Turban, Aronson,

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Turban, Aronson, Liang
Decision Support Systems and
Intelligent Systems, 7th Edition
Chapter 2 Decision-Making Systems, Models, and
Support
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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 between 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

• Process of choosing amongst alternative courses
  of action for the purpose of attaining a goal(s)
• Simons four phases of the decision process are
• Intelligence
• Design
• Choice
• implementation

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Systems Theory

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

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Systems Theory
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System Types

• Closed system
• Independent
• Takes no inputs
• Delivers no outputs to the environment
• Black Box
• Very rare e.g. an economy of mice on the moon
• Open system
• Accepts inputs
• Delivers outputs to environment

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Simons Model of Decision-Making

• Simons original three phases
• Intelligence
• Design
• Choice
• He added fourth phase later
• Implementation
• Book adds fifth stage
• Monitoring
• Refer to Figure 2.2, p. 50
• See Cases 2.1, 2.2, 2.3, pp. 91-98
• See Exercises 5, 6, 7 on p. 88

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Simons Model of Decision-Making
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Intelligence Phase

• Scan the environment
• Analyse organisational goals and objectives
• Collect data
• Identify problem
• Categorise problem
• Programmed (stuctured) and non-programmed
  (unstructured)
• Decomposed into smaller parts e.g. AHP
• Assess ownership and responsibility for problem
  resolution whos going to push this?
• Especially important in large organisations

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Design / Choice Phases

• Select a principle of choice i.e. acceptability
  of a solution e.g.
• Risk-taker, or risk-averse ?
• Optimal, or good enough (satisficing) ? (may
  be affected by complexity, number of
  alternatives, and degree of uncertainty /
  imperfect information)
• Criteria and constraints
• Determine alternative courses of action e.g.
  scenarios
• May be automatic or manual
• Analyse potential solutions / outcomes
• Create model, e.g.
• Markov processes (predictive forecasting),
  financial modelling, workflow simulations,
  logistics / operations research models,
  environmental impact analyses
• Validate results of model analyse for
  robustness
• Evaluate feasibility / goal attainment of various
  solutions

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Implementation Phase

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

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Phases of Decision-Making
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SDLC / Waterfall Model -v- Simons Model
Slide courtesy of Barry, C. (2004) A Conceptual
Framework for Information Systems Development
from a Decision-Making Perspective. In Vasilecas,
O. et al. (eds), Proceedings of 13th
International Conference on Information Systems
Development, Vilnius, Lithuania, September 9-11,
pp. 170-181.
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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, 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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Rationalism (Positivism)

• Technical rationalism
• instrumental problem solving made rigorous by
  the application of scientific theory and
  technique (Schön, 1983 p. 21)
• According to the rational actor model of
  decision-making, man is an intelligent being
  whose every action is purposeful and based on
  conscious, logical reasoning which he can readily
  explain.
• Technical rationalism is founded on the
  epistemology of positivism, the classical basis
  of the natural sciences. It was this ideology
  which underpinned Taylors (1911) Principles of
  Scientific Management

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Rationalism (Positivism)

• Assumes that the world is governed by rules and
  laws, which can be rationally described in terms
  of cause-and-effect.
• Following Bacons aphorism that knowledge is
  power, the objective of positivism is to build
  knowledge of causal relationships and thereby
  attain control over situations through being able
  to accurately predict the outcomes of particular
  interventions, in the words of Auguste Comte,
  savoir pour prévoir.
• Within social science, the application of the
  positivist paradigm assumes that human behaviour
  and social/organisational phenomena can be
  reduced to a set of deterministic laws, as in the
  natural sciences of physics and chemistry. An
  early proponent of this view was Thomas Hobbes,
  who in Leviathan (1651) set out his opinion that
  the human body is a machine, all of whose
  functions and activities can be fully explained
  in mechanistic terms. More recently, the
  psychologist B. F. Skinner in The Behavior of
  Organisms (1938) took a similar view that humans
  can be programmed to respond in predictable ways
  to external stimuli.

