The Phaneron

It was years in the making, but I finally made it to the New England Complex Systems Institute last week for CX201 - Complex Physical, Biological and Social Systems. My interest in complex systems started in or around 2000 when I was living near Cambridge and stumbled upon NECSI. At that time there was an active discussion of Peirce’s firstness, secondness and thirdness on the NECI listserv and this became my introduction to Peirce’s philosophy.

The science of complex systems provides clarity regarding the meaning of terms now coming into use as memes in the social media and executive leadership communities. For example, complex systems grounds the term collective intelligence, a term currently used at the Aspen Institute to denote the “co-creation of value”, as the behavior of the whole that one cannot observe from the behavior of the parts. As social networks become a larger part of our worklife, it’s essential that the social media community ground  its memes in the science of complex systems.

Last week’s lectures were based on Yaneer’s Dynamics of Complex Systems and Making Things Work. Yaneer made an excellent choice by insisting that laptops went down during the lectures, so we were all engaged in the experience rather than intermediated by technology. I enjoyed getting to know Yaneer over the course if the week. He’s a consumate scientist, teacher and leader. My experience there was of the highest quality and I plan to return next year for CX202 - Complex Systems Modeling and Networks by which time I hope to have some Haskell chops and be ready to execute on the modeling phase of the curriculum.

A few days ago our team reviewed some ongoing work in which we’re developing a better approach to sharing information. Today, model driven architecture and the semantic web are widely accepted approaches to sharing information. Despite the acceptance of these approaches, their underlying model theory is not well understood as was evident from our review. So, I’ll spend the next few posts (or more)

explaining truth and meaning in model theory. I’ll explain why it’s important to differentiate truth from meaning, so you can better understand claims made about the semantic web and model driven architecture as approaches to sharing information. The semantic web has a formal model theory defined here. Giving them them the benefit of the doubt, the model driven architecture community works from an implicit model theory. The explanations I provide here can inform the model driven architecture community as it comes to recognize the need to develop a formal model theory.

The triangular figure above is a sign that allows us to understand meaning through its structure: in 3s. The figure has three nodes labeled N1, N2, N3 and three edges labeled E1, E2 and E3. The nodes can indicate either types or tokens in our theory of meaning. The edges represent the relations among the nodes. I’ll show how to incorporate this structure into a truth-based, or Tarskian, model theory to support Goguen’s relational theory of meaning which I reference in my post entitled Algebraic Semiotics: A Relational Theory of Meaning.

But first I’ll explain the truth-based, or Tarskian, model theory behind the semantic web.

I’m just back from Tucson, AZ where I spent the last few days at the 2008 Science of Consciousness conference. The conference is sponsored by the Center for Consciousness Studies (CCS) which is part of the University of Arizona Medical School. CCS presents itself as promoting open, rigorous discussion of all phenomena related to conscious experience.

The structure of the conference includes plenary, concurrent and poster sessions. The plenary sessions were heavily weighted towards normal science. (See Thomas Kuhn The Structure of Scientific Revolutions.) Presentations demonstrated a very high level of the scientific method within clinical psychology and neuro-science. Clinical psychology and neuro-science are not my area, but the results of the research I heard presented underwhelmed me.

Conclusion. Normal science does not explain consciousness very well at all.

A week before the conference, I received a recording of a talk given by Allan Watts at IBM way back in 1969. Yes, Allan Watts from The Way of Zen and TAO: The Watercourse Way. In this talk, Watts differentiates mechanism and organism. Watts reminds the IBM researchers that mechanism provides a very limited model of organism and advises that there are limits on what can be achieved through mechanism and normal science. I left the conference with the impression the science of consciousness needs a new approach based in organism. Our consciousness is an artifact of the human organism and normal science does not explain phenomena related to consciousness well at all.

Overall, a very nice conference. I’ll probably go back as the conference is scheduled to be held every two years, so I’ll offer these suggestions for 2010:

1. Normal science should not drive the agenda. As suggested by Kuhn, identify anomaly and either known or emerging crises, that’s where the action is !
2. Develop a pattern language rooted in organism, not mechanism. Structure the pattern language, the science will follow close behind.
3. Have an open jam session, not a talent show and encourage everyone to participate. The performer/observer model of a talent show connotes entertainment, a consumer model. Jam sessions imply shared consciousness. Anyone can hit a drum.

If you’re interested in the categories ontology mentioned in my post called “On a New List of Categories” and you’re looking for some background, check out Joseph Goguen’s work on algebraic semiotics. As described by Goguen in “On Notation” Peirce’s semiotics forms the basis for a relational theory of meaning for algebraic specifications.

Today, RDF semantics are based in model theory. Model theory defines semantics as an interpretation of the structures in RDF and OWL from which machines can make the same, or consistent, inferences. A consistent, or similarly coherent, interpretation is assumed to represent the truth. But this coherence says nothing about the correspondence of the structures with, or their relation to, the world in which we live. They could be totally wrong and still be consistent. (aka. not true)

Tomorrow, we’ll need to extend RDF semantics with a correspondence theory of the truth so meaning on the semantic web squares with what us regular folks think of as meaning. As in algebraic specifications, Peirce’s semiotics can provide that meaning on the semantic web. The categories ontology needs a bit of work and will for some time. LBase provides the foundation from which new RDF vocabularies can be built. In the coming months I hope to extend the categories ontology with some of Peirce’s later works where his theory of semiotics is fully developed.

Stay tuned !

