Decision Support System For Conflict Resolution In Construction
Bibliographic record
Abstract
Conflicts and disputes occur regularly throughout the entire construction process due to the complexity of operations and their conflicting resource requirements. At times, unresolved conflicts may slow or halt the entire project. In the past, many traditional methods have been used to resolve disputes, including starting with a simple resolution approach and ending with legal arbitration, which consumes time and money. However, in complex conflicts where multiple parties are involved, resolving conflict becomes a complex task and decision support tools are needed. This paper presents a new methodology to facilitate the negotiation that takes place among multiple parties in construction conflicts. Two fundamental theories are used in this methodology: 1) Game theory, which is the study of decision makers and their level of satisfaction in various actions/ counteractions; and 2) the Graph model for conflict resolution, which aims at analyzing the interactions among the decision makers, and reducing their differences to produce the most acceptable settlement. Based on these two fundamental theories, this paper presents a collaborative negotiation methodology and a computer Decision Support System (DSS) named GMCR II, which facilitates the negotiation of multiple-party conflicts in large construction projects. The DSS allows all parties to express their options and interests to create various courses of actions. The DSS then helps in performing an in-depth analysis to ascertain the possible compromise resolutions or equilibria. Details on the DSS are provided in this paper and a case study of a construction conflict is used to demonstrate its application and potential benefits. Based on the case study results, the effectiveness of the proposed DSS system in conflict resolution is confirmed. This DSS is useful for both researchers and practitioners to better deal with the dispute prone-nature of the construction industry.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".