Can you explain how to develop the question _Are we mistaking random noise (entropy) for prescribed information or functional utility
To develop the question—“Are we mistaking random noise (entropy) for prescribed information or functional utility?”—you must conduct a “Chaordic / Cybernetic Investigation” designed to cross the Epistemic/Cybernetic Cut. This means determining whether a complex situation is merely the result of blind, physical chance (noise) or if it is governed by formal, purposeful rules (cybernetic control).
David L. Abel, Claude Shannon, and Terrence Deacon provide a rigorous, step-by-step framework to develop this inquiry and evaluate whether a system possesses actual “Prescriptive Information” (instructions that achieve a goal) rather than just meaningless complexity.
Here is how you structure the investigation to answer this question:
Phase 1: Analyze the Sequence Complexity
We often mistake a highly complicated mess for an intelligent system. To develop the question, you must analyze the data or behavior of the system and categorize its complexity.
- Ordered Sequence Complexity (OSC): Is the behavior highly ordered, redundant, and compressible? If yes, it is driven by rigid physical laws (Necessity), much like a crystal forming. It contains minimal information and no choice.
- Random Sequence Complexity (RSC): Is the behavior highly complex, uncompressible, but ultimately non-functional? If yes, it is generated by Chance (entropy/noise). It possesses maximum Shannon uncertainty but zero utility.
- Functional Sequence Complexity (FSC): Is the sequence both highly uncompressible and functionally optimized? If yes, it indicates the presence of Prescriptive Information (PI) generated by Choice Contingency.
- Diagnostic Question: Does this complex process actually compute a solution or sustain a homeostatic goal, or is it just an astronomically complicated but useless pile of data (like spilled pick-up sticks)?.
Phase 2: Differentiate Constraints from Controls
To find out if a system has functional utility, you must investigate what is dictating its behavior.
- Diagnostic Question: Are the outcomes of this system dictated by invariant, unbreakable physical laws (Constraints)?. For example, gravity and chemical valency blindly force outcomes without any regard for utility.
- Diagnostic Question: Is the system governed by arbitrary rules that can theoretically be broken, which would lead to a loss of function (Controls)?. Formal rules, like software syntax or genetic translation codes, indicate that the system is being formally steered toward a pragmatic goal.
Phase 3: Locate “Dynamically Inert” Switches
Functional utility requires a “Material Symbol System” where physical tokens are arbitrarily assigned to represent abstract concepts.
- Diagnostic Question: Are the components of this system dynamically inert?.
- For a system to contain prescribed information, it must have decision nodes (like logic gates in a computer or nucleotide bonds in DNA) where physical forces do not dictate the setting. Because the switches are free to be arranged in any order (high Shannon uncertainty), their specific, functional arrangement serves as proof of purposeful Choice Causation rather than random noise.
Phase 4: Search for “Constitutive Absence”
Claude Shannon mathematically equated maximum uncertainty with randomness/noise. Terrence Deacon argues that true information and biological function are defined negatively—by “constitutive absence,” or what the system actively prevents from happening.
- Diagnostic Question: What normally occurs in this physical environment that has currently failed to occur?.
- Diagnostic Question: Is the system experiencing a highly improbable reduction in entropy (disorder)?. A spontaneous reduction in noise indicates that external work was performed or specific constraints were applied to carve meaning out of the chaos.
Phase 5: The “Investigator Interference” Falsification Test
We frequently mistake random noise for functional utility because humans implicitly inject their own intelligence into the system. Abel warns that in evolutionary algorithms or models of self-organizing complex adaptive systems, the “illusion” of spontaneous functional organization is almost always the result of hidden human steering.
- Diagnostic Question: In this model or process, did a human investigator secretly steer the iterations, select the fitness function, or isolate the successful reactions?.
- If yes, the system is not naturally generating functional utility out of noise; the utility was smuggled across the Cybernetic Cut by the human designer’s Artificial Selection.