Khaled wahba msm

BIZ Days
Romania – Bucharest
12 November 2014
1
Decision Making in
Complex Context Using
Systems Thinking
Paradigm
Khaled Wahba, PhD | kwahba@msmromania.org
Adjunct Associate Professor | MSM Romania & the
Netherlands

Today I am sharing with you
Decision Making
Complex Context
(Complexity)
Systems Thinking
Copyright, 2014 2 | K. Wahba, PhD | DM-ST

Decision Making
 Processes are there
 And are known
No secrete about them, only the execution is the
challenge

Decision Making
 The Big Question!
 Are all of us supposed to make the
right Decision?
In order to understand the world quickly, look at
the extremes

Decision Making
 1) What would happen to our World if we all
make the right decision?
and
 2) What would happen to our World if we all
make no decision?
 Answer!
 is it the Bottom-Line-Answer (final conclusion)?
1) Life Stops!
2) Life Does not Move!

Decision Making
 So we are now happy that we still
make wrong decisions 
 Life is about making wrong
decisions
 Everyone so far is struggling and
busy to make the right decision
Ok, let them do that, life will stop for
them 

Decision Making
So today is
about how to make
right
Decision

Decision Making
So today is
NOT
about how to make
right
Decision

Decision Making
So today is
about how to make
wrong
Decision

Decision Making
 We wonder!
Why wrong decision could be right one? and why
right decision could be wrong one? and when the
right is right and when the wrong is wrong?
 It is all about HOW the mind is working during the
decision making process

Decision Making (The Mind)
 Philosophical View:
 Plato: Chariot pulled by two horses, Driver, with two
Horses
 Metaphor: Charioteer (Reason) & Horses (Emotion)
 Psychological View:
 Freud: ID & Ego (Conflict between Desire, Rational Brain)
 William James: Two Thinking Systems
 S1 -- Rational and Deliberate
 S2 -- Quick and Effortless, Emotional
 Neuro-Scientifical View:
 Rational (logic) vs. Emotional (passion)
 Dopamine (neurotransmitter), Orbitofrontal Cortex (OFC)

Decision Making
 It is all about how we see our world and
how we see our reality
 BUT in fact we don’t see our reality and
again we are lucky that we don’t see it ;)
We perceive it not see it
 Because if we see it, it means we all live
the same single reality and hence we will
make the right decision again, then Life
stops
 Visually I propose this view

Decision Making

Decision Making
Reality
!

Decision Making
TRUTH
?
Reality
!

Decision Making
TRUTH
?
Reality
!
We deal with
its Image
(Reality)
Truth
is
Hidden
Our
Mental Model

Decision Making
 We are the Blind photographer who exploring the
Truth with our own Camera (Mental Model) to picture
it
 My Camera (MM) is unique, with different Lenses,
Power, Resolutions, Detailing, etc …
 Who has the best Camera (MM) will have better
vision about the Truth (But never the Truth)
 So, our focus now became how to get the best
Camera (MM), but still we need to interpret the
Picture (Reality)
 Then the chance for better decision (maybe wrong,
maybe right)

Decision Making
 The quality of our interpretation
depends on the amount of details
(Complexity) we have in this picture
(Reality)
Then what defines the degree of
Complexity?

Complexity
19
Copyright, 2014 | K. Wahba, PhD | DM-ST

Complexity
 Complexity has turned out to be
very difficult to define (Heylighen)
 The dozens of definitions that
have been offered all fall short in
one respect or another
 The original Latin
word complexus, which signifies
"entwined", "twisted together"
 In order to have a complex you
need two or more components,
which are joined in such a way
that it is difficult to separate them
20

Complexity Defined
 The Oxford Dictionary defines something as
"complex" if it is "made of (usually several)
closely connected parts"
21

Complexity Defined
 The Oxford Dictionary defines something as
"complex" if it is "made of (usually several)
closely connected parts"
22

Complexity Defined
 A system would be more complex if more parts
could be distinguished, and if more
connections between them existed
23

Complexity: Two-Dimensional
 The aspects of distinction and connection
determine two dimensions characterizing complexity
24
distinction
connection

 Distinction corresponds to variety, to heterogeneity, to
the fact that different parts of the complex behave
differently
 Connection corresponds to constraint, to the fact that
different parts are not independent, but that the
knowledge of one part allows the determination of
features of the other parts
25

26
distinction
(differences)
Area of
Isolation
Area of
Integration
Very Low and Low High and Very High
connection (dependency)

27
distinction
(differences)
High and Very High
Very Low and Low
Area of
Disorder
Area of
Order

Complexity Matrix
28
distinction
(differences)
Area of Isolation
Area of Integration
Area of Disorder
Area of Order

