Pragmatic Traction • 6

Re: Peirce List DiscussionGFJFS

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 mathematics to the normative sciences and metaphysics.


Peirce Syllabus

Normative science rests largely on phenomenology and on mathematics;
metaphysics on phenomenology and on normative science.

— Charles Sanders Peirce, Collected Papers, CP 1.186 (1903)
Syllabus : Classification of Sciences (CP 1.180–202, G-1903-2b)

I find this “two-footed, thrice-braced” stance has many advantages over the “dufflepud” attempt to stand logic on phenomenology alone.

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 , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

Sign Relational Manifolds : 6

Semiotic Orbits, Manifolds, Arcs (SOMA)

The arc of the semiotic universe is long but it bends towards universal harmony.

Re: Facebook DiscussionWhat’s at the End of a Chain of Interpretants?

Semiotic manifolds, like physical and mathematical manifolds, may be finite and bounded or infinite and unbounded but they may also be finite and unbounded, having no boundary in the topological sense.  So unbounded semiosis does not imply infinite semiosis.

Here are two points in previous discussions where the question of infinite semiosis came up.

Resource

Posted in C.S. Peirce, Cybernetics, Differential Logic, Geometry, Logic, Manifolds, Mathematics, Peirce, Peirce List, Riemann, Semeiosis, Semiosis, Semiotics, Sign Relational Manifolds, Sign Relations, Triadic Relations | 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 be developed later …

Pragmatism makes thinking to consist in the living inferential metaboly of symbols whose purport lies in conditional general resolutions to act.  (Peirce, CP 5.402 n. 3).

Such reasonings and all reasonings turn upon the idea that if one exerts certain kinds of volition, one will undergo in return certain compulsory perceptions.  Now this sort of consideration, namely, that certain lines of conduct will entail certain kinds of inevitable experiences is what is called a “practical consideration”.  Hence is justified the maxim, belief in which constitutes pragmatism;  namely:

In order to ascertain the meaning of an intellectual conception one should consider what practical consequences might conceivably result by necessity from the truth of that conception;  and the sum of these consequences will constitute the entire meaning of the conception.  (Peirce, CP 5.9, 1905).

Reference

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 MaclarenStatistics 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 some idea —

The program integrated a sequential learning module and a propositional reasoning module that I thought of as The Empiricist and The Rationalist, respectively.

The learning module was influenced by ideas from the psychologists Thorndike and Guthrie and the statisticians Fisher and Tukey.  The reasoning module made use of ideas about logical graphs from C.S. Peirce.  There is a kind of phase transition as we pass from finite state adaptation covered by the learning module to context-free hypothesis generation covered by the reasoning module, but it happens that some aspects of the latter are already anticipated in the former.

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

Pragmatic Traction • 3

Re: Deborah G. MayoRevisiting Popper’s Demarcation of Science

I think Peirce would say that any struggle to pass from the irritation of doubt toward the settlement of belief is a form of inquiry — it’s just that some forms work better than others over the long haul.  Instead of a demarcation Peirce describes a spectrum of methods, graded according to their measure of success in achieving the aim of inquiry.

Posted in Abduction, C.S. Peirce, Control, Cybernetics, Deborah G. Mayo, 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, 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

Pragmatic Traction • 2

Re: Facebook DiscussionRichard Saunders

I’m about to be diverted for a couple of weeks but this is an ever-ongoing question so I know I’ll be coming back to it again.  The short shrift goes a bit like this —

The gist of the idea that Peirce dubbed the pragmatic maxim is really a mathematical principle that has always been hard to render in ordinary language, largely because of the Procrustean subject-predicate embedding that most of the languages we know and love impose on its core structure.  The primal form is more like one of those bistable gestalts — duck-rabbit, Necker cube, old-young woman, etc.  One way to get a mental handle on the matter is to mull over the many variations on its underlying theme, such as the ones I quoted and discussed in my blog post —

Posted in Abduction, C.S. Peirce, Control, Cybernetics, Deborah G. Mayo, 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, 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

Pragmatic Traction • 1

Re: Deborah G. MayoPeircean Induction and the Error-Correcting Thesis

C.S. Peirce’s pragmatic maxim marks the place where the tire of theory meets the test track of experience — it tells us how general ideas are impacted by practical consequences.  If our concept of an object is the sum of its conceivable practical effects then the truth of a concept can be defeated by single outcome outside the sum.

Posted in Abduction, C.S. Peirce, Control, Cybernetics, Deborah G. Mayo, 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, 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