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Rationalism (Positivism)

• Rationalism is founded on the principles of logic
  and mathematics. René Descartes possessed an
  unshakable conviction in the infallibility of
  mathematics, remarking that Arithmetic and
  Geometry alone are free from any taint of falsity
  or uncertainty. He was of the view that all
  phenomena could be studied using the same logical
  procedures as used in mathematics
• Those long chains of reasoning, simple and easy
  as they are, of which geometricians make use in
  order to arrive at the most difficult
  demonstrations, had caused me to imagine that all
  those things which fall under the cognizance of
  man might very likely be mutually related in the
  same fashion and that, provided we follow the
  same method there can be nothing so remote that
  we cannot reach it, nor so recondite that we
  cannot discover it. (Descartes)

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Rationalism (Positivism)

• The philosophy of positivism submits that the
  scientific method of investigation is valid for
  inquiry into all domains, covering the spectrum
  from the natural and analytical sciences to the
  social sciences. John Stuart Mill, in System of
  Logic (1843) , expounded the view that
• The backward state of the moral sciences can
  only be remedied by applying to them the methods
  of physical science duly extended and
  generalised.

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Against Rationalism

• Checkland (1976) remarks that
• It seems beyond the power of science, however,
  to cope with the unstructured problems of the
  real-world, as opposed to the explicitly defined
  problems of the laboratory. In the unrestricted
  sciences progress is slow and methodological
  problems abound. Other ways of thinking need to
  be explored.
• The eminent scientist, Vannevar Bush, has written
  that
• If scientific reasoning were limited to the
  logical processes of arithmetic, we should not
  get far in our understanding of the physical
  world. One might as well attempt to grasp the
  game of poker entirely by the use of the
  mathematics of probability (Bush, 1945).

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Against Rationalism

• Faced by conditions of uncertainty, imperfect
  information, and ill-structured (Simon) or
  wicked (Rittel Weber) problem types,
  decision-makers are more likely to engage in
  satisficing behaviour which aims to produce
  acceptable or good, rather than optimal,
  solutions.
• Simon (1981) refers to this as bounded
  rationality, and it is similar to what Lindblom
  (1959) had earlier called the science of
  muddling through.
• This perspective places greater emphasis on the
  role of individual judgement, creativity,
  intuition, and experience in decision-making and
  problem-solving.

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Against Rationalism

• Speaking of engineering design, Rouse Boff
  (1987) remark that
• if an outside observer were to characterize
  designers behaviors, particularly for complex
  domains such as aircraft design, it is quite
  likely that such an observer would conclude that
  chaos is the most appropriate characterization of
  design teams at work.

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Theory of Situated Action

• The foundation of the situated action view of
  problem-solving is that,
• rather than attempting to abstract action away
  from its circumstances and represent it as a
  rational plan, the approach is to study how
  people use their circumstances to achieve
  intelligent action (Suchman, 1987 p. 50).
• Related to notions of emergent decision-making
  (Mintzberg) or improvisation (Weick, Ciborra)
• Explicitly, improvised problem-solving activity
  may appear to be out of control but implicitly
  there is method in the madness

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Naturalistic Decision Making

• Klein (1998) uses the term naturalistic decision
  making to describe the process by which
  paramedics, firefighters, and other workers
  facing high-stakes spur-of-the-moment situations
  decide on appropriate courses of action. As with
  improvisation in the performance arts, he sees
  situated decision-making as the product of finely
  honed and well-rehearsed skills,
  experientially-based patterns, knowledge of
  fundamental principles and procedures, teamwork,
  and informed judgement.

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In-Class Exercise (45 mins)

• Working in groups of 3-4, take 2-3 of the
  following actors and consider the nature of
  problems faced, decision-making style,
  environment, information requirements, analytical
  rationale, etc.
• A chess player
• A poker player
• A nurse working in the Accident Emergency ward
• A soldier in a foreign war zone
• A professional sportsperson participating in a
  competitive game
• A stock trader / portfolio manager
• A software project manager
• A politician
• An urban planner
• A gardener
• A detective policeman at the scene of a recent
  crime
• A Formula 1 race engineer