A dynamic system interpretation of irreducible complexity
Bibliographic record
Abstract
Behe recently defined the idea of irreducible complexity for biological systems. Using the language of mathematics, we reinterpret his definition from a dynamical systems perspective. Our basic premise is that living organisms behave dynamically in a chaotic way while predictable periodic behavior reflects cessation of function. We consider the dynamics of a functioning system and altered versions of it to draw conclusions about the irreducible complexity of the original system. The dynamics of an organism is described by means of a discrete time transformation τ on the phase space of the system. The statistical behavior of τ is studied by means of its Frobenius–Perron operator which, in special cases, can be represented by a matrix. Using these matrices we rewrite our definition of irreducible complexity:M is irreducibly complex if it is primitive but no principal submatrix of M is primitive. The primitivity property implies chaotic behavior, while failure to have the primitivity property reflects periodic behavior. Examples of irreducibly complex dynamical systems are presented. We show that certain dynamical systems which are irreducibly complex have an additional property, namely that other systems arbitrarily close to it behave in a dramatically different way. Such behavior suggests that selective evolution by means of small perturbations may not be a general mechanism for achieving the dynamical behavior of a complex system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".