Neutrosophic game pricing methods with risk aversion for pricing of data products
Why this work is in the frame
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Bibliographic record
Abstract
Abstract With the progressive development of satellite image data products, their pricing strategies become more and more important for enterprises to earn profits. The objective of this study is to explore several game pricing methods with risk aversion for pricing of data products in neutrosophic environments. First, to reflect the uncertainty of problem parameters, the idea of neutrosophic variables is adopted. With the aid of neutrosophic variables, the truth, indeterminacy and falsity degrees of players can be intuitively and conveniently obtained. Subsequently, considering the risk aversion of decision makers, the optimistic value theory is introduced into neutrosophic variables for calculating the optimistic value of player's profits. Then, different pricing models are constructed under the Bertrand and Stackelberg game scenarios, respectively. After deriving the corresponding equilibrium equations, some numerical instances are provided to testify the feasibility of our methods. Furthermore, the influences of dissimilar market power structures are examined. Finally, the effects of seven problem parameters and players' confidence levels on pricing results are investigated through sensitivity analyses. The results show that the proposed methods are practicable and can offer guidance for the pricing decision of data products.
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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 | 0.001 | 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 | 0.001 |
| 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