The concept of self-organization has been used to explain collective animal behavior, where simple interactions between individuals lead to complex group patterns. Examples include ant pheromone trails, cockroach aggregation, and fish school dynamics, all of which can be described by simple rules. However, animals are complex entities, and some behaviors, like honey bee foraging, require more detailed behavioral algorithms. The key to understanding collective behavior lies in identifying the principles of individual behavior and information flow. These principles, such as positive feedback, response thresholds, and individual integrity, are observed across different animal societies. Future research aims to classify these principles, understand their group-level effects, and explore why they evolved in diverse systems. Mathematical models, like those used in self-organization, help predict and explain collective behavior. Examples include ant trails, fish schools, and human crowds. These models show that systems can be more than the sum of their parts, with outputs often deviating from normal distributions. The central limit theorem explains symmetrical structures formed by independent actions. However, systems are sensitive to initial conditions, as seen in ant trails and cockroach shelter choices. While self-organization provides insights, individual complexity and behavioral algorithms are essential for understanding animal societies. Principles like positive feedback, response thresholds, and synchronization underlie collective behavior, and studying these can help reconcile individual and group-level complexity. The goal is to identify principles that govern collective behavior, enabling better understanding of both animal and human societies.The concept of self-organization has been used to explain collective animal behavior, where simple interactions between individuals lead to complex group patterns. Examples include ant pheromone trails, cockroach aggregation, and fish school dynamics, all of which can be described by simple rules. However, animals are complex entities, and some behaviors, like honey bee foraging, require more detailed behavioral algorithms. The key to understanding collective behavior lies in identifying the principles of individual behavior and information flow. These principles, such as positive feedback, response thresholds, and individual integrity, are observed across different animal societies. Future research aims to classify these principles, understand their group-level effects, and explore why they evolved in diverse systems. Mathematical models, like those used in self-organization, help predict and explain collective behavior. Examples include ant trails, fish schools, and human crowds. These models show that systems can be more than the sum of their parts, with outputs often deviating from normal distributions. The central limit theorem explains symmetrical structures formed by independent actions. However, systems are sensitive to initial conditions, as seen in ant trails and cockroach shelter choices. While self-organization provides insights, individual complexity and behavioral algorithms are essential for understanding animal societies. Principles like positive feedback, response thresholds, and synchronization underlie collective behavior, and studying these can help reconcile individual and group-level complexity. The goal is to identify principles that govern collective behavior, enabling better understanding of both animal and human societies.