Good Moods: Outlook, Affect and Mood in Dynemotion and the Mind Module
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
In this article we describe two systems for autonomous characters intended to simulate the minds of characters in virtual game worlds. These systems, the Dynemotion People Engine (DPE) and the Mind Module (MM), are here presented with special focus placed on the design and implementation of the parts of the architecture that simulate what is colloquially called mood. The mood feature is presented to the user in both applications as a fine-grained matrix that summarizes the character's state of mind, typically a complex state. Thus in both systems the mood feature functions as a qualitative guide describing the affordances for the interaction with one's own avatar or another character at a given moment. This simplifies the design and balancing of game design in terms of authorial affordances and provides a more familiar context for user-character interactions.
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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 | 0.000 |
| 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