An Intelligent Decision Support Model for Actuarial Profession

It is well known that security analysis is a time-consuming and error-prone process. However, it can be improved or enhanced considerably by automated reasoning. Efforts to reduce the inaccuracy and incorrectness of analyses and to enhance the confidence levels of stock selection have led to the development of an intelligent decision support system called TradeExpert, which assists, not replaces, portfolio managers. TradeExpert assumes the role of a hypothetical securities analyst capable of analyzing stocks, calling market turns, and making recommendations. It has a knowledge base of stock trading expertise, and a case base of past episodes and consequences of decisions. By combining knowledge-based problem solving with case-based reasoning and fuzzy inference, TradeExpert demonstrates forms of intelligent behavior not yet observed in traditional decision support systems and expert systems. The novelty of this research lies in its application to analogical reasoning, fuzzy reasoning, and knowledge-based decision making.

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

Background The authors study the development of decision support system (DSS) for designing the bottom-hole assembly (BHA) while drilling, as well as the development of software that implements the DSS. DSS includes expert system, knowledge base, analytics and modeling subsystem, database of drilling equipment. The analytics and modeling subsystems contain algorithms for drilling calculation of the optimal BHA parameters in accordance with the requirements for the well profile, rate of penetration and reduction of sticking danger. The authors discuss how to use the developed expert system as part of an automated control system for directional well drilling. The studied drilling equipment consists of: drill bits, calibrators, downhole screw motors, drilling jar, heavy-weight drill pipes and heavy-wall drill pipes. Analyzed the main parameters of drilling equipment affect sticking danger, rate of penetration and well trajectory. The developed database structure of drilling equipment allows you to calculate the above parameters. The result of the work of the decision-making software is a report defining the types and sequence of installation of the necessary downhole drilling equipment. Aims and Objectives For DSS development for the BHA designing while drilling is necessary to develop an expert system, a knowledge base, analysis and modeling subsystem, a database of drilling equipment to implement this DSS as a part of an automated control system for directional well drilling. Results The authors developed an ontological model for describing and choosing effective BHA design by the Protege software based on evaluation and analysis of the main attributes of BHA selection experience. The authors also developed the drilling equipment database structure for BHA equipment selection based on deductive inference mechanism for decision rules of the knowledge base.

Expert systems (ES) are computer programs which make recommendations and draw conclusions from heuristic rules and from relationships derived out of human experience. A decision support system (DSS) is a computer-based management information system (MIS) linking one or more data bases to computer software for exploration and analysis of the data. Combined in a single system an ES and a DSS comprise an expert support system. A simple expert support system for use in fishery stock assessment and management is described. It demonstrates a number of practical benefits of the use of expert systems technology to implement and integrate some simple tasks, i.e., bioeconomic modelling, cost/benefit sampling analyses, and technical interpretation, namely: (1) the effects of different population structures and patterns of fishing on modelling results can be examined under the programmed guidance of the expert system; (2) the effects of different levels of sampling precision on modelling results can be similarly examined; (3) cost/benefit analyses of different sampling budgets can be examined; and (4) management advice specific to each sampling, surplus production (four different models), and net present value (NPV) case is given. An examination of the future of expert support systems in fishery stock assessment and management considers four possible levels of support, as follows: (1) to provide technical explanations, (2) to manage and catalog repetitive processes, (3) to guide “low-level decisions”, and (4) to contribute directly to strategic and policy decisions. Currently, movement in expert systems usage is toward so-called “embedded systems”, for example, an expert support system which comprises, in addition to computational models and databases, one or more expert systems. Problems related to the design and development of ESS for fishery stock assessment and management are challenging conceptual problems; the construction and programming of ESS are reasonably tractable tasks. Determining what an ESS will do is often the principle conceptual question. Expert support systems provide another tool for fishery stock assessment scientists and managers to increase their productivity.

