Neutrosophic game pricing methods with risk aversion for pricing of data products
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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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".