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

Reasoning by Structural Analogy Taking into Account the Context for Intelligent Decision Support Systems

Development of methods and tools for modeling human reasoning (common sense reasoning) by analogy in intelligent decision support systems is considered. Special attention is drawn to modeling reasoning by structural analogy taking the context into account. The possibility of estimating the obtained analogies taking into account the context is studied. This work was supported by RFBR

Locating fire-stations: an integrated approach for Belgium

This paper demonstrates the potential of a decision-support system developed for Belgium by a consortium of universities and a private firm, in the framework of a public call by the Ministry of the Interior. The system is designed to provide the Belgian emergency management administration with a complete decision-aid tool for the location of fire-stations. The originality of the project is that it includes a risk-modeling approach developed at a national scale. This analysis involves a multiscale GIS system which includes a thorough representation of the physical, human and economic spatial realities, a risk modeling approach, an adequate optimal location and allocation model (taking into account both queuing and staffing problems). The final result is an interactive operational tool for defining locations, equipment allocations, staffing, response times, the cost/efficiency trade-off, etc. which can be used in an assessment as well as a prospective context. It has numerous functionalities including rapid modification of the modeling conditions to allow for quick scenario analysis, multiscale analysis, and prospective analysis.ocation-allocations, GIS, fire-stations, Belgium

INDIVIDUAL NEGOTIATION SUPPORT IN GROUP DSS

Negotiation support is an important aspect of multiperson decision support systems. Besides mechanisms for representing and evolving group joint problem representations, such DSS should also provide an environment in which decision makers are supported in developing, analyzing and reinforcing their individual negotiation position. Recognizing the diversity of research approaches to negotiation modeling in the literature, this paper synthesizes an integrated model from which a knowledge-based individual negotiation support environment using tools from different areas can be designed. Role and architecture of such a component are described in the context of MEDIATOR, a database-centered negotiation support system under development at NYU.Information Systems Working Papers Serie

GEOGRAPHICAL INFORMATIONAL SYSTEMS - APPLICABILITY IN INVESTMENTS AND BANKING

Supported by information systems, the regional or world economy transformed itself in a global economy. Taking fast and optimal decisions is needed for each step an individual or an organization makes in order to support competitiveness in the context of radical evolution of the world economy. When dealing with fundamental decisions, spatial information plays an important role because it allows integration of data from various domains, having multiple connections, as well as their visualization and analysis in a useful, suggestive and efficient manner. Decision support systems (DSS) are spatially based computer applications and data which assists managers in making decisions. Since Geographical Informational Systems technology has a great deal to offer to the mortgage finance industry the goal of this paper is to analyze the main theoretical and practical approaches regarding the role of the geographic location and spatial relationships over the outcomes of the banking, housing and mortgage market.Investment, Banking, GIS Modeling, Decision Making System, Spatial Analysis

Housing and Community Development

Researchers in housing and community development design and evaluate policies regarding access to attractive, affordable and sustainable housing and improving the social, physical and economic infrastructure of communities, especially those in the urban core. Practitioners in this field confront political considerations, administrative guidelines and limited funding. Decision science can increase the efficiency and effectiveness of market-rate housing development and provide support for policy responses to issues such as affordable housing, race and class segregation, ineffective and/or inequitable economic development, and sustainable development. This research spans many disciplines, including systems modeling, urban economics, multi-criteria decision modeling, stochastic models and decision support systems, and is often interdisciplinary and applied in nature. A common thread in this work is the need to explicitly address the needs of multiple stakeholders, to capture the public and private nature of housing, and to incorporate best-available evidence regarding markets, policies and impacts of housing and community development. We describe the policy context for this work, review previous research through the lens of descriptive, prescriptive and decision support models, and identify important limitations to work in this area to date. We then describe diverse opportunities for research in this area that can address current policy concerns such as sustainable development, post-disaster reconstruction and individual and group decision support

Best practices in prediction for decision-making : lessons from the atmospheric and earth sciences

Predictions that result from scientific research hold great appeal for decision-makers who are grappling with complex and controversial environmental issues, by promising to enhance their ability to determine a need for and outcomes of alternative decisions. A problem exists in that decision-makers and scientists in the public and private sectors solicit, produce, and use such predictions with little understanding of their accuracy or utility, and often without systematic evaluation or mechanisms of accountability. In order to contribute to a more effective role for ecological science in support of decision-making, this paper discusses three ``best practices'' for quantitative ecosystem modeling and prediction gleaned from research on modeling, prediction, and decision-making in the atmospheric and earth sciences. The lessons are distilled from a series of case studies and placed into the specific context of examples from ecological science

Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS): A conceptual framework

In recent years there has been increasing interest in applying the computer based problem solving techniques of Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS) to analyze extremely complex problems. A conceptual framework is developed for successfully integrating these three techniques. First, the fields of AI, OR, and DSS are defined and the relationships among the three fields are explored. Next, a comprehensive adaptive design methodology for AI and OR modeling within the context of a DSS is described. These observations are made: (1) the solution of extremely complex knowledge problems with ill-defined, changing requirements can benefit greatly from the use of the adaptive design process, (2) the field of DSS provides the focus on the decision making process essential for tailoring solutions to these complex problems, (3) the characteristics of AI, OR, and DSS tools appears to be converging rapidly, and (4) there is a growing need for an interdisciplinary AI/OR/DSS education

Web Service Retrieval by Structured Models

Much of the information available on theWorldWideWeb cannot effectively be found by the help of search engines because the information is dynamically generated on a user’s request.This applies to online decision support services as well as Deep Web information. We present in this paper a retrieval system that uses a variant of structured modeling to describe such information services, and similarity of models for retrieval. The computational complexity of the similarity problem is discussed, and graph algorithms for retrieval on repositories of service descriptions are introduced. We show how bounds for combinatorial optimization problems can provide filter algorithms in a retrieval context. We report about an evaluation of the retrieval system in a classroom experiment and give computational results on a benchmark library.Economics ;