Information of interactions in complex systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper addresses misconceptions of the multi-variate interaction-information measure Q, which several authors have reinvented since its proposal by McGill (1954), giving it a variety of names and interpretations. McGill’s measure claimed to quantify the amount of information of interactions among three or more variables in complex systems. In (Krippendorff, 1980), I raised doubts about the validity of Q and its relatives. The chief problem that Q-measures fail to recognize is that complex interactions tend to involve circularities and the probability distributions characterizing such circularities cannot be obtained by products of probabilities, which underlie information theory as far as developed by Shannon (Shannon & Weaver, 1949). I argued that Q-measures are mere arithmetic artifacts, and proposed an algorithmic solution to measuring the amount of information in the interactions within complex systems, now widely accepted. The paper responds to Leydesdorff’s (2009) “Interaction information: Linear and nonlinear interpretations,” published in the current issue of this journal and preceding discussions of these issues on the Cybernetics Discussion Group CYBCOM and personal correspondence involving Jakulin (2009). It prefers to rely on demonstrations with numerical data over abstract interpretations of mathematical forms that can so easily entrap scholars into believing that they measure something real without considering evidence to the contrary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it