Routine blood tests drive diagnosis, prognosis, and monitoring in traditional clinical decision support systems. As a routine diagnostic tool with standardized laboratory workflows, clinical blood analysis offers superior accessibility to a comprehensive assessment of physiological parameters. These parameters can be integrated and automated at scale, allowing for in-depth clinical inference and cost-effectiveness compared to other modalities such as imaging, genetic testing, or histopathology. Herein, we extensively review the analytical value of routine blood tests leveraged by artificial intelligence (AI), using the ICD-10 classification as a reference. A significant gap exists between standard disease-associated features and those selected by machine learning models. This suggests an amount of non-perceived information in traditional decision support systems that AI could leverage with improved performance metrics. Nonetheless, AI-derived support for clinical decisions must still be harmonized regarding external validation studies, regulatory approvals, and clinical deployment strategies. Still, as we discuss, the path is drawn for the future application of scalable artificial intelligence (AI) to enhance, extract, and classify patterns potentially correlated with pathological states with restricted limitations in terms of bias and representativeness.

This chapter elaborates the basic concepts underlying the development of Web-based decision support systems (DSS) with a discussion on the key concepts and technical issues. The utility of Web-based decision support system in enhancing communication and decision-making capability in a distributed environment or a multiple stakeholder process (MSP) has been explained through examples with diverse application from the real world. Further, the chapter introduces a Web-based decision support system developed by the authors for water resources management on basin scale and also some evolving concepts like mobile agent technology to meet the challenges and problems associated with traditional Web-based DSS. The authors hope that better understanding of the key issues and concepts can bring together analysts, modelers, and the end users to build Web-based DSS that are understandable, accessible and acceptable to all, be it corporate or business houses, environmental agencies or government organizations.

As businesses grow across continents in terms of reach, scale, and diversity in products, the complexity of management problems and efforts to maximize the profit in corporations have increased significantly. To assist managers and employees at all levels in decision making, corporate modeling is essential to provide decision support information. Corporate modeling within business organizations has moved from standalone database applications to integrated and flexible enterprise-wide information systems. Computer modeling and planning tools such as decision support systems (DSS) are widely used by corporate management to select alternatives based on key business performance measures. Enterprise resource planning (ERP) systems have also been introduced as a new form of corporate modeling that does the same thing as traditional DSS, but they are not confined to the boundaries of business applications. This article reviews the various types of corporate models along with development and applications of advanced DSS and ERP systems. Keywords: corporate modeling; customer-centric enterprise information systems; decision support systems; enterprise resource planning; evolutionary multiagent systems

The continued growth in the volume of available domain and technical data has been facilitated by a corresponding advancement in and communication technology. This information overload can result in inefficient use of time and resources as well as the creation of recommended courses of action that are overruled by the decision makers' judgment and experience. In order to address these problems, multiple knowledge sources and an inference process capable of mirroring the human thought processes (especially judgment and experience) must be available at the right time to the persons or groups needing the knowledge for decision making. Such a concept can be referred to as MANAGED QUALITATIVE THINKING SUPPORT SYSTEMS (MQTSS). Traditional decision support systems (DSS) rely upon decision maker or staff expertise to render knowledge in support of decision making. If the decision maker or staff has insufficient domain or technical expertise to utilize the DSS's embedded models, interpret results, or implement the recommendations, knowledge delivery may be compromised or rendered ineffective. MQTSS can alleviate these support problems and improve knowledge delivery for decision making by reducing knowledge search times, streamlining decision-making tasks, reducing decision time, and promoting appropriate qualitative thinking,. The MQTSS approach theoretically can enhance the decision making process and decision outcomes. This paper attempts to replicate an earlier study by this author to test the theory. First, the MQTSS approach is presented. Next, an system is created to deliver the technology to management. Finally, a simulation experiment is reported that compares the effectiveness of support rendered by a traditional decision support system and the created MQTSS system. The paper closes with conclusions and implications for systems research.

It is shown that, although many aspects of a traditional decision support system (TDSS) and an organizational decision support system (ODSS) are similar, there are important differences that must be understood if the ODSS is to be successful. Differences in purposes lead to differences in design, differences in managing the development effort, differences in implementation, and differences in maintenance. In fact, some aspects of building an ODSS are more similar to building a traditional information system than to building a TDSS. The suggested approach to building an ODSS combines principles and good practice for building both types of systems. The points made are illustrated with examples from the ODSS that are currently being implemented by the US Air Force. The system, called the Enlisted Force Management System, is being used to help members of the Air Staff in the Pentagon make decisions related to their enlisted personnel. >

The concept of Expert Decision Support Systems is based on the integration of Decision Support Systems and Expert Systems, providing the decision maker with features from both types of systems (e.g. simulation, explanations, control of reasoning). Constraint logic programming languages, such as CLP(R) or CHIP, achieve this integration by merging logic programming, a paradigm of Artificial Intelligence and Expert Systems, with constraint solving techniques similar to those used in Operations Research and Decision Support Systems. Their embedded constraint solver extends the expressive power of logic programming languages, most notably on the domain of numerical expressions. The paper illustrates how constraint logic programming may be used in EDSS development by presenting a small classroom scheduling problem developed with CLP(R), and compares this approach to more traditional Operations Research techniques.

