Modeling human decision-making is difficult. Decision-makers are typically primed with unique biases that widen the confidence interval of judgment. Therefore, it is important that the human process in the system being modeled is designed to alleviate damaging biases and assumptions in an effort to increase process consistency between decision-makers. In this experiment, it is hypothesized that coupling specific decision-facilitation methods with a specific scale range will affect the consistency between decision-makers. This article presents a multiphase experiment that examines a varying presentation mode as well as scale range to determine how value is determined in subsequent pairwise comparisons of alternatives against specific requirements. When considering subject value ratings of the expected rank order of alternative subgroups (indicating strong criteria independence), results show that subjects used consistent comparison ratios regardless of the scale range. Furthermore, when comparing the subgroups of expected rank order responses to the subgroups of biased responses, although ratios were different, the same general trend of comparison existed within subgroups. Providing evidence that careful selection of the presentation mode can facilitate more consistent value comparisons between compatible decision-makers allows for the identification of and adjustment of disparities due to bias and potential lack of incremental scaling detail. Furthermore, by creating decision processes that render more consistent cognitive behavior between decision-makers, tighter confidence intervals can be obtained, and critical assumptions can be validated.

Every decision maker’s internal scale is different based on a myriad of possible factors unique to that decision maker. Conflicting criteria within and between alternatives in multicriteria decision making can create negative effects within the weighting schemes and amplify preference biases and scale disparities between decision makers in a group decision context. Additionally, the weighting of group decision-making frameworks can intensify the already skewed criteria values. When making judgments against requirements, it may be preferable to reduce scale trend distortions between decision makers as much as possible. Previous research supports that certain information presentation modes can significantly reduce preference bias and strengthen criteria independence against requirements through cross alternative anchoring. This paper expands that research and proposes a new optimization model for strengthening criteria independence and consensus in group decision making. Initial results indicate the proposed optimization model can significantly reduce scale distortions and smooth comparative alternative value trends between decision makers toward strengthened group consensus. Furthermore, results support the potential and opportunity for semiautonomous group consensus processes.

Selecting an appropriate contract type is a central decision in purchasing and supply management, shaping incentives, risk allocation, and buyer–supplier relationships. Drawing on Transaction Cost Economics (TCE), this study examines how observable procurement characteristics can inform contract governance choice. Using a dataset of over 2,500 U.S. federal service contracts, we develop and test empirical models that relate contract attributes—such as competition structure, subcontracting, performance specifications, and contract value—to governance form (fixed price versus cost plus), providing a large-scale empirical setting with broader implications for procurement and supply chain governance. Our results provide large-sample evidence that commonly available procurement data serve as meaningful proxies for transaction cost dimensions, including asset specificity and complexity. Several contract features are significantly associated with governance choice, supporting TCE predictions, while interaction effects reveal more nuanced relationships. In particular, the findings suggest that asset specificity moderates the influence of complexity on governance selection, highlighting conditional effects that extend prior empirical applications of TCE. Building on these insights, we develop predictive models that use only information available at the time of contract design. These models improve contract-type classification accuracy by approximately 14 percent relative to an informed baseline, demonstrating practical value for procurement decision-making. Overall, the study advances purchasing and supply management research by operationalizing TCE with observable data, offering empirically grounded guidance for contract design, and bridging theory and empirically grounded decision-support approaches for contract design.

Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth measurements. The high accuracy of such systems offers detailed, real-time insights into optimized cargo loading under unpredictable outdoor conditions. The findings, informed by both quantitative and qualitative analyses, reveal the impact of solar position, cloud coverage, and camera placement on the completeness of the point clouds, quality of the depth points, and accuracy of the measurements. This study provides insights to push the boundaries of cargo logistics possibilities in challenging outdoor environments.