Course Support

1. ©2013 LHST sarl
Working in the Digital Age
http://DSign4Change.com
Decision Making
Fundamentals
November 5 2016
Introduction
©2016 L. SCHLENKER

2. ©2013 LHST sarl
Introduction
©2016 L. SCHLENKER
Introduction
Modeling
Rationality
Decision making
Decision Trees

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Introduction
– 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 Jack is looking at Anne, but Anne is looking
at George. Jack will be successful, but
George will not. Is a successful person
looking at an unsuccessful one?

4. ©2013 LHST sarl©2016 L. SCHLENKER
Introduction
INTELLIGENCE
• Fact Finding
• Problem/Opportunity
Sensing
• Analysis/Exploration
DESIGN
• Formulation of Solutions
• Generation of Alternatives
• Modeling/Simulation
CHOICE
• Alternative Selection
• Goal Maximization
• Decision Making
• Implementation

5. ©2013 LHST sarl
Identify
Problem
Formulate &
Implement
Model
Analyze
Model
Test
Results
Implement
Solution
unsatisfactory
results
©2016 L. SCHLENKER
Introduction
• Programmed Decisions
– Situations in which past experience permits
decision rules to be developed and applied
in the future
• Non_programmed Decisions – responses to
unique, poorly defined challenges that have
important consequences to the organization

6. ©2013 LHST sarl©2016 L. SCHLENKER
Introduction
Information
Technology
Business
Analytics
Decision
Making
INDIVIDUALS
GROUPS
ORGANIZATIONS

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• Structured data - refers to information
with a high degree of organization
– Tables
– Spreadsheets
– Databases
• Non structured data – data that is not
organized in a pre-defined manner
– Music
– Videos
• Quantitative data - numeric values that
indicate how much or how many
– Production quantities
– Rate of return
– Cash flows
• Qualitative data - labels or concepts
used to identify an attribute
– Performance
– Effectiveness
Introduction

8. ©2013 LHST sarl
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

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1. Formulation.
2. Solution.
3. Interpretation.
Introduction

10. ©2013 LHST sarl
• Models are at the heart of decision-making
• Types of models:
– Mental (language, mnemonics)
– Visual (blueprints, graphics)
– Physical/Scale (maps, buildings)
– Mathematical (analytics)
Models
Models are usually simplified versions
of the artefacts they represent
A valid model portrays the the
relevant characteristics of the object
or decision being studied

11. ©2013 LHST sarl
• 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.
Models

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• Deterministic Models - all
the input data are available
with complete certainty
• Stochastic Models – some
of the input data values are
uncertain
©2016 L. SCHLENKER
Models

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Classical Decision Theory
©2016 L. SCHLENKER
Models
– Underlying assumptions
• Decision makers have all the information they need
• Decision makers can make the best decision
• Decision makers agree about what needs to be done
When faced with a
decision situation,
managers should. . .
. . . and end up with
a decision that best
serves the interests
of the organization.
• obtain complete
and perfect information
• eliminate uncertainty
• evaluate everything
rationally and logically
The rational model: decision
making is a straightforward, three-
stage process - list, rank, select
Copyright © by Houghton Mifflin Company.

14. ©2013 LHST sarl
Behavioral Decision Making
©2016 L. SCHLENKER
Models• Bounded rationality introduces a set of more realistic
assumptions about the decision-making process
– Satisficing: limited information searches to identify
problems and alternative solutions
– : a limited capacity to process information
When faced with a
decision situation
managers actually…
. . . and end up with a
decision that may or may
not serve the interests
of the organization.
• use incomplete and
imperfect information
• are constrained by
bounded rationality
• tend to satisfice
– Organizational coalitions: solution chosen is a
result of compromise, bargaining, and
accommodation between coalitions

15. ©2013 LHST sarl
Views main components of decision process (problems,
solutions, participants, choice situations) as all mixed
up together in the garbage can of the organization
– Dynamic situation means often more acting than
thinking, solutions used even if can’t be linked to
particular problem, many problems going
unsolved
– Highlights fact that often decision and
implementation done by different people
©2016 L. SCHLENKER
Models

16. ©2013 LHST sarl
Prescriptive known or under LP, Networks, IP,
decision maker’s CPM, EOQ, NLP,
control GP, MOLP
Predictive known or under Regression Analysis,
decision maker’s Time Series Analysis,
control Discriminant Analysis
Descriptive unknown or Simulation, PERT,
uncertain Queueing,
Inventory Models
Model Independent OR/MS
Category Variables Techniques
Models
www.dezyre.com
©2016 L. SCHLENKER

17. ©2013 LHST sarl
• Models can be used for structurable aspects of
decision problems.
• Other aspects cannot be structured easily,
requiring intuition and judgment.
• Caution: Human judgment and intuition is not
always rational!
Bounded
Rationality

18. ©2013 LHST sarl
• Arise when trivial factors
influence initial thinking about
a problem.
• Decision-makers usually
under-adjust from their initial
“anchor”.
Bounded
Rationality
A mental shortcut or rule of thumb the brain
uses to simplify complex problems

