Inquiry Into Inquiry

IAIASATSIMBIN

Posted on September 23, 2022 by Jon Awbrey

I’m Always In A State Adjacent To States I May Be In Next

cc: FB | Occupy Chance

Posted in Acronym, Mantra, Modality, Time | Tagged Acronym, Mantra, Modality, Time | Leave a comment

Theory and Therapy of Representations • 4

Posted on September 17, 2022 by Jon Awbrey

Re: Theory and Therapy of Representations • 3
Re: Ontolog Forum • Paola Di Maio

JA:: What are the forces distorting our representations of what’s observed, what’s expected, and what’s intended?

PDM:: The short answer is …. the force behind all distortions is our own unenlightened mind, and all the shortfalls this comes with.

I think that’s true, we have to keep reflecting on the state of our personal enlightenments. If we can do that without losing our heads and our systems thinking caps, there will be much we can do to promote the general Enlightenment of the State.

On both personal and general grounds we have a stake in the projects of self‑governing systems — whether it is possible for them to exist and what it takes for them to thrive in given environments. Systems on that order have of course been studied from many points of view and at many levels of organization. Whether we address them under the names of adaptive, cybernetic, error-correcting, intelligent, or optimal control systems they all must be capable to some degree of learning, reasoning, and self‑guidance.

Resources

cc: Conceptual Graphs • Cybernetics • Laws of Form • Ontolog Forum
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Posted in Accountability, Adaptive Systems, C.S. Peirce, Cybernetics, Democracy, Economics, Education, Expectation, Governance, Information, Inquiry, Intention, Justice, Law, Logic, Max Weber, Observation, Plato, Pragmata, Representation, Science, Semiotics, Society, Statistics, Systems Theory | Tagged Accountability, Adaptive Systems, C.S. Peirce, Cybernetics, Democracy, Economics, Education, Expectation, Governance, Information, Inquiry, Intention, Justice, Law, Logic, Max Weber, Observation, Plato, Pragmata, Representation, Science, Semiotics, Society, Statistics, Systems Theory | 5 Comments

Theory and Therapy of Representations • 3

Posted on September 16, 2022 by Jon Awbrey

Representation is a concept we find at the intersection of cybernetics, epistemology, logic, mathematics, psychology, and sociology. In my studies it led me from math to psych and back again, with sidelong glances at the history of democratic governance. Its time come round again, I find myself returning to the scenes of two recurring questions.

Scene 1. Pragmatic Truth • Discussion 18

We do not live in axiom systems. We do not live encased in languages, formal or natural. There is no reason to think we will ever have exact and exhaustive theories of what’s out there, and the truth, as we know, is “out there”. Peirce understood there are more truths in mathematics than are dreamt of in logic — and Gödel’s realism should have put the last nail in the coffin of logicism — but some ways of thinking just never get a clue.

That brings us to Question 1 —

What are formalisms and all their embodiments in brains and computers good for?

Scene 2. Theory and Therapy of Representations • 1

Statistics were originally the data a ship of state needed for stationkeeping and staying on course. The Founders of the United States, like the Cybernauts of the Enlightenment they were, engineered a ship of state with checks and balances and error-controlled feedbacks for the sake of representing both reality and the will of the people. In that connection Max Weber saw how a state’s accounting systems are intended as representations of realities its crew and passengers must observe or perish.

That brings us to Question 2 —

What are the forces distorting our representations of what’s observed, what’s expected, and what’s intended?

Resources

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Theory and Therapy of Representations • 2

Posted on September 14, 2022 by Jon Awbrey

December 19, 2011

In a complex society, people making decisions and taking actions at places remote from you have the power to affect your life in significant ways. Those people govern your life, they are your government, no matter what spheres of influence they inhabit, private or public. The only way you get a choice in that governance is if there are paths of feedback permitting you to affect the life of those decision makers and action takers in significant ways. That is what accountability, response-ability, and representative government are all about.

Naturally, some people are against that.

In the United States there has been a concerted campaign for as long as I can remember — but even more concerted since the Reagan Regime — to get the People to abdicate their hold on The Powers That Be and just let some anonymous corporate entity send us the bill after the fact. They keep trying to con the People into thinking they can starve the beast, to limit government, when what they are really doing is feeding the beast of corporate control, weakening their own power over the forces that govern their lives.

That is the road to perdition as far as responsible government goes. There is not much of anything one leader or one administration can do unsupported if the People do not constantly demand a government of, by, and for the People.

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Theory and Therapy of Representations • 1

Posted on September 13, 2022 by Jon Awbrey

Again, in a ship, if a man were at liberty to do what he chose, but were devoid of mind and excellence in navigation (αρετης κυβερνητικης), do you perceive what must happen to him and his fellow sailors?

Plato • Alcibiades • 135 A

Re: Michael Harris • Mathematical Literacy and the Good Society

Statistics were originally the data a ship of state needed for stationkeeping and staying on course. The Founders of the United States, like the Cybernauts of the Enlightenment they were, engineered a ship of state with checks and balances and error‑controlled feedbacks for the sake of representing both reality and the will of the people. In that connection Max Weber saw how a state’s accounting systems are intended as representations of realities its crew and passengers must observe or perish.

