Kinship can hinder cooperation in heterogeneous populations

Kin selection and direct reciprocity are two most basic mechanisms for promoting cooperation in human society. Generalizing the standard models of the multi-player Prisoner's Dilemma and the Public Goods games for heterogeneous populations, we study the effects of genetic relatedness on cooperation in the context of repeated interactions. Two sets of interrelated results are established: a set of analytical results focusing on the subgame perfect equilibrium and a set of agent-based simulation results based on an evolutionary game model. We show that in both cases increasing genetic relatedness does not always facilitate cooperation. Specifically, kinship can hinder the effectiveness of reciprocity in two ways. First, the condition for sustaining cooperation through direct reciprocity is harder to satisfy when relatedness increases in an intermediate range. Second, full cooperation is impossible to sustain for a medium-high range of relatedness values. Moreover, individuals with low cost-benefit ratios can end up with lower payoffs than their groupmates with high cost-benefit ratios. Our results point to the importance of explicitly accounting for within-population heterogeneity when studying the evolution of cooperation

H2-Mapping: Real-time Dense Mapping Using Hierarchical Hybrid Representation

Constructing a high-quality dense map in real-time is essential for robotics, AR/VR, and digital twins applications. As Neural Radiance Field (NeRF) greatly improves the mapping performance, in this paper, we propose a NeRF-based mapping method that enables higher-quality reconstruction and real-time capability even on edge computers. Specifically, we propose a novel hierarchical hybrid representation that leverages implicit multiresolution hash encoding aided by explicit octree SDF priors, describing the scene at different levels of detail. This representation allows for fast scene geometry initialization and makes scene geometry easier to learn. Besides, we present a coverage-maximizing keyframe selection strategy to address the forgetting issue and enhance mapping quality, particularly in marginal areas. To the best of our knowledge, our method is the first to achieve high-quality NeRF-based mapping on edge computers of handheld devices and quadrotors in real-time. Experiments demonstrate that our method outperforms existing NeRF-based mapping methods in geometry accuracy, texture realism, and time consumption. The code will be released at: https://github.com/SYSU-STAR/H2-MappingComment: Accepted by IEEE Robotics and Automation Letter