The document discusses various topics related to technology, innovation, and decision making. It introduces different types of productivity and discusses how algorithms and analytics can improve decision making. It also examines Simon's model of intelligence, design, and choice in decision making and explores concepts like bounded rationality and cognitive biases that can influence decisions. Modeling and various analytical techniques like decision trees, clustering, and association rule mining are presented as approaches to help address complex problems and make better choices.

1. Technology and Innovation
Decision Making
March 10 2017
Introduction
http://DSign4Methods.com

2. Productivity
• Harder, better, faster…
• Mechanized productivity
• Knowledge productivity
• Continuous Productivity
Steven Sinofsky
Digital Age
©2016 L. SCHLENKER

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.
Crossroads
©2016 L. SCHLENKER

4. 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

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

6. ©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

7. 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

8. How do great leaders make great decisions?

9. 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

10. ©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.
Bounded
Rationality
Snowden and
Boone

11. ©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

12. • 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
Digital Age
©2016 L. SCHLENKER

13. • 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

14. – 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?
Decision
Making
©2016 L. SCHLENKER

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
Decision
Making
©2016 L. SCHLENKER

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
Decision
Making
©2016 L. SCHLENKER
• 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

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
Decision
Making

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

19. • 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

20. ©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 …
Bounded
Rationality
An example of cognitive bias, in which people react
to a particular choice in different ways depending on
how it is presented

21. • 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

22. Analytics

23. 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
Analytics

24. 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
Analytics
©2016 L. SCHLENKER

25. Analytics

26. • 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
 Supervised
 Categorical
It’s sunny, hot,
normaly humid, and
windy – should I play
tennis?

27. • 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
Analytics
©2016 L. SCHLENKER

28. • 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
Methods
“A Customer who bought bread an butter also bought a
carton of milk 60 percent of the time.“
 Unsupervised
 Categorical