Spatial alignment, group strategy and non-kin selection enable the evolution of cooperation
This article considers a mechanism to explain the emergence and evolution of social cooperation. Selfish individuals tend to benefit themselves, which makes it hard for the maintenance of cooperation between unrelated individuals. We propose and validate that a smart group strategy can effectively facilitate the evolution of cooperation, provided cooperators spatially align whilst cooperating at a new level of alliance. The general evolutionary model presented here shows that a non-kin selection effect is a possible cause for cooperation between unrelated individuals and highlights that non-kin selection may be a hallmark of biological evolution.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs. Due to the lack of dense annotations, existing text-supervised methods can only learn to group an image i…
SegmentationSemantic SegmentationSentenceJoint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking
We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking. The key innovation of the proposed method is to perform group feature selection across both c…
channel selectionfeature selectionObject TrackingVisual Object TrackingUser Subgrouping in Multicast Massive MIMO over Spatially Correlated Rayleigh Fading Channels
Massive multiple-input-multiple-output (MaMIMO) multicasting has received significant attention over the last years. MaMIMO is a key enabler of 5G systems to achieve the extremely demanding data rates of upcoming service…
FairnessROCKET: Residual-Oriented Multi-Layer Alignment for Spatially-Aware Vision-Language-Action Models
Vision-Language-Action (VLA) models enable instruction-following robotic manipulation, but they are typically pretrained on 2D data and lack 3D spatial understanding. An effective approach is representation alignment, wh…
Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity
We propose a new algorithm for hyperparameter selection in machine learning algorithms. The algorithm is a novel modification of Harmonica, a spectral hyperparameter selection approach using sparse recovery methods. In p…
BIG-bench Machine LearningHyperparameter Optimization