Marakas, Decision Support Systems in the 21st Century, (2002) Prentice Hall Chapter 1: Introduction to Decision Support Systems Objectives 1. Understand the definition of a decision support system (DSS) based on three common themes: problem structure, decision outcome, and managerial control 2. Understand the benefits and limitations of DSS use 3. Be familiar with the history of DSSs 4. Grasp the five basic components of a DSS 5. Learn the roles of data and model management systems 6. Learn the functionality of a DSS knowledge base 7. Learn the importance of the user interface in a DSS 8. Learn the user roles and patterns of DSS use in a DSS 9. Gain an understanding of the categories and classes of DSSs that are essential in determining the best approach to designing or implementing a new system Chapter 2: Decisions and Decision Makers Objectives 1. Understand the elements and framework of the decision-making process 2. Be familiar with the classification of decision makers 3. Based on decision makers' cognitive complexity and value orientation, understand the classification of decision styles and the three related factors: problem context, perception, and personal values 4. Understand the interactions between problem context and decision styles in order to design systems that provide appropriate support 5. Comprehend the definition of a good decision and the forces acting upon the decision makers during the decision process 6. Learn the common types of support that can be provided by decision support systems 7. Understand the difficulties of decision making from different angles such as problem structure, cognitive limitations, uncertainty of decision outcomes, and alternatives and multiple objectives 8. Learn the classification of decisions and understand the role of these typologies in the design of decision support systems 9. Understand Simon's model of problem solving 10. Learn the theory of rational decision making 11. Gain an understanding of Simon's "satisficing" strategy and bounded rationality 12. Clarify the difference between a symptom and a problem 13. Become familiar with the process of choice 14. Understand the decision maker's cognitive process and its effects on decision making 15. Learn four of the most common heuristic biases and their impacts on decision making 16. Distinguish between effectiveness and efficiency Chapter 3: Decisions in the Organization Objectives 1. Learn the definition of an organization and the decision-making activities within an organization 2. Be familiar with the five dimensions of organizational decisions: group structure, group roles, group process, group style, and group norm 3. Understand the need for different types of support systems at different organizational decision levels 4. Comprehend the meaning of organizational culture and its influence over decisionmaking activities within an organization 5. Understand the relationship between organizational culture and the organization's ability to change 6. Discern the influence of power and politics on decision-making activities within an organization and comprehend their impact on the design and implementation of DSSs. 7. Be familiar with the functionality of organizational decision support systems (ODSSs) Chapter 4: Modeling Decision Processes Objectives 1. Learn how a fully formed problem statement is developed using three key components: the current state of affairs, the desired state of affairs, and the objective(s) 2. Learn to identify the problem scope 3. Understand the three fundamental components of problem structuring: choice, uncertainty, and objective 4. Be familiar with two decision-modeling tools: influence diagram and decision tree 5. Based on an understanding of the problem structure components and tools, learn the common decision structures and their variations 6. Be familiar with various types of decision models, either abstract or conceptual, which will be the foundations for the decision maker's analysis and subsequent forecasts and prediction 7. Understand the three requirements of probability 8. Explain the different types of probabilities: long-run frequency, subjective probability, and logical probability 9. Use direct probability forecasting, odds forecasting, or comparison forecasting to estimate and forecast probabilities of the identified uncertainties 10. Recognize the techniques of estimating measurable liability (calibration), analyzing sensitivity (sensitivity analysis), and evaluating information costs (value analysis) Chapter 5: Group Decision Support and Groupware Technologies Objectives 1. Understand the concept of multiparticipant decision maker (MDM), the basic MDM structures, and the basic types of communication networks 2. Understand the different types of problems with groups 3. Be familiar with the basic concepts and definitions of MDM support technologies 4. Learn two different classes and types of MDM support technology classifications by features and by technology 5. Be familiar with the six types of groupware 6. Understand the common MDM coordination methods 7. Comprehend the meaning of the virtual workplace Chapter 6: Executive Information Systems Objectives 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. Understand the definition of an executive information system (EIS) Realize the two design requirements of an EIS and its fluidity Understand the drill down capability of an EIS Learn the history of the EIS Be familiar with executive activities and their basic categories Learn the various types of information needed by top executives Be familiar with executive information determination methods Gain an understanding of EIS hardware and software components Become familiar with the categories of the current EIS technologies Comprehend a development framework for the EIS Understand some limitations and pitfalls of the EIS Learn the conditions of transformation in executive decision making Gain an understanding of the potential features of current and future EISs Chapter 7: Expert Systems and Artificial Intelligence Objectives 1. 2. 3. 4. 5. 