Model Based Decision Support Methodology With Environmental Applications

The complexity of issues requiring rational decision making grows and thus such decisions are becoming more and more difficult, despite advances in methodology and tools for decision support and in other areas of research. Globalization, interlinks between environmental, industrial, social and political issues, and rapid speed of change all contribute to the increase of this complexity. Specialized knowledge about decision-making processes and their support is increasing, but a large spectrum of approaches presented in the literature is typically illustrated only by simple examples. Moreover, the integration of model-based decision support methodologies and tools with specialized model-based knowledge developed for handling real problems in environmental, engineering, industrial, economical, social and political activities is often not satisfactory. Therefore, there is a need to present the state of art of methodology and tools for development of model-based decision support systems, and illustrate this state by applications to various complex real-world decision problems. The monograph reports many years of experience of many researchers, who have not only contributed to the developments in operations research but also succeeded to integrate knowledge and craft of various disciplines into several modern decision support systems which have been applied to actual complex decision-making processes in various fields of policy making. The experience presented in this book will be of value to researchers and practitioners in various fields. The issues discussed in this book gain in importance with the development of the new era of the information society, where information, knowledge, and ways of processing them become a decisive part of human activities. The examples presented in this book illustrate how how various methods and tools of model-based decision support can actually be used for helping modern decision makers that face complex problems. Overview of the contents: The first part of this three-part book presents the methodological background and characteristics of modern decision-making environment, and the value of model-based decision support thus addressing current challenges of decision support. It also provides the methodology of building and analyzing mathematical models that represent underlying physical and economic processes, and that are useful for modern decision makers at various stages of decision making. These methods support not only the analysis of Pareto-efficient solutions that correspond best to decision maker preferences but also allow the use of other modeling concepts like soft constraints, soft simulation, or inverse simulation. The second part describes various types of tools that are used for the development of decision support systems. These include tools for modeling, simulation, optimization, tools supporting choice and user interfaces. The described tools are both standard, commercially available, and nonstandard, public domain or shareware software, which are robust enough to be used also for complex applications. All four environmental applications (regional water quality management, land use planning, cost-effective policies aimed at improving the European air quality, energy planning with environmental implications) presented in the third part of the book rely on many years of cooperation between the authors of the book with several IIASA's projects, and with many researchers from the wide IIASA network of collaborating institutions. All these applications are characterized by an intensive use of model-based decision support. Finally, the appendix contains a short description of some of the tools described in the book that are available from IIASA, free of charge, for research and educational purposes. The experiences reported in this book indicate that the development of DSSs for strategic environmental decision making should be a joint effort involving experts in the subject area, modelers, and decision support experts. For the other experiences discussed in this book, the authors stress the importance of good data bases, and good libraries of tools. One of the most important requirements is a modular structure of a DSS that enhances the reusability of system modules. In such modular structures, user interfaces play an important role. The book shows how modern achievements in mathematical programming and computer sciences may be exploited for supporting decision making, especially about strategic environmental problems. It presents the methodological background of various methods for model-based decision support and reviews methods and tools for model development and analysis. The methods and tools are amply illustrated with extensive applications. Audience: This book will be of interest to researchers and practitioners in the fields of model development and analysis, model-based decision analysis and support, (particularly in the environment, economics, agriculture, engineering, and negotiations areas) and mathematical programming. For understanding of some parts of the text a background in mathematics and operational research is required but several chapters of the book will be of value also for readers without such a background. The monograph is also suitable for use as a text book for courses on advanced (Master and Ph.D.) levels for programs on Operations Research, decision analysis, decision support and various environmental studies (depending on the program different parts of the book may be emphasized).

Here is the first systematic handbook treatment of quantitative modeling natural resource problems, their allocated efficient use, and societal and economic impact. Andrés Weintraub is the very top person in Natural Resource research. He has selected co-editors who are at the top of the sub-fields in natural resources: agriculture, fisheries, forestry, and mining. The book covers these areas with contributions from researchers on, among others, modeling natural research problems, quantifying data, and developing algorithms.

Environmental Systems is a component of Encyclopedia of Environmental and Ecological Sciences, Engineering and Technology Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. Environmental Systems is something about data handling, modeling and decision making in the field of environmental systems. It includes related basic knowledge on measurement techniques, modeling techniques and models and their applications for decisions making. Environmental engineering / research are based on measurement techniques and related knowledge of natural and life sciences. Developed mathematical and numerical simulation models are tools and strictly purpose oriented, that means suitable for decision making. The three volumes on Environmental Systems are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs.

The objective of this project was to develop a system for environmental information management and decision support for transportation plans, programs, projects, operations, and maintenance activities. This document contains the following appendixes to the implementation handbook: (B) Survey Results; (C) Use Cases; (D) Best Practices; (E) Laws and Regulations; and (F) Definitions of Entities.

This book constitutes the refereed proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2008, held in Sabadell, Spain, in October 2008. The 19 revised full papers presented together with 2 invited lectures were thoroughly reviewed and selected from 43 submissions; they are devoted to theory and tools for modeling decisions, as well as applications that encompass decision making processes and information fusion techniques. The papers are organized in topical sections on aggregation operators, decision making, clustering and similarity, computational intelligence and optimization, as well as data privacy.

This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book.

Methods used for regional development analysis are employed mainly to make forecasts and comparisons. Forecasting models of various types (e.g. econometric models) are usually used for forecasting. Recently, vector-autoregressive models (VAR) have become popular. These models were proposed by Sims in 1980. On the contrary, taxonomic methods (that are in the center of attention as far as the present publication is concerned) are most often employed to make comparisons. Linear ordering methods, including standard methods, are the most popular among ta- nomic methods. They are based on different distance and similarity measures, which leads to the fact that they do not always provide reliable information. When, for example, one construes the standard for a base year and then compares it with data for other years, it may turn out that the measure determined will have worse values than the standard for a real object (region, micro region) although this object is better from the standard. Hence, one must look for new methods employed in regional development analysis or improve hitherto existing ones in such a way so that information obtained re?ects the reality to a larger extent. The main aim of the present publication is to work out methodological basis for regional development analysis based on vector calculus together with assumptions about computer system supporting the implementation of the method suggested.

The complex and multidisciplinary nature of environmental problems requires that they are dealt with in an integrated manner. Modeling and software have become key instruments used to promote sustainability and improve environmental decision processes, especially through systematic integration of various knowledge and data and their ability to foster learning and help make predictions. This book presents the current state-of-the-art in environmental modeling and software and identifies the future challenges in the field. State-of-the-art in environmental modeling and software theory and practice for integrated assessment and management serves as a starting point for researchers Identifies the areas of research and practice required for advancing the requisite knowledge base and tools, and their wider usage Best practices of environmental modeling enables the reader to select appropriate software and gives the reader tools to integrate natural system dynamics with human dimensions