Chapter 2 DecisionMaking Systems, Models, and Support

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Chapter 2Decision-Making Systems, Models, and
Support
Turban, Aronson, and Liang
Decision Support Systems
and Intelligent Systems, Seventh
Edition
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Learning Objectives

• Learn the basic concepts of decision making.
• Understand systems approach.
• Learn Simons four phases of decision making.
• Understand the concepts of rationality and
  bounded rationality.
• Differentiate betwixt making a choice and
  establishing a principle of choice.
• Learn which factors affect decision making.
• Learn how DSS supports decision making in
  practice.

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Standard Motor Products Shifts Gears Into
Team-Based Decision-Making Vignette

• Team-based decision making
• Increased information sharing
• Daily feedback
• Self-empowerment
• Shifting responsibility towards teams
• Elimination of middle management

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Decision Making

• Decision Making a process of choosing among
  alternative courses of action for the purpose of
  attaining a goal or objects
• Managerial Decision Making is synonymous with the
  whole process of management (Simon, 1977)

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Decision Making

• The four phases of the decision process are
• Intelligence
• Design
• Choice
• implementation

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Decision Making Disciplines

• Behavioral discipline
• Philosophy
• Psychology
• Sociology
• Social psychology
• Law
• Anthropology
• Political science

• Scientific discipline
• Economics
• Statistics
• Decision analysis
• Mathematics
• MS/OR
• Computer science

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Systems

• A SYSTEM is a collection of objects such as
  people, resources, concepts, and procedures
  intended to perform an identifiable function or
  to serve a goal
• System Levels (Hierarchy) All systems are
  subsystems interconnected through interfaces

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Systems

• Structure
• Inputs
• Processes
• Outputs
• Feedback from output to decision maker
• Separated from environment by boundary
• Surrounded by environment

Input
Processes
Output
boundary
Environment
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• Inputs are elements that enter the system
• Processes convert or transform inputs into
  outputs
• Outputs describe finished products or
  consequences of being in the system
• Feedback is the flow of information from the
  output to the decision maker, who may modify the
  inputs or the processes (closed loop)
• The Environment contains the elements that lie
  outside but impact the system's performance

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How to Identify the Environment?

• Two Questions (Churchman, 1975)
• 1. Does the element matter relative to the
  system's goals? YES
• 2. Is it possible for the decision maker to
  significantly manipulate this element? NO

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Environmental Elements Can Be

• Social
• Political
• Legal
• Physical
• Economical
• Often Other Systems

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System Types

• Closed system
• Independent
• Takes no inputs
• Delivers no outputs to the environment
• Black Box
• Open system
• Accepts inputs
• Delivers outputs to environment

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System Effectiveness and Efficiency

• Two Major Classes of Performance Measurement
• Effectiveness is the degree to which goals are
  achievedDoing the right thing!
• Efficiency is a measure of the use of inputs (or
  resources) to achieve outputsDoing the thing
  right!
• MSS emphasize effectivenessOften several
  non-quantifiable, conflicting goals

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Models

• Major component of DSS
• Use models instead of experimenting on the real
  system
• A model is a simplified representation or
  abstraction of reality.
• Reality is generally too complex to copy exactly
• Much of the complexity is actually irrelevant in
  problem solving

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Models Used for DSS

• Iconic
• Small physical replication of system
• Analog
• Behavioral representation of system
• May not look like system
• Quantitative (mathematical)
• Demonstrates relationships between systems

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Benefits of Models

• 1. Time compression
• 2. Easy model manipulation
• 3. Low cost of construction
• 4. Low cost of execution (especially that of
  errors)
• 5. Can model risk and uncertainty
• 6. Can model large and extremely complex systems
  with possibly infinite solutions
• 7. Enhance and reinforce learning, and enhance
  training. Computer graphics advances more
  iconic and analog models (visual simulation)

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Phases of Decision-Making

• Simons original three phases
• Intelligence
• Design
• Choice
• He added fourth phase later
• Implementation
• Book adds fifth stage
• Monitoring

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Decision-Making Intelligence Phase

• Scan the environment
• Analyze organizational goals
• Collect data
• Identify problem
• Categorize problem
• Programmed and non-programmed
• Decomposed into smaller parts
• Assess ownership and responsibility for problem
  resolution

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The Intelligence Phase

• Scan the environment to identify problem
  situations or opportunities
• Find the Problem
• Identify organizational goals and objectives
• Determine whether they are being met
• Explicitly define the problem

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Problem Classification

• Structured versus Unstructured
• Programmed versus Nonprogrammed Problems Simon
  (1977)
• Nonprogrammed Programmed
• Problems Problems

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• Problem Decomposition Divide a complex problem
  into (easier to solve) subproblemsChunking
  (Salami)
• Some seemingly poorly structured problems may
  have some highly structured subproblems
• Problem OwnershipOutcome Problem Statement

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Decomposition approach
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Decision-Making Design Phase

• Develop alternative courses of action
• Analyze potential solutions
• Create model
• Test for feasibility
• Validate results
• Select a principle of choice
• Establish objectives
• Incorporate into models
• Risk assessment and acceptance
• Criteria and constraints

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Selection of a Principle of Choice

• Not the choice phase
• A decision regarding the acceptability of a
  solution approach
• Normative
• Descriptive

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Normative Models

• The chosen alternative is demonstrably the best
  of all (normally a good idea)
• Optimization process
• Normative decision theory based on rational
  decision makers

