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How to approach hard and soft problems

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Emergence Theory

Publicada em: Tecnologia
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How to approach hard and soft problems

  1. 1. Jun 2007 How to Approach Hard vs. Soft Problems Two problem solving approaches: Holism vs. Reductionism
  2. 2. Let’s preface this discussion by asking a fundamental question What is Intelligence? What is it used for?
  3. 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. 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. 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. 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. 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. 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. 9. Irreducible Complexity in Systems Overview ➢ Emergent properties ➢ Everything matters ○ Internally: Curse of Dimensionality ○ Externally: Can’t separate “system” from environment
  10. 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. 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. 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. 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. 14. Today Reductionist science has solved a major class of problems in the Complexity/Prediction graph Complexity and Prediction
  15. 15. Key Takeaway: Different classes of problems require different approaches to solve! Complexity vs Prediction Problem Classes

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