Complexity Matrix
 Perfect Crystal, where the position of a
molecule is completely determined by the
positions of the neighbouring molecules to which
it is bound
29
distinction
(differences)
Perfect
Order

Complexity Matrix
 Perfect Crystal, where the position of a
molecule is completely determined by the
positions of the neighbouring molecules to which
it is bound
30
distinction
(differences)
Perfect
Order

Complexity Matrix
 Perfect Disorder, chaos or entropy, like in a
gas, where the position of any gas molecule is
completely independent of the position of the
other molecules
31
distinction
(differences)
Perfect
Disorder

Complexity Matrix
 Perfect Disorder, chaos or entropy, like in a
gas, where the position of any gas molecule is
completely independent of the position of the
other molecules
32
distinction
(differences)
Perfect
Disorder

Complexity Matrix Complete
 Complexity can only exist if both aspects are
present: neither perfect disorder, nor perfect
order
33
distinction
(differences)
Perfect
Disorder
Complexity
Perfect
Order
Simplicity

Complexity Matrix Complete
 Complexity increases when the variety
(distinction), and dependency (connection) of
parts or aspects increase, and this in several
dimensions
34
distinction
(differences)
Perfect
Disorder
Complexity
Perfect
Order
Simplicity

Complexity Types
 Weaver has defined two types of Complexities:
Disorganized and Organized
35
distinction
(differences)
Perfect
Disorder
Complexity
Perfect
Order
Simplicity

Complexity Types
 Disorganized coming from large number of
parts and lack of correlation (Chaos and
disordered)
36
distinction
(differences)
Disorganized
Perfect
Disorder
Complexity
Perfect
Order
Simplicity

Complexity Types
 Organized coming from lack of randomness,
high correlation and dependency, connection to
outside, emerges, self-organized
37
distinction
(differences)
Perfect
Disorder
Organized
Complexity
Perfect
Order
Simplicity

Dealing with Complexity
 Disorganized Complexity needs Advanced Statistical
Methods. Organized Complexity needs Modeling and
Simulation
38
distinction
(differences)
Disorganized Organized
Perfect
Disorder
Complexity
Perfect
Order
Simplicity

More Complexity …
39
distinction
(differences)
Disorganized Organized
Perfect
Disorder
Complexity
Perfect
Order
Simplicity
time
(dynamical)
Systems
Thinking

Systems Thinking

Process of Understanding
 Observe …
 Think …
 Theorize …
 Initial Understanding…
 Make Decision or Action …
 See Results and Consequences …
 Update Understanding
Copyright, 2014 | K. Wahba, PhD | DM-ST 41

The Need for Different Perspective
 Lucky Medical Doctor!!
 Tools, Equipment, Technology …
 The X-ray, MRI, Ultrasound …
 Different Views, and Angles and Details …
 Filter (not every details) …
 So, What about us?
 How to see the complexity in our reality?
 How to deal with it?
 We need Tools, Methods to intervene properly and timely
 Systems Thinking …

Policy Resistance:
The Law of Unintended Consequences
 The counterintuitive behavior of social
systems
 People seeking to solve a problem often
make it worse
 Policy may create unanticipated side
effects
 Attempts to stabilize the system may
destabilize it
 Decisions may provoke reactions by others
seeking to restore the balance we upset Copyright, 2014 | K. Wahba, PhD | DM-ST 43

Romanian Birth Rates: Facts
 Crude birth rate in late 1960’s was 15 per 1000
per year
 Government imposed policy to stimulate the birth
rate
 Modest tax incentives for larger families
 Importation of contraceptive devices was outlawed
 Propaganda campaigns praising the virtues of large
families
 Abortion was banned

Romanian Birth Rates: The results
 Crude birth rate 40 per 1000 per year
 Fastest growing nation
 Birth rate began to fall within months
 1970, it reached 20 per 1000 per year
 1989, it reached 16 per 1000 per year
 The same as in early 1960’s
 A Social System has resisted a policy

Policy Resistance: Romanian Birth Rates
o Alternative methods for birth control
o Smuggled contraceptive pills from
What
happened?
40
30
20
10
1966 1967 1968 1969 1970 1971
(Births/year/1000 people)
Crude Birth Rate
1971 1994
outside
o Health problems, led to increase in
death rate

Policy Resistance: Limits to Growth
Social Resistance
-
Birth Rate Population Death Rate
Birth
Regulation
Tax Incentive
& Propaganda
Spending in Health
Care Sector
+
+
-
+ +
-
-
Reinforcing Balancing
System Structure
Causal Loop Diagram
CLD

What are we looking for?
Events
Pattern of Behavior
System Structure
Reinforcing Balancing ?
Birth
Regulation
Incentive
Spending in Health
Care Sector
+
+
-
+ +
-
-

