Towards a Richer Model of Deliberation Dialogue: Closure Problem and Change of Circumstances
Bibliographic record
Abstract
Models of deliberative dialogue are fundamental for developing autonomous systems that support human practical reasoning. The question discussed in this paper is whether existing models are able to capture the complexity and richness of natural deliberation. In real-world contexts, circumstances relevant to the decision can change rapidly. We reflect on today’s leading model of deliberation dialogue and we propose an extension to capture how newly exchanged information about changing circumstances may shape the dialogue. Moreover, in natural deliberation, a dialogue may be successful even if a decision on what to do has not been made. A set of criteria is proposed to address the problem of when to close off the practical reasoning phase of dialogue. We discuss some measures for evaluating the success of a dialogue after closure and we present some initial efforts to introduce the new deliberation features within an existing model of agent dialogue. We believe that our extended model of dialogue may contribute to representing that richness of natural deliberative dialogue that is yet to be addressed in existing models of agent deliberation.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".