Applications of Quantum Probability Amplitude in Decision Support Systems
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
Establishing various frameworks for managing uncertainties in decision-making systems have been posing many fundamental challenges to the system design engineers. Quantum paradigm has been introduced to the area of decision and control communities as a possible supporting platform in such uncertainty management. This paper presents an overview of how a quantum framework and, in particular, probability amplitude has been proposed and utilized in the literature to complement two classical probabilistic decision-making approaches. The first such framework is based in the Bayesian network, and the second is based on an element of Dempster–Shafer (DS) theory using the definition of mass function. The paper first presents a summary of these classical approaches, followed by a review of their preliminary enhancements using the quantum model framework. Particular attention was given on how the notion of probability amplitude is utilized in such extensions to the quantum-like framework. Numerical walk-through examples are combined with the presentation of each method in order to better demonstrate the extensions of the proposed frameworks. The main objective is to better define and develop a common platform in order to further explore and experiment with this alternative framework as a part of a decision support system.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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