theoretical framework and practical strategies

Pannell

1997

Agricultural Economics

102

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The parameter values and assumptions of any economic model are subject to change and error. Sensitivity analysis (SA), broadly defined, is the investigation of these potential changes and errors and their impacts on conclusions to be drawn from the model. There is a very large literature on procedures and techniques for SA, but it includes almost nothing from economists. This paper is a selective review and overview of theoretical and methodological issues in SA. There are many possible uses of SA, described here within the categories of decision support, communication, increased understanding or quantification of the system, and model development. The paper focuses somewhat on decision support. It is argued that even the simplest approaches to SA can be theoretically respectable in decision support if they are applied and interpreted in a way consistent with Bayesian decision theory. This is not to say that SA results should be formally subjected to a Bayesian decision analysis, but that an understanding of Bayesian probability revision will help the modeller plan and interpret an SA. Many different approaches to SA are described, varying in the experimental design used and in the way results are processed. Possible overall strategies for conducting SA are suggested. It is proposed that when using SA for decision support, it can be very helpful to attempt to identify which of the following forms of recommendation is most appropriate: (a) do, V, (b) do either X or Y depending on the circumstances, (c) do either X or Y, whichever you like, (d) if in doubt, do X. A system for reporting and discussing SA results is recommended.

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“…A small number of computer simulations have appeared recently in the literature but these have been in specialist publications [7][8][9]. In addition they have relied not only on the use of a personal computer and a proprietary spreadsheet package but also on the use of specialized add-in software such as At Risk or Crystal Ball that works with a spreadsheet to provide specialized simulation facilities.…”

Section: Figure 1 Risk Analysis Techniquesmentioning

confidence: 99%

Incorporating Risk into Capital Budgeting Decisions Using Simulation

Smith

1994

Management Decision

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Monte Carlo simulation is one of a number of analytical techniques available to enable managers to assess the risks surrounding capital budgeting decisions. Although it is covered in most texts on financial management, surveys have for many years pointed to only limited use of the technique by practising managers making decisions about investment projects. Describes and analyses a simulation model utilizing facilities available to most managers. The model uses a macro to automate the simulation process and generate a frequency distribution of a project′s net present value. The model can be rapidly implemented, uses a large number of individual simulations to provide a reliable frequency distribution and aids managerial decision making by presenting managers with an easily understood perspective on the uncertainty surrounding an investment project.

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Monte Carlo simulation on microcomputers

Cited by 5 publications

References 6 publications

Sensitivity analysis of normative economic models: theoretical framework and practical strategies

Sensitivity analysis of normative economic models: theoretical framework and practical strategies

Sensitivity analysis of normative economic models: theoretical framework and practical strategies

Incorporating Risk into Capital Budgeting Decisions Using Simulation

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