DEVS as a Method to Model and Simulate Combinatorial Double Auctions for E-Procurement
Bibliographic record
Abstract
The surge in electronic procurement is fostering the proliferation of electronic marketplaces and advanced auctions as primary coordination mechanisms. Among these, combinatorial and double auctions are gaining traction in the procurement sector. However, prevalent implementations often assume participants to be perfectly rational, adhering to predefined behaviors within the auction model. These centralized models, while prevalent, fail to capture the intricate dynamics of real auction environments adequately. Consequently, there is a growing recognition of the necessity for decentralized models within an agentbased framework to simulate such auctions authentically. The contribution of this work is the application of the DEVS formalism to develop a decentralized model for a combinatorial iterative double auction to address the limitations of centralized implementations. The model is formally defined, and a case study is presented to verify it against its centralized version. This is the first step toward accommodating agents with varied behavioral patterns within auction simulations.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".