Q_Goest said:
Are you suggesting that genes are what overcome the symbol grounding problem that Harnad talks about? What paper does Pattee have that explains that concept best? I might go along with that. Here's the problem though. Neuron interactions are governed by classical mechanics, so any strongly emergent phenomena (ie: phenomenal consciousness) can not emerge from those interations alone since classical interactions only allow for weakly emergent phenomena. Yet the mainstream view holds that phenomenal consciousness is emergent on the neuron interactions and not for example, genes or any molecular interactions. Does Pattee address this issue or does he go along with the mainstream view that phenomenal consciousness emerges from neuron interactions alone?
You keep building your position on the claim that because classical physics does not seem to permit something, it is not permitted. Plus then the assumption that the purpose of a model is to give the modeller "the feeling of what it is like to be" rather than a formal theory of the general constraints (which feel nothing like anything in particular precisely because they are maximally generic, maximally abstract).
Newton gave us F = ma. That describes a completely generic symmetry of nature. It does not tell you what it is like to be a falling apple or a human throwing a baseball.
So Pattee and other systems thinkers are trying to abstract the general laws of symbols, or hierarchies, or global constraints, or whatever. Phenomenal consciousness is something very particular (even your own state of mind is constantly changing). So it is just a false goal to demand that physicalist models
must tell you why anything in phenomenological terms. It is a category error.
On the genes thing and symbol grounding, genes are just one example of semiotic constraints. Membranes, words, organelles, axon fibres - any kind of dimension reducing structure is a meaningful constraint on a systems free dynamics. But genes and words would be significant in being about the strongest level of semiotic constraint. Being 1D serial codes, they are both as removed from the worlds they control as they can be.
Anyway, there was a good conference on Pattee's work that offers a variety of views...
http://informatics.indiana.edu/rocha/pattee/
And some of the papers from it...
The Physics of Symbols: Bridging the Epistemic Cut
H. H. Pattee
Evolution requires the genotype-phenotype distinction, a primeval epistemic cut that separates energy-degenerate, rate-independent genetic symbols from the rate-dependent dynamics of construction that they control. This symbol-matter or subject-object distinction occurs at all higher levels where symbols are related to a referent by an arbitrary code. The converse of control is measurement in which a rate-dependent dynamical state is coded into quiescent symbols. Non-integrable constraints are one necessary conditions for bridging the epistemic cut by measurement, control, and coding. Additional properties of heteropolymer constraints are necessary for biological evolution.
http://informatics.indiana.edu/rocha/pattee/pattee.html
Symbols and Dynamics in the Brain
Peter Cariani
The work of physicist and theoretical biologist Howard Pattee has focused on the roles that symbols and dynamics play in biological systems. Symbols, as discrete functional switching-states, are seen at the heart of all biological systems in form of genetic codes, and at the core of all neural systems in the form of informational mechanisms that switch behavior. They also appear in one form or another in all epistemic systems, from informational processes embedded in primitive organisms to individual human beings to public scientific models. Over its course, Pattee's work has explored 1) the physical basis of informational functions (dynamical vs. rule-based descriptions, switching mechanisms, memory, symbols), 2) the functional organization of the observer (measurement, computation), 3) the means by which information can be embedded in biological organisms for purposes of self-construction and representation (as codes, modeling relations, memory, symbols), and 4) the processes by which new structures and functions can emerge over time. We discuss how these concepts can be applied to a high-level understanding of the brain. Biological organisms constantly reproduce themselves as well as their relations with their environs. The brain similarly can be seen as a self-producing, self-regenerating neural signaling system and as an adaptive informational system that interacts with its surrounds in order to steer behavior.
http://informatics.indiana.edu/rocha/pattee/cariani.html
Howard Pattee's Theoretical Biology - A radical epistemological stance to approach life, evolution, and complexity.
Jon Umerez
This paper offers a short review of Pattee's main contributions to science and philosophy. With no intention of being exhaustive, an account of Pattee's work is presented which discusses some of his ideas and their reception. This is done through an analysis centered in what is thought to be his main contribution: the elaboration of an internal epistemic stance to better understand life, evolution and complexity. Having introduced this core idea as a sort of a posteriori cohesive element of a complex but highly coherent and complete system of thinking, further specific elements are also reviewed
http://informatics.indiana.edu/rocha/pattee/umerez.pdf
The semiotics of Control and Modeling Relations in Complex Systems
Cliff Joslyn
We provide a conceptual analysis of ideas and principles from the systems theory discourse which underlie Pattee's semantic or semiotic closure, which is itself foundational for a school of theoretical biology derived from systems theory and cybernetics, and is now being related to biological semiotics and explicated in the relational biological school of Rashevsky and Rosen. Atomic control systems and models are described as the canonical forms of semiotic organization, sharing measurement relations, but di.ering topologically in that control systems are circularly and models linearly related to their environments. Computation in control systems is introduced, motivating hierarchical decomposition, hybrid modeling and control systems, and anticipatory or model-based control. The semiotic relations in complex control systems are described in terms of relational constraints, and rules and laws are distinguished as contingent and necessary functional entailments respectively. Finally, selection as a meta-level of constraint is introduced as the necessary condition for semantic relations in control systems and models.
http://informatics.indiana.edu/rocha/pattee/joslyn.html