A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

Considering the credit index calculation differences, semantic differences, false data, and other problems between platforms such as Internet finance, e-commerce, and health and elderly care, which lead to the credit deviation from the trusted range of credit subjects and the lack of related information of credit subjects, in this paper, we proposed a crossplatform service credit conflict detection model based on the decision distance to support the migration and application of crossplatform credit information transmission and integration. Firstly, we give a scoring table of influencing factors. Score is the probability of the impact of this factor on credit. Through this probability, the distance matrix between influencing factors is generated. Secondly, the similarity matrix is calculated from the distance matrix. Thirdly, the support vector is calculated through the similarity matrix. Fourth, the credit vector is calculated by the support vector. Finally, the credibility is calculated by the credit vector and probability.

Is Automation of Statistical Reasoning a Suitable Mindware in a Base-Rate Neglect Task?

Until recently, studies within the dual-process approach were mainly focused on group differences in processing, and individual differences were neglected. However, individual differences have proven to be a significant factor in conflict detection efficiency and the overall success in base-rate neglect and similar tasks. This should be taken into consideration within the framework of the Hybrid Model of Dual Processing. New tendencies in the development of this model have focused attention on the degree of mindware instantiation as a predictor of base-rate neglect task efficiency. This study aimed to examine the relationship between mindware and base-rate neglect task efficiency and to test and explore the relationship between base-rate response frequency and conflict detection efficiency and the degree of mindware instantiation. All participants solved base-rate neglect tasks, made judgments of confidence in their responses, and solved the Statistical Reasoning Test, Cognitive Reflection Test and Numeracy Scale. We used the Statistical Reasoning Test as a measure of mindware instantiation. The degree of mindware instantiation was found to be the only significant predictor of base-rate neglect task efficiency and the results showed that participants with a higher degree of mindware instantiation generally made more base-rate responses. No correlation was found between the degree of mindware instantiation and conflict detection efficiency. These findings support the hypothesis that the power of logical intuition depends on the individual’s degree of mindware instantiation. Therefore, the results of this research indicate the importance of further research into the role of statistical reasoning in base-rate neglect task efficiency. However, we discuss that there are some methodological limitations in this research which might explain why the degree of mindware instantiation had no relationship with conflict efficiency.

Conflict Detection Predicts the Temporal Stability of Intuitive and Deliberate Reasoning

Although the susceptibility to reasoning biases is often assumed to be a stable trait, the temporal stability of people’s performance on popular heuristics-and-biases tasks has been rarely directly tested. The present study addressed this issue and examined a potential determinant for answer change. Participants solved the same set of “bias” tasks twice in two test sessions, two weeks apart. We used the two-response paradigm to test the stability of both initial (intuitive) and final (deliberate) responses. We hypothesized that participants who showed higher conflict detection in their initial intuitive responses at session 1 (as indexed by a relative confidence decrease compared to control problems), would be less stable in their responses between session 1 and 2. Results showed that performance on the reasoning tasks was highly, but not entirely, stable two weeks later. Notably, conflict detection in session 1 was significantly more pronounced in those cases that participants did change their answer between sessions. We discuss practical and theoretical implications.

An Automatic Conflict Detection Framework for Urban Intersections Based on an Improved Time Difference to Collision Indicator

Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately.

Research on Aircraft Surface Taxi Path Planning and Conflict Detection and Resolution

Aircraft surface taxiing conflict avoidance is mostly adopted by waiting and rerouting methods, but this method does not consider the difference in total taxiing time that may be caused by different strategies. In this study, the airport taxiing path optimization model and taxiing area division model are constructed first. Then, the taxiway use is controlled by subregion based on the analysis of the surface activity area connection relationship. Based on the results of aircraft surface taxiway preselection, the time window of the taxiing area is solved and conflict detection is performed. For aircraft with taxiing conflicts, waiting or changing paths is selected to deconflict taxiing by comparing priorities. An improved A∗ algorithm solution is applied to generate conflict-free glide paths and new glide trajectory occupancy time windows, while the glide paths of aircraft without glide conflicts are not affected. The results of the study show that the use of time windows for conflict detection and deconfliction can further reduce the total taxiing time of aircraft operating on the surface, resulting in a significant reduction in the number of aircraft conflicts, and thus, airport operational safety is ensured. This study has a high practical value and is expected to be applied in the real-time control decision of aircraft taxiing in the future.