Decision-making in the purchase of equipment in agricultural research laboratories: a multiple-criteria approach under partial information
Bibliographic record
Abstract
Investments in the agricultural sector represented by innovations and new technologies strongly influence the economic growth in developing countries. In this context, purchasing decisions have become more relevant. Multiple-criteria decision-making techniques are well suited for decision-makers (DMs) who are considering the introduction of new technologies. In this paper, a multi-criteria model is built to help a Colombian agricultural research company make decisions on purchasing different laboratory equipment. A compensatory approach based on trade-offs is used to elicit the preferences of a group of DMs. The high number of answers and cognitive effort required from them during the elicitation process led to using an alternative approach based on partial information, called the FITradeoff (The Flexible and Interactive Tradeoff) method. It showed to be the best fit to solve the company’s purchasing problem and allowed its managers to make decisions that consider criteria other than price, taking account of the DMs’ conflicting viewpoints. The proposed model aimed at contributing to the articulation of the end-user knowledge in decision-making in order to ensure effective articulation of actors and strengthening of science, technology and innovation in agriculture.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".