Structure …?
Systems Thinking Language
Feedback
Birth
Regulation
Tax Incentive
& Propaganda
Spending in Health
Care Sector
+
+
-
+ +
-
-
Reinforcing Balancing
Negative Feedback Loop
“Balancing”
Positive Feedback Loop
“Reinforcing”
Driving signals
Delay
Why there is problem? stop unwanted feedback loops
How to solve it …? trigger feedback loops

Anatomy and Behavior of
Complex Dynamic Systems
There are different mode of behavior, but also common

Anatomy and Behavior of Complex
Dynamic Systems: Exponential Growth

+
R
Birth Rate Population
+

+
Debt
Outstanding
Interest Due
+
R

Dynamic Systems: Goal Seeking
+
+

+
+
Competitors
-
+
Change in Price
Price
Price
+
Corrective
Actoins
+

+
Inventory
Level
+
-
+
Discrepancy
Desired
Inventory
+
Production
Rate
+

Dynamic Systems: Oscillation

Dynamic Systems: Oscillation
-
+
Discrepancy
Desired
Inventory
Inventory
Level
+
Production
Rate
+

Dynamic Systems: S-Shaped Growth
Inflection point: the
system shifts from
acceleration to
deceleration
+

Dynamic Systems: S-Shaped Growth
Inflection point: the
system shifts from
acceleration to
deceleration
+
R
+
+
+
Fractional Increase
Birth Rate (%)
-
Adequacy
Resource
Reserve (Food,
Energy, Cash)
+
+
B

Dynamic Systems: S-Shaped Growth with Overshoot

Dynamic Systems: S-Shaped Growth with Overshoot
+
R
+
+
+
Fractional Increase
Birth Rate (%)
-
Adequacy
Resource
Reserve (Food,
Energy, Cash)
+
+
B

Dynamic Systems: S-Shaped Growth with OSH &
Collapse
+
+

Dynamic Systems: S-Shaped Growth with OSH &
Collapse
+
R
+
+
+
Fractional Increase
Birth Rate (%)
+
-
Adequacy
Resource
+
Consumption of
The Reserve
-
Reserve (Food,
Energy, Cash)
+
+
B
B

What Actions to be taken (Intervention Points) to
avoid the Collapse?
+
R
+
+
+
Fractional Increase
Birth Rate (%)
+
-
Adequacy
Resource
+
Consumption of
The Reserve
-
Reserve (Food,
Energy, Cash)
+
+
B
B

What Actions to be taken (Intervention Points) to
avoid the Collapse?
+
R
+
+
+
Slow Down Actions,
Intervention Point (Short
Fractional Increase
Birth Rate (%)
+
-
Adequacy
Resource
+
Consumption of
The Reserve
B Refill the Reserve,
-
Reserve (Food,
Energy, Cash)
+
+
B
Intervention Point (Long
Term)
+
Term)
-

Business Context: Think Proactively
+
Business
(Customers)
Customers
Acquisition Rate
+ +
Business Development
Effort, Marketing, Sales
-
Resources Available
for Operations
+
Running Out of
Reserve
B Refill the Reserve
-
Reserve (HR, Assets,
Patents, Experience,
Cash)
+
+
R
B
(R&D, Banks, Investors)
+
Slow Down
Business
-

Management Paradigms
• Total Quality Management (TQM)
• Business Process Reengineering (BPR)
• Systems Thinking and Learning
Organization
• Project Management
• Six Sigma

What is a System?
 It is collection of parts that interact with one
another to function as a whole.
 Examples:
 Mechanical Systems (thermostat)
 Ecological Systems (population/food)
 Biological Systems (digestive, body
temp.)
 Social-economic systems (production)

Systems Thinking … !!!
 Systems Thinking is a paradigm and
methodology for dealing with complex situations
underlying business, economics, scientific, and social
systems.
 Systems Thinking views the organization as a
whole and focuses on interdependencies and links
between various departments, functions and divisions
and how they impact each other and the entire
organization. Complexity is a new paradigm drawing
from Relativity and Quantum theories.