The question for our time is —

What are the forces distorting our representations of what’s observed, what’s expected, and what’s intended?

Repercussions

The Place Where Three Wars Meet

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Survey of Cybernetics • 2

Posted on September 8, 2022 by Jon Awbrey

Plato • Alcibiades • 135 A

This is a Survey of blog posts relating to Cybernetics. It includes the selections from Ashby’s Introduction and the comment on them I’ve posted so far, plus two series of reflections on the governance of social systems in light of cybernetic and semiotic principles.

Ashby’s Introduction to Cybernetics

Chapter 10 • Regulation In Biological Systems

Selections • (1) • (2) • (3) • (4) • (5) • (6) • (7) • (8) • (9) • (10)

Discussions • (1) • (2) • (3) • (4)

Chapter 11 • Requisite Variety

Selections • (11) • (12) • (13)

Blog Series

Basal Ingredients Of Society (BIOS)

Prologue • Prescription

Comments • (1) • (2) • (3) • (4) • (5) • (6) • (7)

Theory and Therapy of Representations • (1) • (2) • (3) • (4) • (5)

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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 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 | 3 Comments

Theory and Therapy of Representations • 5

Posted on September 5, 2022 by Jon Awbrey

Re: R.J. Lipton and K.W. Regan • Legal Complexity

I do not pretend to understand the moral universe;
the arc is a long one, my eye reaches but little ways;
I cannot calculate the curve and complete the figure by
the experience of sight; I can divine it by conscience.
And from what I see I am sure it bends towards justice.

🙞 Theodore Parker

The arc of the moral universe may bend toward justice — there’s hope it will.
For the logic of laws to converge on justice may take some doing on our part.

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Posted in Cybernetics, Democracy, Economics, Education, Expectation, Governance, Information, Inquiry, Intention, Justice, Law, Logic, Observation, Plato, Representation, Science, Semiosis, Semiotics, Society, Statistics, Virtue | Tagged Cybernetics, Democracy, Economics, Education, Expectation, Governance, Information, Inquiry, Intention, Justice, Law, Logic, Observation, Plato, Representation, Science, Semiosis, Semiotics, Society, Statistics, Virtue | 2 Comments

Theme One Program • Jets and Sharks 3

Posted on September 3, 2022 by Jon Awbrey

Re: Theme One Program • Jets and Sharks • (1) • (2)

Example 5. Jets and Sharks (cont.)

Given a representation of the Jets and Sharks universe in computer memory, we naturally want to see if the memory serves to supply the facts a well-constructed data base should.

In their PDP Handbook presentation of the Jets and Sharks example, McClelland and Rumelhart suggest several exercises for the reader to explore the performance of their neural pool memory model on the tasks of retrieval and generalization (Exercise 2.1).

Using cactus graphs or minimal negations to implement pools of mutually inhibitory neurons lends itself to neural architectures on a substantially different foundation from the garden variety connectionist models. At a high level of abstraction, however, there is enough homology between the two orders to compare their performance on many of the same tasks. With that in mind, I tried Theme One on a number of examples like the ones suggested by McClelland and Rumelhart.

What follows is a brief discussion of two examples as given in the original User Guide. Next time I’ll fill in more details about the examples and discuss their bearing on the larger issues at hand.

With a query on the name “ken” we obtain the following output, giving all the features associated with Ken.

$\text{Jets and Sharks} \stackrel{_\bullet}{} \text{Query 1}$

With a query on the two features “college” and “sharks” we obtain the following outline of all features satisfying those constraints.

$\text{Jets and Sharks} \stackrel{_\bullet}{} \text{Query 2}$

From this we discover all college Sharks are 30‑something and married. Further, we have a complete listing of their names broken down by occupation.

To be continued …

References

McClelland, J.L. (2015), Explorations in Parallel Distributed Processing : A Handbook of Models, Programs, and Exercises, 2nd ed. (draft), Stanford Parallel Distributed Processing Lab. Online, Section 2.3, Figure 2.1.
McClelland, J.L., and Rumelhart, D.E. (1988), Explorations in Parallel Distributed Processing : A Handbook of Models, Programs, and Exercises, MIT Press, Cambridge, MA. “Figure 1. Characteristics of a number of individuals belonging to two gangs, the Jets and the Sharks”, p. 39, from McClelland (1981).
McClelland, J.L. (1981), “Retrieving General and Specific Knowledge From Stored Knowledge of Specifics”, Proceedings of the Third Annual Conference of the Cognitive Science Society, Berkeley, CA.