I make available for all to use an OWL ontology of Charles Sander’s Perice’s “On a New List of Categories” under a Creative Commons By license. In this early manuscript (1868), we see the foundations of his semiotics that later matures in MS 478 Sundry Logical Conceptions, MS 450 Nomenclatures and Divisions of Triadic Relations and MS 517 New Elements.

I specified the ontology in Swoop using the Pellet description logic reasoner. These open source tools serve as an excellent reference implementation and I’m confident the ontology can be loaded in other semantic web tools. Once you load the ontology, be sure to turn on Pellet and watch the list classify into the categories !

Although I’m sure the ontology can be improved in many ways, I hope you find it as useful as I did in furthering your understanding of how Peirce’s semiotics better informs our conception of semantics beyond what’s available on today’s Semantic Web.

The new year’s here and with the passing of 2007, its time for me to add a new law to the Natural Laws of Federation called The Law of Limits. These laws are just thought a experiment, but adding The Law of Limits says a lot about what I’ve come to believe after having the opportunity to closely study ontology, logic, semiotics, category theory, complex systems over the past few years. Before you read the Law of Limits, I highly recommend #3 of the Ten Predictions from Stevey’s Drunken Blog Rants which inspired the Law of Limits.

The Law of Limits: Complex systems exhibit continuous adaptation when characteristics of the system function as limits. When characteristics of the system function as breakpoints, adaptation is stepwise. Knowledge is a characteristic of information sharing that functions as a breakpoint. And we need to accelerate knowledge transfer from the academic research community to both public and private sectors to achieve a breakthrough in information sharing.

As the old year winds down and the new year approaches it’s time to take stock of our beliefs and increase awareness of what’s emerging in the world around us. Reminiscent of Thomas Kuhn’s Structure of Scientific Revolutions, the following quote from Charles Sanders Peirce’s Of Reasoning in General says so much:

“Notwithstanding all that was to be said in favor of the theory, those men must, if they be coldly logical, have foreseen when they staked their fortunes upon that hypothesis, that the chances were that it would prove to be unfounded. Nevertheless, on they marched, a forlorn hope attacking a terrible problem; and if they are good scientific men, they must be ready any day to come forward and declare that the evidence now is, the whole thing is a delusion. A degree of heroism is required to maintain that attitude, which is all the more sublime that the mass of mankind, instead of praising such recantation, will look upon it as utterly contemptible.”

With Enterprise Architecture on the rocks these days, organizations are looking for a fresh approach to governance. Most folks become frustrated or ambivalent after a few years of being squeezed between centralization and localization. And most aren’t making the connection to complex systems.

So as a thought experiment, here are three new principles of governance which I call the Natural Laws of Federation. As Hobbes described in Leviathan, going against natural laws means going against self preservation.

1. The Law of Approximation: The behavioral and structural characteristics of our governance mechanisms must closely approximate the systems we govern And the pervasiveness of internet technology is driving most governance structures towards scale free networks, or Federation.
2. Law of Emergence: Discovery precedes determinism. We don’t all need to agree first, agree on everything, or agree on the same thing. And we need to share information beyond our scope of control.
3. Law of Generativity: We’ll know more tomorrow than we do today.. Advancement, growth and sustainability imply tolerance, independent invention, free extension, language mixing, and partial understanding.

Folks might enjoy the Soloman Feferman lecture Goedel, Nagel, Minds and Machines. After recounting an exchange between Godel and Nagel circa 1956, Feferman describes the implications of the minds vs. machines dichotomy ensuing from the exchange. To avoid the impass resulting from the dichotomy, Feferman proposes the redefinition of a formal system to an “open ended schematic axiomatic system.” He claims this redefinition is a constructive step towards an “informative, systematic account at a theoretical level of how the mathematical mind works that squares with experience.” He stresses the importance of a subject neutral theory of operations with basic schemata for language, arithmetic, set theory that would amount to a version of an untyped lambda calculus. Feferman concludes by rejecting any effective machine representation of mind as contemplated by Nagel, Penrose & others.

So, what does this mean to Common Logic and LBase ? Seems to me that efforts like Common Logic and LBase would either have to a) be defined within this type of an open ended system, let’s say as the natural language description of the constraints to which the axioms that make up the theory of such a system would adhere; or b) evolve into an open ended system that exhibits characteristics of transformation across languages, logics, models and theories.

Both public and private sectors are currently evaluating various information sharing capabilities based on Model Driven Architecture and the Semantic Web. The supply of knowledge workers for Model Driven Architecture is relatively high. Currently, the private sector reasonably satisfies demand for these capabilities. But knowledge workers for the Semantic Web are currently in short supply.

Capabilities based on Model Driven Architecture and the Semantic Web are an effective interim step toward information sharing. However, to better satisfy information sharing requirements, Model Driven Architecture and the Semantic Web must evolve to both a broader vision and a more formal specification for information sharing. This vision, embodied in the Logical Environment of the Information Flow Framework (IFF), implies that we abstract today’s approach to one where information is shared among languages, logics, models and theories, with emphasis on the multiplicity of each ! Formal specification implies smart machines and a smarter workforce, or knowledge workers.

Unfortunately, this sets a high barrier for tomorrow’s knowledge workers. Tomorrow’s knowledge workers will become experts in ontology, semiotics and formal concept analysis. Both Model Driven Architecture and the Semantic Web will benefit from a strong labor supply with knowledge workers in these areas. And both public and private sector interests should consider the relevance of knowledge workers to their annual workforce assessments.