An environment for development of decision support systems

This paper aims to introduce the Decision Support System (DSS), which is an area informatics approach to area studies, and the research network complex based on DSS's application in Thailand. In particular, the paper presents and discusses current activities of the DSS research network at the national level in Thailand rather than being a theoretical and analytical study of the DSS mechanism or its cases. DSS has wide applications in Thailand, extending to various fields such as agricultural and natural resource management, economic and social management, historical and cultural preservation, and so on. DSS generally refers to a support system embedded into a computer system for providing intellectual resources with the appropriate numerical model necessary for decision making in production activities. Agricultural and natural resources have been the foundation of social and economic development in Thailand since the country's first national plan in the1960s. Decision making to maintain agricultural productivity as well as to protect natural resources requires well-integrated data sets. The Thailand Research Fund(TRF) established a DSS research and development network (TRF-DSS) in 2002 to help various research teams in Thailand develop DSS tools and components for addressing agricultural and natural resource management issues. Using a "systems"approach, the DSS framework allows researchers and users to identify and integrate key components as well as to define databases and model-base management systems. This paper focuses on the TRF-DSS research and development network, which consists of 12 universities, two line agencies in Thailand, and a line agency in Cambodia. During 2002–10, a total of 59 research projects were granted a budget allocation of 140.1 million baht, and a budget of 15.4 million baht was allocated to support activities of the network. In addition, 10 projects were funded to carry out post-project activities, with a total budget of 1.5 million baht. More than 20 DSS tools were developed and implemented by various users, ranging from policy makers to provincial and local government agencies engaged in short- and long-term planning and management. Most DSS tools were designed to allow the integration of biophysical and socioeconomic data as well as the decision support modules for alternatives evaluation and analysis. These approaches support the choices of dynamic simulation models as well as multi-criteria analyses for modelbase software development. They also allow users to evaluate various alternatives in agricultural and natural resource management. Networking is a powerful platform for DSS research and development and maybe applied to other types of research and development agenda in Thailand, such as area study projects. DSS tools contribute to an understanding of sustainability of production systems against a background of climate change, poverty reduction, and food security.

AMTexpert is a prototype of an expert decision support system (EDSS) for the corporate alternative minimum tax (AMD. The corporate AMT was introduced as part of the Tax Reform Act of 1986 in order to force US corporations to pay more tax. The complicated and interrelated nature of the AMT system has made compliance and planning even more difficult for corporate taxpayers. Given the complexities of the corporate AMT, significant benefits may accrue from the development of an EDSS. In effect, an EDSS combines the judgmental benefits of an expert system (ES) with the computational benefits of a decision support system (DSS). AMTexpert is menu driven with six interdependent phases. The heart of the system is a ‘scoreboard’ used to compare and evaluate alternative tax positions. Context‐sensitive advice and explanations are provided to guide the user. The system was validated and evaluated by two groups of outside experts. AMTexpert demonstrates that an EDSS may be developed from existing tax and systems software.

A prototype of knowledge-based decision support system for construction cost management

Business organisations are increasing their influence and presence throughout the world as domestic economies become oversaturated. Organisations increasingly turn to international commerce to discover new markets. Globalisation requires that firms adapt their management and decision-making practices to recognise localised cultural, political, legal, social, and security requirements. In response, organisations are revamping their decision processes, tailoring them as cultures evolve, political assumptions and realities change, laws are enacted, social issues come to the forefront, and security requirements mature. Traditional decision support systems (DSS) are incapable of readily incorporating localised variables into decision-making processes and must be enhanced. Geographic Information Systems (GIS), have the potential to enhance traditional management decision-making (DSS) tools by adapting them for use in global environments. This paper proposes the melding of GIS and DSS toolsets to create a new framework, hereinafter referred to as Geographically-Enhanced Decision Support Systems (GEDSS), for enhanced management and decision-making for businesses.