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Suboptimization

• Narrow the boundaries of a system
• Consider a part of a complete system
• Leads to (possibly very good, but) non-optimal
  solutions
• Viable method

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Descriptive Models

• Describe how things are believed to be
• Typically, mathematically based
• Applies single set of alternatives
• Examples
• Simulations
• What-if scenarios
• Cognitive map
• Narratives

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Problems

• Satisficing is the willingness to settle for less
  than ideal.
• Form of suboptimization
• Bounded rationality
• Limited human capacity
• Limited by individual differences and biases
• Too many choices

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Satisficing (Good Enough)

• Most human decision makers will settle for a good
  enough solution
• Tradeoff time and cost of searching for an
  optimum versus the value of obtaining one
• Good enough or satisficing solution may meet a
  certain goal level is attained
• (Simon, 1977)

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Why Satisfice?Bounded Rationality (Simon)

• Humans have a limited capacity for rational
  thinking
• Generally construct and analyze a simplified
  model
• Behavior to the simplified model may be rational
• But, the rational solution to the simplified
  model may NOT BE rational in the real-world
  situation
• Rationality is bounded by
• limitations on human processing capacities
• individual differences
• Bounded rationality why many models are
  descriptive, not normative

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Developing (Generating) Alternatives

• In Optimization Models Automatically by the
  Model!Not Always So!
• Issue When to Stop?

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Measuring Outcomes

• Is a statement of assumptions about the operation
  environment of a particular system at a given
  time, that is, a narrative description of the
  decision-situation setting.
• Goal attainment
• Maximize profit
• Minimize cost
• Customer satisfaction level (minimize number of
  complaints)
• Maximize quality or satisfaction ratings (surveys)

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Scenarios

• Useful in
• Simulation
• What-if analysis

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Importance of Scenarios in MSS

• Help identify potential opportunities and/or
  problem areas
• Provide flexibility in planning
• Identify leading edges of changes that management
  should monitor
• Help validate major assumptions used in modeling
• Help check the sensitivity of proposed solutions
  to changes in scenarios

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Possible Scenarios

• Worst possible (low demand, high cost)
• Best possible (high demand, high revenue, low
  cost)
• Most likely (median or average values)
• Many more
• The scenario sets the stage for the analysis

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Decision-Making Choice Phase

• Decision making with commitment to act
• Determine courses of action
• Analytical techniques
• Algorithms
• Heuristics
• Blind searches
• Analyze for robustness

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Decision-Making Implementation Phase

• Putting solution to work
• Vague boundaries which include
• Dealing with resistance to change
• User training
• Upper management support

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Source Based on Sprague, R.H., Jr., A Framework
for the Development of DSS. MIS Quarterly, Dec.
1980, Fig. 5, p. 13.
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Decision Support Systems

• Intelligence Phase
• Automatic
• Data Mining
• Expert systems, CRM, neural networks
• Manual
• OLAP
• KMS
• Reporting
• Routine and ad hoc

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Decision Support Systems

• Design Phase
• Financial and forecasting models
• Generation of alternatives by expert system
• Relationship identification through OLAP and data
  mining
• Recognition through KMS
• Business process models from CRM, RMS, ERP, and
  SCM

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Decision Support Systems

• Choice Phase
• Identification of best alternative
• Identification of good enough alternative
• What-if analysis
• Goal-seeking analysis
• May use KMS, GSS, CRM, ERP, and SCM systems

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Decision Support Systems

• Implementation Phase
• Improved communications
• Collaboration
• Training
• Supported by KMS, expert systems, GSS

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Decision-Making In Humans

• Temperament
• Hippocrates personality types
• Myers-Briggs Type Indicator (Focus 2.22
• Birkmans True Colours
• Gender

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Myers-Briggs Dimensions

• Extraversion (E) to Intraversion (I)
• Sensation (S) to Intuition (N)
• Thinking (T) to Feeling (F)
• Perceiving (P) to Judging (J)
• http//www.humanmetrics.com/cgi-win/JTypes2.asp

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Birkman True Colors Types
Red
Green
Blue
Yellow
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Decision-Making In Humans

• Cognitive styles
• What is perceived?
• How is it organized?
• Subjective
• Decision styles
• How do people think?
• How do they react?
• Heuristic, analytical, autocratic, democratic,
  consultative

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Cognition

• Cognition Activities by which an individual
  resolves differences between an internalized view
  of the environment and what actually exists in
  that same environment
• Ability to perceive and understand information
• Cognitive models are attempts to explain or
  understand various human cognitive processes

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Cognitive Style

• The subjective process through which individuals
  perceive, organize, and change information during
  the decision-making process
• Often determines people's preference for
  human-machine interface
• Impacts on preferences for qualitative versus
  quantitative analysis and preferences for
  decision-making aids
• Affects the way a decision maker frames a problem

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Cognitive Style Research

• Impacts on the design of management information
  systems
• May be overemphasized
• Analytic decision maker
• Heuristic decision maker

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Some Decision Styles

• Heuristic
• Analytic
• Autocratic
• Democratic
• Consultative (with individuals or groups)
• Combinations and variations
• For successful decision-making support, an MSS
  must fit the
• Decision situation
• Decision style

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• The system
• should be flexible and adaptable to different
  users
• have what-if and goal seeking
• have graphics
• have process flexibility
• An MSS should help decision makers use and
  develop their own styles, skills, and knowledge
• Different decision styles require different types
  of support
• Major factor individual or group decision maker

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