Dynamics of Equilibrium Prices With Differential and Delay Differential Equations Using Characteristic Equation Techniques
Bibliographic record
Abstract
This study compares differential model to delay differential model in terms of their qualitative behaviour with respect to equilibrium price changes using roots of characteristic equation techniques. The equilibrium states of both price adjustment models were simulated using inputs from same source. The study found that irrespective of initial prices set for the system, the current price of the differential model would always move monotonically towards the equilibrium price defined for the system. However, the current price of the delay- differential model will fluctuate and move away from the initial prices due to the delay parameter associated with the supply, then gradually decrease and turn towards the defined system equilibrium price. Results from the study also showed that current prices in the delay-differential model are not predictable at the initial stage due to the time delay parameter in the supply function of price. On the other hand, current prices in their counterpart models without delay are predictable, as they always converge to the equilibrium price points defined in the system. Since most economic and physical systems are time delay inherent, it is recommended that such systems are modeled using delay-differential equations to reflect realities of the phenomena.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".