Integrating Virtual Reality and Consensus Models for Streamlined Built Environment Design Collaboration
Bibliographic record
Abstract
Collaboration in design decision-making is a critical factor in enhancing the quality of built environments. However, several factors, such as the lack of clarity in the negotiation process, the diverse disciplinary backgrounds of stakeholders, and conformity bias, pose significant challenges, rendering the collaboration in built environment design time-consuming. To overcome these challenges and improve the efficiency of collaborative design, a novel solution that introduces a consensus model enhanced by virtual reality (VR) is proposed in this paper. The proposed model implements a structured decision-making process to foster fact-based discussions and minimize conflicts arising from personal biases. It ensures equal influence among all stakeholders, prevents the dominance of any single viewpoint, and offers personalized recommendations to guide stakeholders toward group consensus while respecting their initial preferences. The study leverages VR techniques to enhance communication and comprehension during design negotiations. The VR collaboration platform provides a robust visualization and user-friendly interface, empowering stakeholders to understand the decision-making process better and iteratively refine their preferences for optimal user satisfaction. By integrating VR, the burden traditionally placed on a moderator in group decision-making (GDM) is reduced, resulting in a more streamlined and efficient collaborative process. Additionally, stakeholders can conveniently communicate their preferences remotely through cloud services within the immersive VR collaboration environment, further enhancing the overall design collaboration experience. To the best knowledge of the authors, this study is the first attempt to combine a consensus model with VR to create a comprehensive solution that supports stakeholders in achieving consensus design solutions in the early design stage. This study contributes to the advancement of knowledge in the field of design collaboration by exploring the potential benefits of a VR-enhanced consensus model for GDM.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".