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Understanding Representations and Compressions: Balancing Opportunity and Risk

Explore the concept of representations and compressions in knowledge acquisition, discussing their role in reliable prediction and attunement with affordances. Distinguish between models based on representations and compressions, and understand the importance of context in decision-making.

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Understanding Representations and Compressions: Balancing Opportunity and Risk

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  1. Representations and Compressions Opportunity/Risk from Heuristic ShortcutsMichael LissackInstitute for the Study of Coherence and Emergence

  2. “Knowledge is not a matter of getting reality right, … but rather a matter of acquiring habits of action for coping with reality” Richard Rorty

  3. Representations • The action or fact of one item standing for another. • The substitution of an individual or class in place of another(indexicals) Representations are labels and sign tokens of membership in a predefined category

  4. Compressions • Compression is the reduction in size of data in order to save space or transmission time. • Compressions can be either lossy {some information is permanently lost) or lossless (all information can be restored). Compressions are stories, models, narratives which allow the user to ask “what if”

  5. What is the Problem? Models based on representations are concerned with reliable prediction Models based on compressions are concerned with attaining better attunement with affordances These goals are NOT the same But all too often the models are confused

  6. “Everything should be made as simple as possible. … But not simpler. Albert Einstein

  7. Ontology Science I Science 2 Complex Simple Chaotic Complicated Emergence Reflexive Anticipation Will

  8. Science I Simple Focus is on Description Deduction Complicated Focus is on Reliable Prediction Induction via Probabilistic Inference

  9. Science II Complex Focus is on Sagacity(Preparedness)/ Resilience/ Robustness Abduction Chaotic Focus is on Pattern Recognition/Identity Assertion Assert Identity

  10. The degree of complexity present is the degree to which our chosen method of reduction has failed

  11. Challenges to the Representation Model • Anticipation • Action • Attention • Affordances   • Experience • Learning

  12. Takeaways Codes are NOT CuesLabels are NOT StoriesHumans are NOT AlgorithmsRepresentations are NOT narratives Context Matters Efficiency can be the enemy of Resilience

  13. http://isce.eduhttp://isce.edu/mbr.pdf

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