Complex decision making processes: their modelling and support (original) (raw)

Supporting Decision Makers in Better Dealing with Interrelaetaed Decissions

Every decision has a different level of influence or impact in the human life. Very often, numerous smaller decisions have to be made before a complex decision can eventually arrive at its best conclusion. Moreover, each decision may have a bearing on other subsequent decisions, and thus requires the decision making process to be structured in such a flexible manner that enables the decisions to be considered and solved differently each time. However, most decision making processes and systems are designed to solve simple and linear problems and are therefore unable to support complex problems which consist of interrelated decisions that span across multiple domains, paradigms, and/or perspectives. Furthermore, the true purpose of decision making is to gain a better understanding of the issues involved behind each decision. To address these problems we first proposed conceptual decision-making and modelling processes, and then developed and implemented a flexible object-oriented decision system framework, architecture, and prototype to support these proposed processes. Through the implementation, we were able to explore and implement some general modelling ideas as well as specific issues such as the integration of models and scenarios of different types, levels of complexity, depths of integrations, and decision maker orientations.

A method for integrating multiple components in a decision support system

Computers and Electronics in Agriculture, 2005

We present a flexible, extensible method for integrating multiple tools into a single large decision support system (DSS) using a forest ecosystem management DSS (NED-2) as an example. In our approach, a rich ontology for the target domain is developed and implemented in the internal data model for the DSS. Semi-autonomous agents control external components and communicate using a blackboard. We illustrate how this multi-agent approach with its blackboard architecture supports the expansion of a DSS (in this case, NED-2) to incorporate new models and decision support tools as they become available. The exemplar NED-2 DSS developed using this method is a goal-driven DSS that integrates a sophisticated inventory system, treatment plan development, growth-and-yield models, wildlife models, fire risk models, knowledge based systems for goal satisfaction analysis, and a powerful report generation system.

Model-Based Decision Support Systems - Conceptualization and General Architecture

2020

The paper presents an attempt to conceptualize decision support and various generic subtasks, to develop a general architecture of intelligent decision support systems, and to exploit previous work on process-oriented diagnosis within this architecture. The primary subtasks whose (intelligent) solution is heavily dependent on domain knowledge are situation assessment, i.e. inferring what is happening in a system from a set of observations, and therapy proposal, i.e. developing plans for interventions to achieve certain goals starting from the current situation. Both tasks can be solved by an extension of consistency-based diagnosis to process-oriented models.

OO Design & Implementation of a Flexible Software Architecture for Decision Support Systems

Proc. of the Int. Conf. on Software …, 1997

Many implementations of decision support systems suffer from a lack of flexibility, that is, they are built for a specific application domain. For different application domains, large portions of the particular decision support system have to be reimplemented from scratch. As object-orientation allows the construction of flexible software architectures, this paradigm was applied in the realm of building decision support systems. The paper represents an experience report, which first outlines the conventional implementation of a decision support system and the problems that were encountered when the system was adapted to different application domains. The paper goes on to discuss the concepts of object-oriented components and frameworks and how these concepts were applied in particular in the construction of an objectoriented decision support system that deserves the attribute generic.

Modeling by construction: a new methodology for constructing models for decision support

Proceedings of HICSS-29: 29th Hawaii International Conference on System Sciences, 1996

When to compare the fields of model building and m d e l solving / analyzing, it is the lmer that is more widely researched and understood. ThB is particularly true in respect of the construction of solving-paradigm-free representations (i.e. created not for a special solving method like e.g. linear programming) such as are needed in the case of general models for decision support, to function as problem descriptions. The process of formulating such modeh is supported badly in a conceptual and in a technical way. In this paper we focus on the conceptual way. Model building ist viewed as a creative act, which needs constructive achievement, and which is not only a mapping of reality. We provide a framework for a step-by-step conception and formulation of general models for decision support in an easyto-use and problemdriven manner. Modeling by construction is based on a theoretical 'concept of independence' which definitwn B directly derivedfrom element types of a problem. It b shown that a distinction between semantic and conceptual modeling is arguable on this basis. This concept of independence is the foundation of the definition of eight modeling levels which can function as components of the modeling process. We shall discuss these levekr in detail. We supply an example which uses a graphical modeling language carefully suited to the levek;. Some implications for the transformation operations of the graphical model are considered. We view our methodology not as a replacement of GEOFFNON'S structured modeling, but as a complementary approach to the difficult task of general decision model construction.

A Decision Support Tool for Decision Makers

2005

Many decisions in a multidisciplinary and interdisciplinary process, as an intelligent activity, are too complex for decision makers to make choices based solely on instinct. The complexity stems from this fact that such activities cannot be defi ned precisely. Also satisfying goals depend on achievement of several interrelated tasks which cannot be solved simultaneously. Our decision support tool uses a mathematical application so called Analytical Hierarchy Process as a decision making aid. The developed tool provides a powerful and fl exible mean for tackling the complex decision process into a simple concept of hierarchy, which incorporates factors infl uencing the decision alternatives in a systematic way.

THE EVOLVING ROLES OF MODELS IN DECISION SUPPORT SYSTEMS

Decision Sciences, 1980

An important aspect of decision support systems (DSS) is their utilization of computational models. To illustrate the evolving roles of models in decision support systems, several representative systems are reviewed. These illustrations reveal three major interfaces that must be considered by a decision support system's designer. Successful treatment of these DSS interfaces depends upon two types of languages: one for directing computations and one for directing data manipulation. Each language type is presented as forming a spectrum of languages. Combined, the two spectra provide a classification scheme for decision support systems. This scheme is both useful as a framework for comparative study of decision support systems and also suggestive of directions for future research and developments in the decision support field.

AN ONTOLOGY-DRIVEN FRAMEWORK FOR SUPPORTING COMPLEX DECISION PROCESS

The study proposes a framework of ONTOlogy-based Group Decision Support System (ONTOGDSS) for decision process which exhibits the complex structure of decision-problem and decision-group. It is capable of reducing the complexity of problem structure and group relations. The system allows decision makers to participate in group decision-making through the web environment, via the ontology relation. It facilitates the management of decision process as a whole, from criteria generation, alternative evaluation, and opinion interaction to decision aggregation. The embedded ontology structure in ONTOGDSS provides the important formal description features to facilitate decision analysis and verification. It examines the software architecture, the selection methods, the decision path, etc. Finally, the ontology application of this system is illustrated with specific real case to demonstrate its potentials towards decision-making development.

An object-oriented decision support system for MCDM

This paper deals with the basic ideas and implementation of an object-oriented decision support system (DSS), especially for multiple criteria decision making (MCDM). There are various ways of integrating different MCDM methods in a DSS. One approach is the interactive application of different methods by means of an integrative user interface. Integration can also be achieved by utilizing neural networks and similar concepts. Neural network-based DSS are able to combine different methods and to learn from historical decision making. They are able to answer questions like these: What method should be used? How can the parameters be adjusted? Is it useful to apply different methods and to aggregate their results to a compromise solution? And how should this be done? In the object-oriented implementation concept decision making problems and methods like neural networks and MCDM methods are represented by objects. A class hierarchy of different problems and methods is presented. Each problem can be linked with a method which can solve it. A method can be linked with another method (meta method) which provides a learning strategy. Learning strategies work by applying the methods on some input problems and by comparing the calculated results with some reference results. The relationship between problems, methods, and learning methods is illustrated by an example.