M. Makowski

Model-Based Decision Support Methodology with Environmental Applications

This is the first book to develop a decision support methodology for strategic environmental decision problems, and provides several generic as well as specific tools. The book is divided into three parts plus an Appendix. Part I introduces the methodological background and describes various features of the decision environment and the ways in which model-based decision support can help the decision making process. 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, is presented. These methods support not only the analysis of compromise 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. Part II describes various types of tools that are used for the development of decision support systems. Several of these tools described in the Appendix are available from IIASAs Web site, free of charge, for use in research and teaching. The last part of the book consists of four chapters on applications. Each chapter treats an area of environmental decision making: water quality management in river basins, land use planning, cost effective policies for improving air quality, and energy planning. For each area decision support systems are presented and it is shown how they are used for supporting decision making and negotiations. The applications as well as the methodology presented in this book have been developed at IIASA in close cooperation with several other institutes and organizations.

Multiple criteria land use analysis

Since the early 1980s, the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) have been collaborating on expanding FAOs Agro-Ecological Zones (AEZ) Methodology of land resources appraisal by incorporating decision support tools for optimizing the use of land resources. Initially these tools consisted in the application of linear optimization techniques for analyzing land-use scenarios with regard to single objective functions, such as maximizing agricultural production or minimizing the cost of production under specific physical environmental and socio-economic conditions and constraints. Often the specification of a single objective function does not adequately reflect the preferences of decision-makers, which are of a multi-objective nature in many practical problems dealing with resources. Multi-objective optimization approaches address problem definitions and solutions in a more realistic way and have recently been applied by FAO and IIASA in a land resources appraisal study in Kenya. In this study, multi-objective optimization coupled with multi-criteria decision analysis (MCDA) techniques, using the Aspiration Reservation Based Decision Support (ARBDS) approach, have been used to analyze various land use scenarios, considering simultaneously several objectives such as maximizing revenues from crop and livestock production, maximizing district self-reliance in agricultural production, minimizing costs of production and environmental damages from erosion. The main users of the new tool being developed, which combines AEZ and MCDA, are expected to be natural resources analysts and managers, land-use planners, ecologists, environmentalists, economists at national and regional levels, and agricultural extensionists at the local scale.

Interactive specification and analysis of aspiration-based preferences

Model based Decision Support Systems (DSS) often use multi-criteria optimization for selecting Pareto-optimal solutions. Such a selection is based on interactive specification of user preferences. This can be done by specification of aspiration and reservation levels for criteria. Diverse graphical user interface could be used for the specification of these levels as well as for the interpretation of results. In the approach presented in this paper the specified aspiration and reservation levels are used for generation of component achievement functions for corresponding criteria, which reflect the degree of satisfaction with given values of criteria. The paper outlines the methodological background and modular structure of a tool (called NCMA) for multi-criteria analysis of decision problems that can be represented as linear programming (LP) or mixed integer programming (MIP) problems. The MCMA has been used at IIASA for the analysis of decision problems in water quality management and land use for sustainable development planning. These experiences have shown that the MCMA tool is applicable also to a large LP and MIP problems. Other implementations of the same methodology have also been used for the analysis of non-linear problems in several engineering applications. specification of user preferences is described. The presented methodology of multi-criteria model analysis and the documented software is illustrated by a detailed tutorial example.

A structured modeling technology

This paper presents the methodological background and implementation of a structured modeling environment developed to meet the requirements of modeling activities undertaken to support intergovernmental negotiations aimed at improving European air quality. Although the motivation for the reported work came from the actual complex application presented in the paper, the actual scope of the paper covers a wide range of issues related to model-based decision-making support. The paper starts with a summary of the context of modeling composed of: the role of models in decision-making support; modeling paradigms; and state-of-the-art aspects of modeling complex problems. The modeling process is then characterized, and the requirement analysis for implementation of structured modeling is specified. The main part of the paper presents the structured modeling technology which was developed to support the implementation of the structured modeling principles for modeling complex problems.

Decision support versus knowledge creation support

Creative Space summarizes and integrates the various up-to-date approaches of computational intelligence to knowledge and technology creation including the specific novel feature of utilizing the creative abilities of the human mind, such as tacit knowledge, emotions and instincts, and intuition. It analyzes several important approaches of this new paradigm such as the Shinayakana Systems Approach, the organizational knowledge creation theory, in particular SECI Spiral, and the Rational Theory of Intuition resulting in the concept of Creative Space. This monograph presents and analyzes in detail this new concept together with its ontology the list and meanings of the analyzed nodes of this space and of the character of transitions linking these nodes.

Modeling paradigms applied to the analysis of European air quality

The paper presents an overview of various modeling paradigms applicable to the analysis of complex decision-making problems that can be represented by large non-linear models. Such paradigms are illustrated by their application to the analysis of a model that helps to identify and analyze various cost-effective policy options aimed at improving European air quality. Also presented is the application of this model to support intergovernmental negotiations.

Modeling Knowledge: Model-Based Decision Support and Soft Computations

This chapter provides an overview of model-based support for modern decision making. It starts with discussing basic elements of decision making process, including characteristics of complex decision problems, concepts of rationality, and various requirements for model-based support at different stages of decision making process. Then the characteristics of models, and of modeling processes aimed at decision-making support for complex problems are presented. In this part guidelines for model specification and instantiation are illustrated by an actual complex model. This is followed by a discussion of modern methods of model analysis, which include a more detailed discussion of reference point optimization methods, and an outline of methods for sensitivity analysis, and of softly constrained inverse simulation. Finally, an overview of architecture of model-based decision support system is presented.

The role of multi-objective optimization in negotiation and mediation support

The paper reviews the methodology of multi-objective modeling and optimization used in decision support based on computerized analytical models (as opposed to logical models used in expert systems) that represent expert knowledge in a given field. The essential aspects of this methodology relate to its flexibility: modeling and optimization methods are treated not as goals in themselves but as tools that help a sovereign user (an analyst or a decision maker) to interact with the model, to generate and analyze various decision options, to learn about possible outcomes of these decisions. Although the applications of such methods in negotiation and mediation support is scarce yet, their flexibility increases essentially the chances of such applications. Various aspects of negotiation and mediation methods related to multi-objective optimization and game theory are also reviewed. A possible application of the MCBARG system for supporting negotiation related to the acid rain problem is briefly summarized.

A simplified ozone model based on fuzzy rules generation

Abstract In this paper, simplified ozone models for potential use in integrated assessment are developed from the EMEP ozone model, which is a single-layer Lagrangian trajectory model. The simplification method uses fuzzy rule generation methodology to represent numerous results of the EMEP model as a response surface describing the source–receptor relationships between ozone precursor emissions and daily tropospheric ozone concentrations.

Management of Attainable Tradeoffs between Conflicting Goals

Rational decision-making requires governance of attainable trade-offs between conflicting goals, uncertainties and risks, which in turn demands both novel modeling methods and appropriate modeling technology. The paper deals with recent developments in applied modeling that have been motivated by the requirements for model-based support of solving complex problems. It starts with presenting novel modeling technology and integrated methods of integrated model analysis aimed at supporting decisionmakers in diversified ways of analysis of the underlying decision problem. Then, multicriteria analysis is discussed in more detail with a focus on an extension of the reference point optimization, which supports an effective analysis of trade-offs between conflicting criteria aiming at analysis of attainable goals. Next, new approaches to coping with endogenous uncertainty and catastrophic risks are characterized, followed by a summary of issues related to transparency and public understanding.