A Decision Support Tool for Agricultural Applications Based on Computational Social Choice and Argumentation
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 the current article, the authors describe an applied procedure to support collective decision making for applications in agriculture. An extended 2-page abstract of this paper has been accepted by the EFITA WCCA congress and this manuscript is an extended version of this submission. The problem the authors are facing in this paper is how to reach the best decision regarding issues coming from agricultural engineering with the aid of Computational Social Choice (CSC) and Argumentation Framework (AF). In the literature of decision-making, several approaches from the domains of CSC and AF have been used autonomously to support decisions. It is our belief that with the combination of these two fields the authors can propose socially fair decisions which take into account both (1) the involved agents' preferences and (2) the justifications behind these preferences. Therefore, this article implements a software tool for decision-making which is composed of two main systems, i.e., the social choice system and the deliberation system. In this article, the authors describe thoroughly the social choice system of our tool and how it can be applied to different alternatives on the valorization of materials coming from agriculture. As an example, that is demonstrated an application of our tool in the context of Ecobiocap European project where several decision problems are to be addressed. These decision problems consist in finding the best solutions for questions regarding food packaging and end-of-life management.
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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 | 0.001 |
| 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