The document discusses various topics related to digital technologies, innovation, and decision-making, including: 1) It introduces algorithms, reinforced learning, predictive analytics, and examples like PageRank. 2) It discusses the concepts of intelligence, design, and choice in decision-making. 3) It covers types of decisions like programmed vs non-programmed and factors like uncertainty, complexity, and equivocality that influence decisions.

1. Digital Technologies and Innovation
Decision-Making
April 2018
http://DSign4Methods.com

2. ©2016 L. SCHLENKER
Agenda
Introduction
The Data Revolution
Time, Space and Organization
The Analytical Method
Introduction

3. • Reinforced learning and predictive
analytics
• “ Algorithms tailor search to our
interests far better than any one
human ever could”
• Merge Sort, Fourrier Transform, RSA
algorithm, PageRank (eigenvalues)…
• The UK’s the Alan Turing Institute
“Informally, an algorithm is any well-defined computational procedure that takes
some value, or set of values, as input and produces some value, or set of values, as
output.” Introduction to Algorithms 3rd edition.
©2016 L. SCHLENKER
Introduction

4. Productivity
• Harder, better, faster…
• Mechanized productivity
• Knowledge productivity
• Continuous Productivity
Steven Sinofsky
©2016 L. SCHLENKER
Introduction

5. – 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

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

7. ©2016 L. SCHLENKER
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
Introduction

8. 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

9. How do great leaders make great decisions?
Introduction

10. 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

11. – 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?
©2016 L. SCHLENKER
Introduction

12. ©2016 L. SCHLENKER
• 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

13. 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

15. 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
©2016 L. SCHLENKER
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

17. • 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
Introduction

18.  Supervised
 Categorical
It’s sunny, hot,
normaly humid, and
windy – should I play
tennis?
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

20. 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
©2018 L. SCHLENKER
Introduction

21. 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

23. • Cluster analysis is used for
identifying natural groupings of
things.
• Data instances that are similar
to each other are categorized
into one cluster.
• Similarly, data instances that are
very different from each other
are moved into other clusters.
• Clustering is an unsupervised
learning technique as there is
no output or dependent
variable
 Unsupervised
 Discrete
©2018 L. SCHLENKER
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