A quantitative comparison of virtual and physical experimental paradigms for the investigation of pedestrian responses in hostile emergencies.

Modern experiments investigating human behaviour in emergencies are often implemented in virtual reality (VR), due to the increased experimental control and improved ethical viability over physical reality (PR). However, there remain questions regarding the validity of the results obtained from these environments, and no full validation of VR experiments has yet appeared. This study compares the results of two sets of experiments (in VR and PR paradigms) investigating behavioural responses to knife-based hostile aggressors. This study quantitatively analyses these results to ascertain whether the different paradigms generate different responses, thereby assessing the use of virtual reality as a data generating paradigm for emergencies. The results show that participants reported almost identical psychological responses. This study goes on to identify minimal differences in movement responses across a range of predictors, noting a difference in responses between genders. As a result, this study concludes that VR can produce similarly valid data as physical experiments when investigating human behaviour in hostile emergencies, and that it is therefore possible to conduct realistic experimentation through VR environments while retaining confidence in the resulting data. This has major implications for the future of this type of research, and furthermore suggests that VR experimentation should be performed for both existing and new critical infrastructure to understand human responses in hostile scenarios.

Evolutionary Game and Simulation of Green Housing Market Subject Behavior in China.

In China, driven by the national "3060" double carbon targets (i.e., reaching peak carbon emissions by 2030 and carbon neutrality by 2060), green housing has become one of the major fields to reduce carbon emissions, facilitating the achievement of the double carbon targets. Promoting the growth of green housing is an important way for the real estate industry to achieve low-carbon transformation and improve the quality of housing. Meanwhile, the construction industry also can benefit from green housing to achieve its energy conservation and emission reduction targets. Therefore, it is critical to boost and maintain the sustainable growth of the green housing market in China. However, the literature has not focused attention on the market behavior of the green housing market in China. This study proposes a tripartite evolutionary game model to investigate the subject behavior of the green housing market in China. This model consists of three major subjects in a green housing market: developers, consumers, and governments. Based on this model, this study analyzes the stability of the strategy options for each stakeholder and identifies the stable conditions of strategy portfolios to reach the equilibrium points of the game system. The validity of the proposed tripartite evolutionary game model is tested through the simulation of the impacts from various factors on system evolution. According to the impacts of factors and the stable conditions of strategies, this paper puts forward relevant policy suggestions for the healthy and sustainable growth of China's green housing market.

Common Laws Driving the Success in Show Business.

In this paper, we want to find out whether gender bias will affect the success and whether there are some common laws driving the success in show business. We design an experiment, set the gender and productivity of an actor or actress in a certain period as the independent variables, and introduce deep learning techniques to do the prediction of success, extract the latent features, and understand the data we use. Three models have been trained: the first one is trained by the data of an actor, the second one is trained by the data of an actress, and the third one is trained by the mixed data. Three benchmark models are constructed with the same conditions. The experiment results show that our models are more general and accurate than benchmarks. An interesting finding is that the models trained by the data of an actor/actress only achieve similar performance on the data of another gender without performance loss. It shows that the gender bias is weakly related to success. Through the visualization of the feature maps in the embedding space, we see that prediction models have learned some common laws although they are trained by different data. Using the above findings, a more general and accurate model to predict the success in show business can be built.