Tag Archives: Control

Where Is Fancy Bred? • Comment 1

Re: Artem Kaznatcheev • Labyrinth : Fitness Landscapes As Mazes, Not Mountains A species in progress, with its naturally evolved organs of sensitivity, effectivity, and discernment, in its trials to learn the properties of its environment, cannot be expected to … Continue reading

Posted in Adaptive Systems, Analogy, Artem Kaznatcheev, Artificial Intelligence, Biological Systems, Communication, Computational Complexity, Control, Evolution, Fitness Landscapes, Imagination, Information, Inquiry, Inquiry Driven Systems, Learning Theory, Mathematical Models, Mental Models, Natural Intelligence, Semiotics, Sign Relations | Tagged , , , , , , , , , , , , , , , , , , , | Leave a comment

Survey of Semiotic Theory Of Information • 3

This is a Survey of previous blog and wiki posts on the Semiotic Theory Of Information.  All my projects are exploratory in essence but this line of inquiry is more open-ended than most.  The question is: What is information and how … Continue reading

Posted in Abduction, C.S. Peirce, Communication, Control, Cybernetics, Deduction, Determination, Discovery, Doubt, Epistemology, Fixation of Belief, Induction, Information, Information = Comprehension × Extension, Information Theory, Inquiry, Inquiry Driven Systems, Inquiry Into Inquiry, Interpretation, Invention, Knowledge, Learning Theory, Logic, Logic of Relatives, Logic of Science, Mathematics, Peirce, Philosophy, Philosophy of Science, Pragmatic Information, Probable Reasoning, Process Thinking, Relation Theory, Scientific Inquiry, Scientific Method, Semeiosis, Semiosis, Semiotic Information, Semiotics, Sign Relational Manifolds, Sign Relations, Surveys, Triadic Relations, Uncertainty | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

Constraints and Indications : 2

Re: Ontolog Forum • JS Coping with collaboration, communication, context, integration, interoperability, perspective, purpose, and the reality of the information dimension demands a transition from conceptual environments bounded by dyadic relations to those informed by triadic relations, especially the variety … Continue reading

Posted in Adaptive Systems, Artificial Intelligence, Ashby, C.S. Peirce, Constraint, Control, Cybernetics, Determination, Error-Controlled Regulation, Feedback, Indication, Indicator Functions, Information, Inquiry, Inquiry Driven Systems, Intelligent Systems, Intentionality, Learning Theory, Peirce, Semiotic Information, Semiotics, Systems Theory, Uncertainty | Tagged , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

Pragmatic Traction • 7

Re: Peirce List Discussion • John Sowa It’s good to remember that observation, perception itself, has an abductive character in Peirce’s analysis and induction for him is more a final testing than initial conception stage.  Yes, it’s wheels upon wheels … Continue reading

Posted in Abduction, Action, C.S. Peirce, Control, Cybernetics, Deduction, Definition, Determination, Fixation of Belief, Induction, Inference, Information, Inquiry, Inquiry Driven Systems, Learning, Learning Theory, Logic, Logic of Science, Mathematics, Metaphysics, Normative Science, Observation, Peirce, Peirce's Categories, Perception, Phenomenology, Philosophy, Pragmatic Maxim, Pragmatism, Recursion, Scientific Method, Semiotics, Volition | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

Pragmatic Traction • 6

Re: Peirce List Discussion • GF • JFS When it comes to the relative contributions of phenomenology and mathematics to logic, I always find myself returning to the picture I drew once before from Peirce’s Syllabus, on the relationship of phenomenology and … Continue reading

Posted in C.S. Peirce, Control, Cybernetics, Definition, Determination, Fixation of Belief, Information, Inquiry, Inquiry Driven Systems, Logic, Logic of Science, Mathematics, Metaphysics, Normative Science, Peirce, Peirce's Categories, Phenomenology, Philosophy, Pragmatic Maxim, Pragmatism, Scientific Method, Semiotics, Volition | Tagged , , , , , , , , , , , , , , , , , , , , , , | 1 Comment

Pragmatic Traction • 5

☯  TAO  ☯ Trials And Outcomes Expression | Impression Effectors | Receptors Exertion | Reaction Conduct | Bearing Control | Observe Effect | Detect Poke | Peek Note | Note Pat | Apt | Tap Just a few notes to … Continue reading

Posted in Abduction, C.S. Peirce, Control, Cybernetics, Deduction, Error, Error-Controlled Regulation, Feedback, Fixation of Belief, Hypothesis, Induction, Inference, Information, Information Theory, Inquiry, Inquiry Driven Systems, Knowledge, Knowledge Representation, Learning, Learning Theory, Likelihood, Logic, Logic of Science, Logical Graphs, Peirce, Philosophy, Philosophy of Science, Pragmatic Information, Pragmatic Maxim, Pragmatism, Probability, Probable Reasoning, Scientific Inquiry, Scientific Method, Semiotics, Statistical Inference, Statistics, Uncertainty, Volition | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

Pragmatic Traction • 4

Re: Oliver Maclaren • Statistics Without True Models Or Hypothesis Testing I once wrote a “pure empiricist” sequential learning program that took this sort of approach to the data in its input stream. Here is the manual, that will give … Continue reading

Posted in Abduction, C.S. Peirce, Control, Cybernetics, Deduction, Error, Error-Controlled Regulation, Feedback, Fixation of Belief, Hypothesis, Induction, Inference, Information, Information Theory, Inquiry, Inquiry Driven Systems, Knowledge, Knowledge Representation, Learning, Learning Theory, Likelihood, Logic, Logic of Science, Logical Graphs, Peirce, Philosophy, Philosophy of Science, Pragmatic Information, Pragmatic Maxim, Pragmatism, Probability, Probable Reasoning, Scientific Inquiry, Scientific Method, Semiotics, Statistical Inference, Statistics, Uncertainty | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment