Sympathy and Punishment: Evolution of Cooperation in Public Goods Game

An important way to maintain human cooperation is punishing defection. However, since punishment is costly, how can it arise and evolve given that individuals who contribute but do not punish fare better than the punishers? This leads to a violation of causality, since the evolution of punishment is prior to the one of cooperation behaviour in evolutionary dynamics. Our public goods game computer simulations based on generalized Moran Process, show that, if there exists a \'behaviour-based sympathy\' that compensates those who punish at a personal cost, the way for the emergence and establishment of punishing behaviour is paved. In this way, the causality violation dissipates. Among humans sympathy can be expressed in many ways such as care, praise, solace, ethical support, admiration, and sometimes even adoration; in our computer simulations, we use a small amount of transfer payment to express \'behaviour-based sympathy\'. Our conclusions indicate that, there exists co-evolution of sympathy, punishment and cooperation. According to classical philosophy literature, sympathy is a key factor in morality and justice is embodied by punishment; in modern societies, both the moral norms and the judicial system, the representations of sympathy and punishment, play an essential role in stable social cooperation.Public Goods Game, Cooperation, Social Dilemma, Co-Evolution, Sympathy, Punishment

Generative Adversarial Networks for Video-to-Video Domain Adaptation

Endoscopic videos from multicentres often have different imaging conditions, e.g., color and illumination, which make the models trained on one domain usually fail to generalize well to another. Domain adaptation is one of the potential solutions to address the problem. However, few of existing works focused on the translation of video-based data. In this work, we propose a novel generative adversarial network (GAN), namely VideoGAN, to transfer the video-based data across different domains. As the frames of a video may have similar content and imaging conditions, the proposed VideoGAN has an X-shape generator to preserve the intra-video consistency during translation. Furthermore, a loss function, namely color histogram loss, is proposed to tune the color distribution of each translated frame. Two colonoscopic datasets from different centres, i.e., CVC-Clinic and ETIS-Larib, are adopted to evaluate the performance of domain adaptation of our VideoGAN. Experimental results demonstrate that the adapted colonoscopic video generated by our VideoGAN can significantly boost the segmentation accuracy, i.e., an improvement of 5%, of colorectal polyps on multicentre datasets. As our VideoGAN is a general network architecture, we also evaluate its performance with the CamVid driving video dataset on the cloudy-to-sunny translation task. Comprehensive experiments show that the domain gap could be substantially narrowed down by our VideoGAN.Comment: Accepted by AAAI 202