Interactive Analysis of Agent-Goal Models in Enterprise Modeling
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
Understanding and analyzing the needs of an enterprise in the early stages of a project requires knowledge about stakeholders, their goals, interactions, and alternative actions. Agent-goal models offer a way to systematically and graphically capture this information, even as it evolves through continued elicitation. However, the complexity of resulting models makes it difficult to evaluate the achievement of key stakeholder goals within a model without applying systematic analysis procedures. Existing approaches to agent-goal model evaluation focus on automated procedures, without explicitly promoting model iteration and domain elicitation. In this paper, the authors argue that “Early” Enterprise modeling requires analysis procedures that account for the incompleteness and informality of early agent-goal models, facilitating iteration, elicitation, and user participation. A qualitative, interactive evaluation procedure for agent-goal models is introduced, using the i* Framework illustrated. Case study experience and results of an exploratory experiment show the applicability of the procedure to early enterprise analysis.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| 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