In the intricate world of construction project management, the reliability of expert evaluations holds paramount importance in ensuring fair and effective bidding processes. A recent groundbreaking study introduces a novel approach to analyzing and enhancing the reliability of scoring behaviors exhibited by bidding evaluation experts. This research leverages the innovative integration of decision entropy concepts with a robust reliability assessment framework to detect inconsistencies in expert judgments, thereby refining project selection and oversight mechanisms.
The core premise of the study revolves around the evaluation of scoring behaviors displayed by experts tasked with assessing bids for construction contracts. Traditional methods often fall short in addressing anomalies that arise from non-principled factors such as human error or inadequate expertise. By incorporating a decision entropy methodology, the authors propose a comprehensive model that quantifies the uncertainty present in the evaluators’ scoring patterns, offering an unprecedented lens through which reliability can be objectively analyzed.
At the heart of this methodology lies the identification of abnormal scores that do not conform to expected evaluative standards. These aberrations frequently stem from inadvertent mistakes made by experts or from their insufficient evaluative capabilities. Rather than dismissing these outliers, the research integrates them into the reliability analysis, treating them as crucial data points that reveal underlying weaknesses in expert judgment. Such an approach acknowledges the complex human factors that influence scoring and accommodates them within a mathematical framework.
Subsequently, for the cohort of scores deemed normal by initial anomaly detection, decision entropy is employed to evaluate the degree of certainty in the experts’ scoring behavior. Decision entropy, a adaptation of information entropy applied to decision-making processes, quantifies the disorder or variability in a set of scores. Lower entropy indicates higher consistency and, by extension, higher reliability. The study introduces a reliability metric, R_i, calculated via this entropy approach, offering a measurable parameter to assess each expert’s evaluation fidelity.
Importantly, the research establishes a critical threshold for expert scoring reliability at 80%. Experts whose reliability scores fall below this benchmark are flagged for low scoring reliability, indicating subpar evaluation proficiency. This threshold functions as a proactive early warning system, signaling bidding administrative supervision bodies to intervene or provide remedial measures. The establishment of such an objective standard represents a significant advancement in monitoring and improving expert assessment quality.
The practical implementation of this model was validated in a real-world scenario involving the X Square construction project. By applying their reliability assessment method, the researchers demonstrated its effectiveness in identifying unreliable evaluators and maintaining the integrity of the bid evaluation process. The success of this validation highlights the model’s adaptability and its potential applicability to other construction projects with analogous evaluative demands.
A notable advantage of the proposed method is its minimal prerequisite conditions, allowing for seamless integration into diverse construction contexts without imposing restrictive data or operational requirements. This flexibility is crucial for widespread adoption, as construction projects vary widely in scale, complexity, and stakeholder involvement. The adaptability of the method underscores its practical utility across different settings.
Delving deeper into the computational aspects, the researchers outline detailed algorithmic steps that underpin their approach. These include the use of the power method, a numerical linear algebra technique employed to compute dominant eigenvalues and eigenvectors essential for information entropy calculations. The power method’s iterative nature enables efficient convergence even with large datasets, addressing common challenges linked to scalability and computational expense.
The calculation of information entropy, a measure rooted in Shannon’s information theory, serves as the mathematical foundation for quantifying uncertainty in expert decisions. This concept, when translated to the domain of scoring reliability, captures the unpredictability in evaluators’ scoring patterns, effectively discerning consistency from randomness. The paper demonstrates how integrating entropy measures directly correlates with expert reliability, ensuring a rigorous and quantitative basis for assessment.
Furthermore, the generation of decision opinions through this framework allows for the synthesis of expert scores into a coherent evaluative picture. By transforming raw scoring data into structured opinion metrics informed by entropy, decision-makers receive enhanced clarity on the trustworthiness of evaluations. Such clarity facilitates more informed administrative decisions and targeted supervision.
From a computational efficiency standpoint, the study’s approach lends itself well to programmatic implementation. The algorithmic steps – encompassing eigenvalue computations, entropy calculations, and decision opinion derivations – can be codified into software applications. This automation potential significantly alleviates the burden of manual analysis, especially when faced with voluminous or complex scoring datasets common in large-scale construction bidding processes.
The integration of reliability analysis with entropy-based evaluation marks a paradigm shift, positioning this approach at the forefront of construction management research. It embodies an interdisciplinary fusion of decision theory, information science, and civil engineering management, reflecting the evolving complexity of modern construction oversight.
In summary, this research presents a sophisticated yet practical solution to a longstanding problem in construction project bidding: ensuring the reliability and effectiveness of expert evaluations. By harnessing the decision entropy method coupled with detailed algorithmic procedures, the study equips supervisory authorities with a powerful tool to maintain the integrity and fairness of bid evaluations.
As construction projects continue to grow in size and complexity globally, the demand for transparent and objective expert evaluation methods will only increase. This study’s contribution offers a timely and scalable pathway to address these needs, potentially inspiring further advancements and adaptations in related sectors.
The implications extend beyond the construction industry alone; any domain relying on expert scoring and judgment could benefit from adopting similar entropy-based reliability analyses. Thus, the potential ripple effects of this methodology are vast, spanning fields as varied as academic peer review, financial risk assessment, and medical diagnostics.
Ultimately, by illuminating the reliability landscape of expert bidders through the lens of decision entropy, this research advances the pursuit of fairness, transparency, and accuracy in critical evaluative processes. Its innovative fusion of theoretical rigor and practical applicability is poised to set new standards in the governance of construction project evaluation and beyond.
Subject of Research:
Rating behaviors of bidding evaluation experts in construction projects focusing on reliability analysis using decision entropy methods.
Article Title:
Research on rating behaviors of bidding evaluation experts in construction projects based on novel integrated methods about reliability and decision entropy.
Article References:
Bai, X., Jia, M. Research on rating behaviors of bidding evaluation experts in construction projects based on novel integrated methods about reliability and decision entropy.
Humanit Soc Sci Commun 12, 953 (2025). https://doi.org/10.1057/s41599-025-05339-0
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