6. Define expert system and artificial intelligence Describe the several different reasoning processes used by humans Describe the methods available to create a computer-based reasoning system Explain the concepts and structure of expert systems Understand the predesign activities associated with building an expert system Learn how to evaluate an expert system Chapter 8: Knowledge Engineering and Acquisition Objectives 1. Understand the concept of knowledge engineering and how it is distinct from traditional IS development 2. Compare a decision support system with an expert system 3. Provide an overview of the methods and tools used in knowledge engineering today and provide a glimpse of how these will evolve in the future 4. Understand the basis of knowledge and distinguish it from data and information 5. Conceptualize the views of knowledge under three different perspectives: representation, production, and states 6. Comprehend the sources of knowledge and be able to classify different types of knowledge 7. Identify various methods of knowledge acquisition and management Chapter 9: Machines That Can Learn Objectives 1. Understand the types of problems that lend themselves to the application of machine learning systems 2. Understand the basics of how fuzzy logic processing employs set membership and how linguistic ambiguity can be modeled 3. Understand the strengths and limitations of fuzzy logic systems 4. Understand the basic concepts and components of artificial neural networks and their structures 5. Understand the strengths and limitations of neural computing 6. Understand the basic components and functioning of genetic algorithms 7. Be able to determine what type of intelligent system is best suited to different kinds of problems Chapter 10: The Data Warehouse Objectives 1. Explain the goal of the data warehouse and its characteristics 2. Explain the differences between an operational data store, a data mart, and a data warehouse 3. Describe briefly each interconnected element in the data warehouse architecture 4. Describe the role of metadata in the data warehouse 5. Describe the components of the metadata 6. Identify the challenge of implementing a data warehouse 7. Describe the various data warehouse technologies and the future of data warehousing Chapter 11: Data Mining and Data Visualization Objectives 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Understand the concept of data mining (DM) Trace the evolution of decision support activities from verification to discovery Understand the concept of online analytical processing (OLAP) and its rules Learn the two approaches used to conduct multidimensional analysis of datamultidimensional OLAP (MOLAP) and relational OLAP (ROLAP)-and explore the different situations suited for MOLAP and ROLAP architectures Recognize the four major categories of processing algorithms and rule approaches used to mine data: classification, association, sequence, and cluster Assess current data mining technologies including statistical analysis, neural networks, genetic algorithms, fuzzy logic, and decision trees Learn the general process of knowledge discovery through examples Examine market basket analysis procedures Understand the current limitations and challenges to data mining Survey the history of data visualization and how it can help with decision-making activities 11. Consider the typical applications of data visualization techniques 12. Review several current "siftware" technologies 13. Conduct several PolyAnalyst and TextAnalyst exercises using actual data sets Chapter 12: Designing and Building the Data Warehouse Objectives 1. 2. 3. 4. 5. 6. 7. 8. Understand enterprise model approach to building a data warehouse Explore the issues related to defining the project scope Examine the concepts associated with economic justification of the project Review the various analysis tools used to gather system requirements Explain the design of a project plan for construction of a data warehouse Understand the process of economic feasibility analysis and the importance of intangibles Review the various data warehouse architectures and development methodologies Understand the project success factors associated with data warehouse implementation Chapter 13: The Systems Perspective of a DSS Objectives 1. 2. 3. 4. 5. 6. Develop an clear understanding of the concept of a system Understand the need for a systems perspective in DSS deployment Understand the concept and value of functional decomposition Define the DSS Information System Architecture Recognize the factors contributing to information quality Focus on the role of the Internet in DSS development and use Chapter 14: Designing and Building Decision Support Systems Objectives 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Grasp the two basic DSS development strategies Comprehend the various approaches to DSS analysis and design Understand the DSS development process (DDP) Learn the differences between the traditional system development life cycle (SDLC) approach and the DDP Study the process of prototyping Assess the two basic kinds of prototypes: throwaway prototypes and iterative prototypes Review the benefits and limitations of prototyping Consider the skill set needed by DSS developers Learn the concept of end-user computing Recognize the advantages and risks of end-user DSS development Evaluate the criteria for selection of DSS development tools Chapter 15: Implementing and Integrating Decision Support Systems Objectives 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Understand that the essence of implementation is the introduction of change Learn several theoretical models of change Based upon the sources of project initiation, identify the six patterns of implementation Examine the frameworks for system evaluation: overall software quality, attitudinal measures of success, technical measures of success, organizational measures of success Learn how to measure user satisfaction toward a DSS Review the four categories of measurement in a generalized framework to measure the success of a DSS: system performance, task performance, business opportunities, and evolutionary aspects Comprehend the risk factors in DSS implementation projects Demonstrate how to formulate possible implementation strategies for dealing with identified risk factors Consider the importance of integration Investiage the concepts behind global DSS integration Recognize the factors that can result in resistance to changes associated with a new system Chapter 16: Creative Decision Making and Problem Solving Objectives 1. Explore three perspectives on the theory of creativity: psychoanalytical, behavioral, and process 2. Review five basic categories of ways of thinking: logical, lateral, critical, opposite, and groupthink 3. Understand why it is important for decision makers to impart intuition and creativity to the decision process 4. Recognize categories of creative problem-solving techniques and their basic concepts Chapter 17: Intelligent Software Agents, Bots, Delegation, and Agency Objectives 1. 2. 3. 4. 5. Understand the world of delegation and agency in cyberspace and networks Learn the basic concept of intelligent software agents (ISAs) Recognize the characteristics of intelligent software agents Understand the types of problems intelligent agents can solve Explore the future applications of intelligent software agents