Measuring information propagation and retention in boolean networks and its implications to a model of human organizations
Bibliographic record
Abstract
Abstract: - A system structure, i.e., how elements of a system are connected, is a key factor for information retention and transmission through its elements. From the system dynamics, i.e., the states of the elements over time, we measure the system’s ability to propagate information through its elements as the pairwise mutual information (pMI) between the elements at moments t and t + L, where L is the minimum path length between the two elements. Information retention is measured with Lempel-Ziv (LZ), a measure of the complexity of transmitted information, from the same time series of states. We propose a combined measure of information propagation and ability to retain information efficiently, to determine optimal structures for information propagation and retention. We present the results on information propagation and retention, as a function of topology (random and small world structures), connectivity, noise and clustering coefficient. The conclusions are applicable in any context where these networks are used to model the system. Here, we apply our findings to a model of human organizations and than propose a generalization of the model to capture more realistic features, such as more complex internal states for elements and simulating information exchange with the environment outside of the system. As more features are incorporated, this model will capture many important features of human organizations, and other complex systems.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".