Resources

cc: Conceptual Graphs • Cybernetics • Laws of Form • Ontolog Forum
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Posted in Algorithms, Animata, Artificial Intelligence, Boolean Functions, C.S. Peirce, Cactus Graphs, Cognition, Computation, Constraint Satisfaction Problems, Data Structures, Differential Logic, Equational Inference, Formal Languages, Graph Theory, Inquiry Driven Systems, Laws of Form, Learning Theory, Logic, Logical Graphs, Mathematics, Minimal Negation Operators, Painted Cacti, Peirce, Propositional Calculus, Semiotics, Spencer Brown, Visualization | Tagged Algorithms, Animata, Artificial Intelligence, Boolean Functions, C.S. Peirce, Cactus Graphs, Cognition, Computation, Constraint Satisfaction Problems, Data Structures, Differential Logic, Equational Inference, Formal Languages, Graph Theory, Inquiry Driven Systems, Laws of Form, Learning Theory, Logic, Logical Graphs, Mathematics, Minimal Negation Operators, Painted Cacti, Peirce, Propositional Calculus, Semiotics, Spencer Brown, Visualization | 3 Comments

Theme One Program • Jets and Sharks 2

Posted on August 30, 2022 by Jon Awbrey

Re: Theme One Program • Jets and Sharks • (1)

Example 5. Jets and Sharks (cont.)

As we saw last time, Theme One reads the text file shown below and constructs a cactus graph data structure in computer memory. The cactus graph represents a single logical formula in propositional calculus and that proposition embodies the logical constraints defining the Jets and Sharks data base.

$\text{Jets and Sharks} \stackrel{_\bullet}{} \text{Log File}$

Our cactus graph incorporates a vocabulary of 41 logical terms, each of which represents a boolean variable, so the proposition in question, call it $``q",$ is a boolean function of the form $q : \mathbb{B}^{41} \to \mathbb{B}.$ Given $2^{41} = 2,199,023,255,552$ we know a truth table for $q$ takes over two trillion rows and a venn diagram for $q$ takes the same number of cells. Topping it off, there are $2^{2^{41}}$ boolean functions of the form $f : \mathbb{B}^{41} \to \mathbb{B}$ and $q$ is just one of them.

Measures of strategy are clearly needed to negotiate patches of cacti like those.

To be continued …

References

McClelland, J.L. (2015), Explorations in Parallel Distributed Processing : A Handbook of Models, Programs, and Exercises, 2nd ed. (draft), Stanford Parallel Distributed Processing Lab. Online, Section 2.3, Figure 2.1.
McClelland, J.L., and Rumelhart, D.E. (1988), Explorations in Parallel Distributed Processing : A Handbook of Models, Programs, and Exercises, MIT Press, Cambridge, MA. “Figure 1. Characteristics of a number of individuals belonging to two gangs, the Jets and the Sharks”, p. 39, from McClelland (1981).
McClelland, J.L. (1981), “Retrieving General and Specific Knowledge From Stored Knowledge of Specifics”, Proceedings of the Third Annual Conference of the Cognitive Science Society, Berkeley, CA.

Resources

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Theme One Program • Jets and Sharks 1

Posted on August 25, 2022 by Jon Awbrey

It is easy to spend a long time on the rudiments of learning and logic before getting down to practical applications — but I think we’ve circled square one long enough to expand our scope and see what the category of programs envisioned in Theme One can do with more substantial examples and exercises.

During the development of the Theme One program I tested successive implementations of its Reasoning Module or Logical Modeler on appropriate examples of logical problems current in the literature of the day. The PDP Handbook of McClelland and Rumelhart set one of the wittiest gems ever to whet one’s app‑titude so I could hardly help but take it on. The following text is a light revision of the way I set it up in the program’s User Guide.

Example 5. Jets and Sharks

The propositional calculus based on the minimal negation operator can be interpreted in a way resembling the logic of activation states and competition constraints in one class of neural network models. One way to do this is to interpret the blank or unmarked state as the resting state of a neural pool, the bound or marked state as its activated state, and to represent a mutually inhibitory pool of neurons $A, B, C$ by the proposition $\texttt{(} A \texttt{,} B \texttt{,} C \texttt{)}.$ The manner of representation may be illustrated by transcribing a well-known example from the parallel distributed processing literature (McClelland and Rumelhart 1988) and working through a couple of the associated exercises as translated into logical graphs.

Displayed below is the text expression of a traversal string which Theme One parses into a cactus graph data structure in computer memory. The cactus graph represents a single logical formula in propositional calculus and this proposition embodies all the logical constraints defining the Jets and Sharks data base.

$\text{Jets and Sharks} \stackrel{_\bullet}{} \text{Log File}$

To be continued …

References

McClelland, J.L. (2015), Explorations in Parallel Distributed Processing : A Handbook of Models, Programs, and Exercises, 2nd ed. (draft), Stanford Parallel Distributed Processing Lab. Online, Section 2.3, Figure 2.1.
McClelland, J.L., and Rumelhart, D.E. (1988), Explorations in Parallel Distributed Processing : A Handbook of Models, Programs, and Exercises, MIT Press, Cambridge, MA. “Figure 1. Characteristics of a number of individuals belonging to two gangs, the Jets and the Sharks”, p. 39, from McClelland (1981).
McClelland, J.L. (1981), “Retrieving General and Specific Knowledge From Stored Knowledge of Specifics”, Proceedings of the Third Annual Conference of the Cognitive Science Society, Berkeley, CA.

Resources

cc: Conceptual Graphs • Cybernetics • Laws of Form • Ontolog Forum
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Ashby’s Introduction to Cybernetics

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