Complexity Sciences and Artificial Intelligence for Improving Lives through Convergent Innovation
Bibliographic record
Abstract
This panel symposium brings together a unique portfolio of scientists in management, artificial intelligence and complexity science to help address questions bearing on how organizations and organization theory can contribute more and faster to the betterment of society through ameliorating the health and well-being of those who live in it. The overarching goal of the panel symposium is to advance the development of the integrative translational research framework of Convergent innovation (CI). As a form of meta-innovation–an innovation in the way we innovate, CI is a novel transdisciplinary and cross-sectoral approach that combines theory- and data-driven science to capitalize on powerful computational models and improve understanding and prediction of behavioral and system-level pathways of more targeted innovations. Panelists will progressively discuss : (1) how a multidimensional compositional approach to behavioral research can be effectively integrated into complexity science and artificial intelligence modeling; (2) how artificial intelligence methodologies can examine emotions as part of the client- provider interface, within the context of emotion-aware human-computer dialogue; (3) how fine-grained agent-based models enable organizations and systems forming society while keeping individual behavioral at the center; (4) how social network models can bridge individuals to their community and broader societal context; (5) how multi-method computational models can integrate decisions made by several different actors and across different scales by individuals themselves and by organizations and institutions deveining the context in which individuals live. Insights will be provided on how this multiscale model framework can formalize the convergent innovation framework.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".