This chapter discusses the objectives of 14 chapters in a textbook on decision support systems (DSS). The chapters cover topics such as the definition of DSS, decision making, organizational decisions, modeling techniques, group decision support, executive information systems, expert systems, knowledge engineering, machine learning, data warehousing, data mining, designing and building DSSs, implementing DSSs, creative problem solving, and intelligent software agents. The objectives provide an overview of the key concepts and topics discussed in each chapter.

1. 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

2. 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 decision-
making 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

3. 6. Understand the common MDM coordination methods
7. Comprehend the meaning of the virtual workplace
Chapter 6: Executive Information Systems
Objectives
1. Understand the definition of an executive information system (EIS)
2. Realize the two design requirements of an EIS and its fluidity
3. Understand the drill down capability of an EIS
4. Learn the history of the EIS
5. Be familiar with executive activities and their basic categories
6. Learn the various types of information needed by top executives
7. Be familiar with executive information determination methods
8. Gain an understanding of EIS hardware and software components
9. Become familiar with the categories of the current EIS technologies
10. Comprehend a development framework for the EIS
11. Understand some limitations and pitfalls of the EIS
12. Learn the conditions of transformation in executive decision making
13. Gain an understanding of the potential features of current and future EISs
Chapter 7: Expert Systems and Artificial Intelligence
Objectives
1. Define expert system and artificial intelligence
2. Describe the several different reasoning processes used by humans
3. Describe the methods available to create a computer-based reasoning system
4. Explain the concepts and structure of expert systems
5. Understand the predesign activities associated with building an expert system
6. 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

4. 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. Understand the concept of data mining (DM)
2. Trace the evolution of decision support activities from verification to discovery
3. Understand the concept of online analytical processing (OLAP) and its rules
4. Learn the two approaches used to conduct multidimensional analysis of data-
multidimensional OLAP (MOLAP) and relational OLAP (ROLAP)-and explore the different
situations suited for MOLAP and ROLAP architectures
5. Recognize the four major categories of processing algorithms and rule approaches used
to mine data: classification, association, sequence, and cluster
6. Assess current data mining technologies including statistical analysis, neural networks,
genetic algorithms, fuzzy logic, and decision trees
7. Learn the general process of knowledge discovery through examples

5. 8. Examine market basket analysis procedures
9. Understand the current limitations and challenges to data mining
10. 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. Understand enterprise model approach to building a data warehouse
2. Explore the issues related to defining the project scope
3. Examine the concepts associated with economic justification of the project
4. Review the various analysis tools used to gather system requirements
5. Explain the design of a project plan for construction of a data warehouse
6. Understand the process of economic feasibility analysis and the importance of intangibles
7. Review the various data warehouse architectures and development methodologies
8. Understand the project success factors associated with data warehouse implementation
Chapter 13: The Systems Perspective of a DSS
Objectives
1. Develop an clear understanding of the concept of a system
2. Understand the need for a systems perspective in DSS deployment
3. Understand the concept and value of functional decomposition
4. Define the DSS Information System Architecture
5. Recognize the factors contributing to information quality
6. Focus on the role of the Internet in DSS development and use
Chapter 14: Designing and Building Decision Support Systems
Objectives
1. Grasp the two basic DSS development strategies
2. Comprehend the various approaches to DSS analysis and design
3. Understand the DSS development process (DDP)
4. Learn the differences between the traditional system development life cycle (SDLC)
approach and the DDP
5. Study the process of prototyping
6. Assess the two basic kinds of prototypes: throwaway prototypes and iterative prototypes
7. Review the benefits and limitations of prototyping
8. Consider the skill set needed by DSS developers
9. Learn the concept of end-user computing

6. 10. Recognize the advantages and risks of end-user DSS development
11. Evaluate the criteria for selection of DSS development tools
Chapter 15: Implementing and Integrating Decision Support Systems
Objectives
1. Understand that the essence of implementation is the introduction of change
2. Learn several theoretical models of change
3. Based upon the sources of project initiation, identify the six patterns of implementation
4. Examine the frameworks for system evaluation: overall software quality, attitudinal
measures of success, technical measures of success, organizational measures of
success
5. Learn how to measure user satisfaction toward a DSS
6. 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
7. Comprehend the risk factors in DSS implementation projects
8. Demonstrate how to formulate possible implementation strategies for dealing with
identified risk factors
9. Consider the importance of integration
10. Investiage the concepts behind global DSS integration
11. 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. Understand the world of delegation and agency in cyberspace and networks
2. Learn the basic concept of intelligent software agents (ISAs)
3. Recognize the characteristics of intelligent software agents
4. Understand the types of problems intelligent agents can solve
5. Explore the future applications of intelligent software agents