A new decision-making tool developed by researchers at Queensland University of Technology (QUT) is designed to help governments, businesses, and organizations make more reliable decisions for complex projects such as infrastructure development and public policy. The model, called the Group-Consistency Best-Worst Method (GC-BWM), was created by Ph.D. researchers Omid Motamedisedeh and Faranak Zagia from the QUT School of Architecture and Built Environment. It addresses a common issue in group decision-making: minor differences in expert opinions can lead to significantly different outcomes. Published in the journal Array, the method helps organizations identify the most consistent and aligned expert opinions when making decisions involving uncertainty. Lead author Motamedisedeh explained that traditional approaches can be highly sensitive to small errors or changes in expert responses, whereas the new model consistently produces more stable and reliable rankings. As many important decisions today involve multiple experts with different perspectives, the need for a more robust method has become increasingly clear. One of the key challenges in group decision-making is that even knowledgeable experts can produce very different results due to slight variations in their responses. The research shows that group decision-making can be made more reliable by considering not only whether individual responses are internally consistent, but also how well they align with the broader group. Unlike traditional methods that average responses before or after analysis, GC-BWM evaluates all respondents within a single framework. This approach identifies a subset of respondents whose judgments are both individually consistent and closely aligned, reducing the influence of conflicting or unclear responses. Motamedisedeh said the method was inspired by the "wisdom of crowds" concept, where collective judgments can be more accurate than individual ones when combined effectively. The goal is not to force agreement, but to identify areas of genuine consensus while minimizing the impact of responses that might be influenced by misunderstanding or fatigue. Zagia noted that the model remained stable even when expert responses were deliberately altered during testing. Through simulations and sensitivity analysis, they found the model maintained consistent rankings more often than conventional methods, even with modified inputs. This robustness is crucial in real-world decision-making, where mistakes or differing viewpoints are common. The model can be applied in various fields where decisions involve multiple stakeholders and competing priorities, such as infrastructure planning, transportation, energy, sustainability, risk analysis, and policy development. The research showed that more reliable group decisions can be achieved without requiring experts to provide additional information or complete more complex assessments. This framework improves the quality and reliability of outcomes without increasing the burden on decision-makers. Ultimately, it provides organizations with a practical way to make better decisions in situations where uncertainty and differing opinions are unavoidable.