The goal of Kangas, Kangas and Kurttila's Decision Support for Forest Management is to provide students and researchers with a toolbox of methods for approaching the different planning situations that may arise in practice. It draws together a wide range of methods used in planning forest management regimes and presents a systematic overview of current methodological approaches.
While earlier books concerning forest planning have tended to focus on linear programming, economic aspects, or specific multi-criteria decision aid tools, this book provides a much broader range of tools to meet a variety of planning situations. The methods themselves cover a range of decision situations - from cases involving single decision makers, through group decision making, to participatory planning. They include traditional decision support tools, from optimization to utility functions, as well as methods that are just gaining ground in forest planning - such as problem structuring methods and social choice theory.
Including examples which illustrate the application of each technique to specific management planning problems, the book offers an invaluable resource for both researchers and advanced students specializing in management and planning issues relating to forestry.
Rezensionen / Stimmen
From the reviews:"It introduces many numerical techniques, and a fairly advanced level of knowledge . . If today's forest managers are to successfully manage the multiple values in a forest, they need to be aware of the decision support techniques that are available to them, and this book will certainly provide that information. Forestry students also need to know about these techniques if they wish to become successful forest managers . . It can therefore be recommended to all those involved in forest management." (John Innes, International Forestry Review, Vol. 10 (4), 2008)
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ISBN-13
978-1-4020-6787-7 (9781402067877)
DOI
10.1007/978-1-4020-6787-7
Schweitzer Klassifikation
Discrete Problems.- Unidimensional Problems.- Multi-Criteria Decision Problems.- Uncertainty in Multi-Criteria Decision Making.- Continuous Problems.- Optimization.- Heuristic Optimization.- Cases with Several Decision Makers.- Group Decision Making and Participatory Planning.- Voting Methods.- Application Viewpoints.- Behavioural Aspects.- Practical Examples of Using MCDS Methods.- Final Remarks.
