Agent-Based Modeling of Construction Firms’ Organizational Behavior in Public Tenders
Bibliographic record
Abstract
A key problem of construction firms’ management and economy is organization of effective participation in public tenders. The direct executor, who determines the price of the contract, may be interested in obtaining as many contracts as possible. It means that his strategic behavior in tender may be to undervalue each individual offer. At the same time, such a strategy can be a source of risk of project loss because the actual costs may be lower than the price of the contract won. The management of the construction organization is not interested in this. On the other hand, overpricing strategy may lead to a reduction in the number of contracts won, which may not seem effective either for the head or for the executor of such an organization. The article discusses whether the profits of a construction firm can increase by using a more precise method of calculating the estimated cost. The second question is—which staff of a construction firm will benefit from using such methods? The aim of this work is to test these hypotheses with the instrumentality of agent-based modeling. Profit values of construction firms were obtained by the computer simulation of the construction firms’ strategic behavior in public tenders. Results of 1500 computer experiments are presented as a decision tree. It can be seen that when using a more precise method, construction firms win tenders almost two times less often. However, they incur losses many times less than with an inaccurate method. If a construction firm made a profit from the contracts won, the profit margin was almost always greater when using the more precise method. Moreover, the results of game-theoretic modeling are given. Values of the objective functions of the executor and head of the construction firm were obtained, taking into account the reward for contracts won and penalty for miscalculating the cost of work. It has been proved that using more precise methods for calculating the estimated cost is beneficial to both the head and the executor. It can be concluded that both hypotheses were confirmed and a precise method for calculating the cost increases the efficiency of a construction firm.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".