6. – Intelligence: The identification of
a challenge that requires data
collection and a relevant decision
– Design: Exploring, planning, and
analyzing alternative courses of
action
– Choice: Selecting the appropriate
course of action
H. A. Simon
Introduction
9. UNCERTAINTY
• Facts not known
• Look for
Information
• Fact Finding
/.Analysis
DATA
BASED
COMPLEXITY
• Too many
facts
• Produce
Information
• Simulation/Synt
hesis
MODEL
BASED
EQUIVOCALITY
• Facts not Clear
• Analyse Information
• Application of Expertise
KNOWLEDGE
BASED
Introduction
10. How do great leaders make great decisions?
Introduction
11. System 1, System 2
• Thinking Fast, Thinking Slow (2011)
presents a dichotomy between two modes
of thought
• "System 1" is fast, instinctive and
emotional (intuition)
• "System 2" is slower, more deliberative,
and more logical (reasoning).
• Biases have two sources of error, the
observed behavior and “rationality”
Introduction
12. • In the 'simple' domain, problems and
solutions are known. There is a one-to-
one relationship between cause and
effect.
• In the 'complicated' domain, problems
and solutions are knowable. There is a
one to N relationship between cause and
effect.
• In the 'complex' domain, problems or
solutions are unknown. There is a N to N
relationship between causes and effects.
Snowden and
Boone
Introduction
14. • Cost - It is often less costly to analyze decision
problems using models.
• Time - Models often deliver needed information
more quickly than their real-world counterparts.
• Comprehension - Models can be used to do
things that would be impossible.
• Models give us insight & understanding that
improves decision making.
Introduction
16. • What does productivity mean (faster, more
impressive, more precise) ?
• Is it observable – how is something more
precise answer to a problem?
• The challenge is deciding what we want to
measure
Lewis Mumford, Technics and Civilization
• What does productivity mean (faster, more
impressive, more precise) ?
• Is it observable – how is something more
precise answer to a problem?
• The challenge is deciding what we want to
measure
Introduction
19. Types of Learning
• Supervised (inductive) learning
Training data includes desired
outputs
• Unsupervised learning
Training data does not include
desired outputs
• Semi-supervised learning
Training data includes a few desired
outputs
• Reinforcement learning
Rewards from sequence of actions
Introduction
22. • Decision-tree models offer a visual tool that can
represent the key elements in a model for decision
making
• Decision trees are a comprehensive tool for modeling all
possible decision options.
• While influence diagrams produce a compact summary
of a problem, decision trees can show the problem in
greater detail.
Supervised
Categorical
It’s sunny, hot,
normaly humid, and
windy – should I play
tennis?
Introduction
24. • Associate rule mining, or market
basket analysis; is a popular,
unsupervised learning
technique, used in business to
help identify shopping patterns.
• It helps find interesting
relationships (affinities) between
variables (items or events).
• Thus, it can help cross-sell
related items and increase the
size of a sale.
• There is no dependent variable –
and no right of wrong answer
“A Customer who bought bread an butter also bought a
carton of milk 60 percent of the time.“
Unsupervised
Categorical
Introduction