Modeling Sustainable Digital Personality States through Constrained Nonlinear Dynamical Systems and Bounded Ethical Control Functions in Socio-Technical AI Frameworks
Bibliographic record
Abstract
The introduction of the Al systems that communicate longitudinally with human participants provokes the need to develop solid models of sustainable digital personality states (SDPS) in socio-technical interactions. The existing models of digital identity design do not implement ethical protection and tend to provide unstable or misaligned personality development. This paper proposes a new model which combines constrained and nonlinear dynamical models with controlled ethical mechanisms which would serve to orient the evolution of digital personalities in the long run. The software real-time models control the state of digital personality as a vector. Moral boundaries are coded as well as sustainability ensured through a Lyapunov stability analysis. Bounded feedback laws that synthesize control inputs have norm-limiting actuation and ethical barrier functions as constraints are met. The use of social reward signals to make behavioral adjustment with the help of an adaptive gain mechanism. The socio-technical architecture is multi-agent and allows response feedback cycles between humans and Al as well as co-simulation of the digital twin. The presented solution makes further progress in the ethical al personality development by creating a tradeoff between responsiveness and adherence to constraints with ethical potential gradients as a measure of resilience.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| 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; both teacher heads agree on what is shown here.
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".