Support for the module Management Decision Making

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Judgement
and Choice
January 22 2017
Introduction
©2016 L. SCHLENKER
Managerial Decision Making
http://Dsign4.biz

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2
Date Subject
23 janvier Judgement and Choice
24 janvier Cognitive Biases
24 janvier Working with Data
25 janvier Decisions under Risk and Uncertainty
26 janvier Wellbeing and happiness
How does the digitalization of the
economy how we analyze, decide
and measure business performance?
©2016 LHST sarl
Course Structure Introduction

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Assessment
Grading Scale
Blog posts: 50 % of your grade will be based on the quality of your in-class work
Final exam: 50 % of your grade will be based upon a QCM
• Participants will produce two deliverables
for this module.
• Individually, they will document and
produce blog posts on the story of self, the
story of us, and the story of Now.
• The final exam will explore what they have
learned about decision-making, business
analytics and visual communication.
Introduction

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Introduction
©2016 L. SCHLENKER
Introduction
Method
Rationality
Decision making
Decision Trees

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Blog post for Wednesday
• What are you looking for?
• What is the client’s story
• How are you the solution
• What is the happy end

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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

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Introduction
Copyright © 2004 by South-Western, a division of Thomson Learning. All
rights reserved.

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• Programmed decisions:
Challenges that occur often
enough to enable decision rules to
be developed.
• Nonprogrammed decisions:
Responses to situations that are
unique, are poorly defined and
largely unstructured.
Introduction

9. ©2013 LHST sarl©2016 L. SCHLENKER
Introduction
Information
Technology
Business
Analytics
Decision
Making
INDIVIDUALS
GROUPS
ORGANIZATIONS
• 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

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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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How do great leaders make great decisions?

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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

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• Artefacts
Any thing made by people in which knowledge is
imbedded
• Skills
Abilities that can be trained and measured
• Heuristics
Rules of thumb, the outcome of experience
• Experience
Accumulated experience of failure and success
ASHEN David Snowden
Bounded
Rationality

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• 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.
Bounded
Rationality
Snowden and
Boone

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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

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• Break up into groups of four
• Individually, think of a
decision you had to make
that was particularly difficult
• What made it difficult?
Method

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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
Methods
Models are simplified versions of the
artefacts they represent

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“A formal, logical explanation of some
events that includes predictions of how
things relate to one another.”
©2014 L. SCHLENKER
W. Zikmund (2010)
S. Greener, Chapter 1
Rational Choice– We
choose to maximize
“happiness”.
Methods

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Introduction
Ontology Realism Internal Realism Relativism Nominalism
Epistemology
Methodology
Strong
Positivism
Positivism
Constructionis
m
Strong
Constructionis
m
Aims Discovery Exposure Convergence Invention
Starting points Hypotheses Propositions Questions Critique
Designs Experiment
Large surveys;
multi-casts
Cases and
surveys
Engagement and
reflexivity
Data types
Numbers and
facts
Numbers and
words
Words and
numbers
Discourse and
experiences
Analysis/
interpretation
Verification/
falsification
Correlation and
regression
Triangulation and
comparison
Sense-making;
understanding
Outcomes
Confirmation of
theories
Theory testing
and generation
Theory
generation
New insights and
actions
Epistemology qualifies the relationship between the researcher
and reality .
S. Greener, Chapter 6©2014 L. SCHLENKER
Models

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A prediction about the relationship between
two or more variables.
Yeong, 2011
©2014 L. SCHLENKER
Worker
satisfaction
increases worker
productivity
Method

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Independent Variable: The presumed “cause” in
the theoretical model.
Dependent Variable: The presumed “effect” in
the theoretical model.
©2014 L. SCHLENKER
S. Greener, Chapter 6
The more people in line
ahead of you, the longer
you will need to wait
quiz
Method

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©2014 L. SCHLENKER
Fann, 1970
Brown, 2009
generalize existing narrow down existing
choices
create space to generate
new ideas
Inductive Deductive Abductive
©2014 L. SCHLENKER
What best explains
why Jack works so
hard?
Method

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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.

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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

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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

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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

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A systematic pattern of
deviation from norm or
rationality in judgment
Cognitive Biases
Bounded
Rationality

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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

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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

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• 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

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• 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

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• 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