Nonlinear social evolution and the emergence of collective action

Background: We focus on familial selection of Haldane in monogamous families in a diploid population, where the survival probability of each sibling is determined by altruistic food sharing with its siblings during starvation. An autosomal recessive-dominant or intermediate allele pair uniquely determines the altruistic or selfish behavior, which are coded by homozygotes. We focus on the case when additive cost and benefit functions determine the survival probability of each full sibling. Results: We provide conditions for the existence of the altruistic and selfish homozygote. We show that the condition of evolutionary stability of altruism depends on the genotype-phenotype mapping. Furthermore, if the offspring size increases then the condition of evolutionary stability of altruism becomes stricter. Contrary to that, for the evolutionary stability of selfish behavior it is enough if the classical rule of Hamilton does not hold. Moreover, when the classical rule of Hamilton holds and the condition of evolutionarily stability of altruism does not hold, then the selfish and altruistic phenotypes coexist. Conclusions: In summary, the classical rule of Hamilton is a sufficient condition for the existence of altruism, but it alone does not imply the evolutionary stability of the pure altruistic homozygote population when the altruistic siblings share the cost of altruism.

show abstract

Utilizing Potential Field Mechanisms and Distributed Learning to Discover Collective Behavior on Complex Social Systems

Zhang,

Qu,

Chen

2024

Symmetry

View full text Add to dashboard Cite

This paper proposes the complex dynamics of collective behavior through an interdisciplinary approach that integrates individual cognition with potential fields. Firstly, the interaction between individual cognition and external potential fields in complex social systems is explored, integrating perspectives from physics, cognitive psychology, and social science. Subsequently, a new modeling method for the multidimensional potential field mechanism is proposed, aiming to reduce individual behavioral errors and cognitive dissonance, thereby improving system efficiency and accuracy. The approach uses cooperative control and distributed learning algorithms to simulate collective behavior, allowing individuals to iteratively adapt based on local information and collective intelligence. Simulations highlight the impact of factors such as individual density, noise intensity, communication radius, and negative potential fields on collective dynamics. For instance, in a high-density environment with 180 individuals, increased social friction and competition for resources significantly decrease collective search efficiency. Validation is achieved by comparing simulation results with existing research, showing consistency and improvements over traditional models. In noisy environments, simulations maintain higher accuracy and group cohesion compared to standard methods. Additionally, without communication, the Mean Squared Error (MSE) initially drops rapidly as individuals adapt but stabilizes over time, emphasizing the importance of communication in maintaining collective efficiency. The study concludes that collective behavior emerges from complex nonlinear interactions between individual cognition and potential fields, rather than being merely the sum of individual actions. These insights enhance the understanding of complex system dynamics, providing a foundation for future applications in adaptive urban environments and the design of autonomous robots and AI systems.

show abstract

Nonlinear social evolution and the emergence of collective action

Cited by 3 publications

References 108 publications

The coalescent in finite populations with arbitrary, fixed structure

The coalescent in finite populations with arbitrary, fixed structure

Survival cost sharing among altruistic full siblings in Mendelian population

Utilizing Potential Field Mechanisms and Distributed Learning to Discover Collective Behavior on Complex Social Systems

Contact Info

Product

Resources

About