how to approach hard and soft problems
TRANSCRIPT
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Jun 2007
How to Approach Hard vs. Soft ProblemsTwo problem solving approaches: Holism vs. Reductionism
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Let’s preface this discussion by asking a fundamental question
What is Intelligence? What is it used for?
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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
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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
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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
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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
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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
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Ambiguity in Systems
Overview
➢ Incomplete information
➢ Self reference, loops
➢ Chicken and the egg problem
➢ Incorrect information○ Lies, misunderstandings○ Multiple points of view, opinions○ Persuasion
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Irreducible Complexity in Systems
Overview
➢ Emergent properties
➢ Everything matters○ Internally: Curse of Dimensionality○ Externally: Can’t separate “system” from environment
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PROCESS
Complex Systems that defy Reductionism
1. Chaotic
2. Contain Ambiguity
3. Irreducible Complexity
4. Require a Holistic Stance
We have described four kinds of complex systems that defy Reductionism and are unpredictable relative to reductionist approaches
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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
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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
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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.
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Today Reductionist science has solved a major class of problems in the Complexity/Prediction graph
Complexity and Prediction
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Key Takeaway: Different classes of problems require different approaches to solve!
Complexity vs Prediction Problem Classes