19. ©2013 LHST sarl©2016 L. SCHLENKER
• Refers to how decision-makers view a problem
from a win-loss perspective.
• The way a problem is framed often influences
choices in irrational ways…
• Suppose you’ve been given $200 000 for
sure. Which of the following two options would
your prefer to lose 500 for sure, to 1000 with a
a probability of 50%?
Bounded
Rationality
An example of cognitive bias, in which people react
to a particular choice in different ways depending on
how it is presented

20. ©2013 LHST sarl
• Decision making is affected by
a lack of information
• Decision-makers usually
prefer to make “safe bets”
rather than test the unknown
Bounded
Rationality
The tendency to avoid options for which missing
information makes the probability seem "unknown".[
©2016 L. SCHLENKER

21. ©2013 LHST sarl
Good decisions do not always lead to good
outcomes...
A structured, modeling approach to decision making
helps us make good decisions, but can’t guarantee
good outcomes.
Good decisions vs good outcomes
Bounded
Rationality
Define
the
Problem
Identify
the
Alternatives
Determine
the
Criteria
Identify
the
Alternatives
Choose
an
Alternative
Structuring the Problem Analyzing the Problem

22. ©2013 LHST sarl
Why do we take poor decisions?
• The object of measurement (i.e., the
thing being measured) is not
understood.
• The concept or the meaning of
measurement is not understood.
• The methods of measurement are
not well understood
Decision
Making
©2016 L. SCHLENKER

23. ©2013 LHST sarl
• 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
Decision
Making
©2016 L. SCHLENKER

24. ©2013 LHST sarl©2016 L. SCHLENKER
• Is management an art or a science
?
• Mesaurement is the reduction of
uncertainty through putting a
number on it
• In science , engineering, actuarial
science, economics, - we talk of
putting a number on it
www.google.com/dashboard
©2014 L. SCHLENKER
"Although this may seem a paradox,
all exact science is based on the idea
of approximation” Bertrand Russel
Decision
Making
©2016 L. SCHLENKER

25. ©2013 LHST sarl
• Reducing the number of
potential outcomes is the key
to better decision-making
• Develop unambiguous
definitions and measurement
• What data do I have, Choose
the appropriate measure
• Understand how people react
to the data
www.google.com/dashboard
©2016 L. SCHLENKER
Ask Examples Resources
Is it possible that this may
already have been
researched?
The average cost of IT
training for given type of
user
Go to the
library
(Internet)
Could it be projected from
past experience?
Growth in product demand Research the
market
Does it leave a trail of
some kind?
Current level of customer
retention
Look for the
data
Could it be observed in
real-time?
The amount of time an
equipment operator spends
filling out forms
Unsupervised
learning
Can it be tested? The effect of a new system
on the productivity of a
sales clerk
Supervised
learning
Decision
Making

26. ©2013 LHST sarl
Analysis Phase of Decision-Making
Process
• analyst will concentrate on the
quantitative data associated with the
problem
• analyst will develop mathematical
models that describe the objectives,
constraints, and other relationships that
exist in the problem
• analyst will use quantitative methods to
make a recommendation
Business analysis that aims to understand or
predict behavior or events through the use of
mathematical measurements
Decision
Making
Potential Reasons for a
Quantitative Analysis Approach
to Decision Making
– The problem is complex.
– The problem is very
important.
– The problem is new.
– The problem is repetitive.

27. ©2013 LHST sarl
• 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.
Decision
Trees
Witten, Frank and Hall 2010
 Supervised
 Categorical
Do we plan when it’s
sunny, hot, normal and
windy ?

28. ©2013 LHST sarl
• Decision trees utilize a network of two types of
nodes: decision (choice) nodes, and states of
nature (chance) nodes
• Square represents decisions to be made.
• Circles represents chance events. Chance
nodes, are random variables and they represent
uncertain quantities that are relevant to the
decision problem.
• Branches from a square correspond to the
choices available to the decision maker.
• Branches from a circle represent the possible
outcome of a chance event.
• The consequence is specified at the ends of the
branches.
Simple Probability Tree
Decision
Trees
©2016 L. SCHLENKER
 Supervised
 Categorical

29. ©2013 LHST sarl
• A good decision tree should be short and ask
only a few meaningful questions.
• Decision trees are used mostly to answer
relatively simple binary decisions.
• Decision trees use machine learning algorithms
to abstract knowledge from data
• The more training data is provided, the more
accurate its knowledge extraction will be, and
thus, it will make more accurate decisions.
• The more variables the tree can choose from,
the greater is the likely of the accuracy of the
decision tree. split.
Decision
Trees
©2016 L. SCHLENKER
 Supervised
 Categorical

30. ©2013 LHST sarl
• The options represented by branches from a
decision node must be such that the decision
maker can choose only one option.
• Each chance node must have branches that
correspond to a set of mutually exclusive
outcomes;
• Whereas we build the tree left to right, we
evaluate the tree in the reverse direction (“rolling
back the tree”).
• At each chance node, we can calculate the
expected payoff represented by the probability
distribution at the node.
Decision
Trees
©2016 L. SCHLENKER
 Supervised
 Categorical