Reductionism and holism are two approaches to problem solving. Reductionism breaks problems down into components while holism considers the whole system and interactions between parts. Some complex systems like the brain, society, and chaotic systems cannot be understood through reductionism alone due to emergent and ambiguous properties. These systems require a holistic approach. Soft sciences like biology and philosophy often deal with more complex systems than hard sciences and are therefore more difficult to make predictions about. The type of problem, its complexity level, and the degree of prediction possible determine whether a reductionist or holistic approach is best.

1. Jun 2007
How to Approach Hard vs. Soft Problems
Two problem solving approaches: Holism vs. Reductionism

2. Let’s preface this discussion by asking a fundamental question
What is Intelligence? What is it used for?

3. The purpose of intelligence is for prediction
● Intelligence is for prediction
● Prediction is a low level operation in the brain
● Prediction not logic is most important
Many complex systems including entrepreneurial ventures and
creating hit entertainment products require prediction as a
fundamental skill set to achieve success

4. Throughout history two fundamental approaches to understand science and the
world around us have been used: Reductionism and Holism
Reductionism Holism
● Parts, Division ● Context, Whole, Environment
● Math, Physics, Computer Science ● Biology, Ecology, Philosophy
● Programmers, Surgeons, Engineers ● Nurses, Authors, Philosophers
● Proof, Precise Measurement, Prediction ● Categories, Description, Speculation
Today we live in a world ruled by Reductionism and Reductionist
scientific approaches
Reductionism vs. Holism

5. Reductionism focuses on Component Dominated Complexity
Reductionist Approach to Complex Systems
System
Component 2 Component 3
Sub-Component
Sub-Component
Sub-Component
Solution for System Complexity
● Manage complexity through division
● DIvide the system into components
● Create simple interfaces between components
Component 1

6. Holism on the other hand, focuses on Interaction Dominated Complexity
Holistic Approach to Complex Systems
Examples
● Neurons in the brain
● People in society
● Concepts, abstractions,
ideas

7. Chaotic Systems
Chaotic Systems and Reductionism
● Stateful components
● Non-linear components
● Interaction dominated
complexity
● Chaotic systems are
common in life
● Non-divisible complexity
● Can’t use reductionist
science for prediction
Chaotic Systems Characteristics Key Insights

8. Ambiguity in Systems
Overview
➢ Incomplete information
➢ Self reference, loops
➢ Chicken and the egg problem
➢ Incorrect information
○ Lies, misunderstandings
○ Multiple points of view, opinions
○ Persuasion

9. Irreducible Complexity in Systems
Overview
➢ Emergent properties
➢ Everything matters
○ Internally: Curse of Dimensionality
○ Externally: Can’t separate “system” from environment

10. PROCESS
Complex Systems that defy Reductionism
1. Chaotic
1. Contain Ambiguity
1. Irreducible Complexity
1. Require a Holistic Stance
We have described four kinds of complex systems that defy Reductionism and
are unpredictable relative to reductionist approaches

11. Soft sciences are more difficult because soft science tends to deal with more
complex systems than hard science does
Overview
➢ Soft science cannot make as good prediction as hard sciences because
they have to deal with life
➢ Life is bizarre
➢ Reductionist (Hard) science cannot deal with bizarre systems
➢ Reductionist success comes from limiting their problem down to non-
bizarre systems

12. We can express various classes of problems based on the amount of
complexity of the system and the range of prediction possible
Complexity and Prediction

13. Examples of Bizarre Systems
➢ Entrepreneurial ventures / Venture capital
➢ Language translation
➢ Weather
➢ Stock markets
➢ Human interest / intent / recommendations
➢ Internet search
➢ Hit mobile game design & development
➢ Etc., etc.

14. Today Reductionist science has solved a major class of problems in the
Complexity/Prediction graph
Complexity and Prediction

15. Key Takeaway: Different classes of problems require different approaches to
solve!
Complexity vs Prediction Problem Classes