Systems Thinking
 Systems thinking is the process of understanding
how things influence one another within a whole.
 In nature, systems thinking examples include
ecosystems in which various elements such as
air, water, movement, plants, and animals work
together to survive or perish.
 In organizations, systems consist of people,
structures, and processes that work together to
make an organization healthy or unhealthy.
71

Systems Thinking
 Systems thinking is crucial to problem
solving including Economic, Social,
Organizational
 No problem exists in isolation, all are part
of a larger system of interacting networks;
social networks, biogeophysical networks,
political networks, and economic
networks.
 We can’t understand the behavior of
system by studying its parts; we need to
72
study the whole thing. Copyright, 2014 | K. Wahba, PhD | DM-ST

Systems Thinking Scope
 Design of New Systems
 Re-engineering or Improvement of Existing
Systems
 Prediction of Behavior of Complex System
under varying Conditions
 Understanding the Interaction of
Components sub-Systems
 Strategy Development and Testing
 Scenario Modeling and Testing
 Group and Organizational Learning

Systems Thinking Scope
 National
 Macroeconomic, monetary policy
 Industrial/Sectoral
 Supply Chain Management
 Organizational
 Value Chain, Human Resource
 Individual
 Learning

Systems Thinking Benefits
 Systems thinking enables leaders and organizations:
 Effectively deal with uncertainty and complexity
 Foresee the consequences of their actions, policies and
strategies
 Identify fundamental causes and solutions to chronic
problems
 Avoid misjudging problem symptoms for their causes
 Reconcile dilemma of short-term fixes vs long terms
strategies
 Resolve endemic staff morale and productivity problems
 Bring alignment of vision and action to teams and groups
 Become a Living and Learning Organization

Systems Thinking as Language
 Systems Thinking provides a tool for
understanding complexity and dynamic decision
making
 The Language …
 Is visual and diagrammatic (CLD)
 Has a set of precise rules
 Translates perceptions into explicit pictures
 Emphasizes closed interdependencies (circular
language)

Systems Thinking Process
1. Problem Articulation
(Boundary Selection)
5. Policy
Formulation
& Evaluation
2. Dynamic
Hypothesis
4. Testing 3. Formulation

Causal Loop Modeling
Causal Loop Diagram CLD
+
Business
(Customers)
Customers
Acquisition Rate
+ +
Business Development
Effort, Marketing, Sales
-
Resources Available
for Operations
+
Running Out of
Reserve
B Refill the Reserve
-
Reserve (HR, Assets,
Patents, Experience,
Cash)
+
+
R
B
(R&D, Banks, Investors)
+
Slow Down
Business
-

Causal Loop Modeling
 According to P. Senge, 1990
 Causal Loop Diagram provides “… a framework
for seeing interrelationships rather than things,
for seeing patterns of change rather than static
snapshots”
 Causal Loop Diagram (CLD) is a tool for
revealing the casual relationships among a
set of variables (factors) operating in a
complex system

Leverage versus Solution
 System Thinking is not about Problem
Solving Methodology (by product)
 Problem is not isolated and unstructured
 Short term, local, optimal, neat, content,
symptoms
 Leverage
 It refers to actions or interventions that can
have a lasting impact on the system,
 Long term, global, fundamental, realistic,
content and context, cause

Systems Archetypes
 Systems Archetypes are generic systems
models or templates that represent a wide range
of situations
 It provides a high-level map of dynamic
processes
 It is the Communalities – SD Group, MIT
 They are more than 8+

Systems Archetypes
 Fixes that Fail
 Short term and solve the symptoms rather than the
problem and is associated with side effects
+
+
+
+
-
-

Systems Archetypes
 Shifting the Burden
 Helen Keller
 Parent Dependency
 Nanny Teaching, helping
+
+
+
-
-
-

Systems Archetypes
 Limits to Success (Growth)
 People Express – No. of Passengers, Revenue,
QoS
+
+
+
-
-

Systems Archetypes
85
 Tragedy of the
Commons
 Common-sense
 Cars per family and
traffic problem,
convenience
+
+
+
+
+
+
+
+
+
-

Textbooks and Software
Business Dynamics: Systems Thinking and Modeling for a Complex
World, John D. Sterman, www.mhhe.com 2000, by the McGraw-Hill
Companies, Inc. ISBN 0-07-231135-5.
Systems Thinking, and Modelling, Understanding Change and
Complexity, Kambiz E. Maani, Robert Y. Cavana, 2nd Ed., 2008, Prentice
Hall,
The Art of Systems Thinking, Essential Skills for Creativity and Problem
Solving, Joseph O’Connor and Ian McDermott, 1997, Thorsons, ISBN 0-
7225-3442-6.
Software
Ithink, http://www.iseesystems.com/
Vensim, http://www.vensim.com
Powersim, http://www.powersimsolutions.com/
86

BIZ Days
Romania – Bucharest
12 November 2014
87
Thank You
Decision Making in Complex Context
Using Systems Thinking Paradigm
Khaled Wahba, PhD | kwahba@msmromania.org
Adjunct Associate Professor | MSM Romania & the
Netherlands

Khaled wahba msm

Recommandé

Recommandé

Contenu connexe

Plus de RevistaBiz

Plus de RevistaBiz (20)

Dernier

Dernier (20)

Khaled wahba msm