Evolutionary phenomena, or their products, indicate emergent phenomena. The more we explore the condition of emergence, the more we see that true emergence can only be found in evolutionary life phenomena. However, the emergence-creating mechanism has not yet been identified. The self-organization phenomenon refers to ubiquitous phenomena exhibited by open, non-equilibrium, nonlinear systems, whereas emergent phenomenon sequentially repeats self-organization, which accompanies the updating of the micro rules that underlie self-organization. Examples of this process in relation to emergent phenomena include the computer, the Internet, the iPhone and so on. For example, society changes by developing new technologies, and research needs change as society changes, which creates new devices. Self-organization is the one-way formation of macroscopic order from micro dynamics, whereas emergent phenomenon is the circulation of recurrent information from macro to micro, and vice versa. However, there are some differences between them. Therefore, this raises the question of whether we can capture life phenomena as examples of self-organization, or whether we need to create a new theory to understand those phenomena.Īn emergent phenomenon is an extension of this self-organization phenomenon.

These shape and pattern formations are considered to be typical examples in life and non-life systems, although we still cannot spontaneously generate life from non-living matter. In some magnetic materials, it is known that hierarchical ordered magnetic spin structures appear to be random in a spatial direction, while the temporal correlation is sustained and organized (called spin glass).Įxamples of these types of self-organization phenomena are common in the natural world. For instance, examples of well-known cooperative phenomena include the transition phenomenon of a laminar flow pattern to a turbulent flow, as indicated by a fluid mechanical system, and various types of phase transition phenomena exhibited by a magnetic spin system. The self-organization of structures and patterns has been well studied in various fields of physics and chemistry. Self-organization is similar to but different from emergence. In this paper, we discuss the concept of emergent phenomena based on the results of a concrete simulation and analysis of large web service data, by referring to their relation to adjacent possible theory. Specifically, in that interview, he claims that a biosphere can be viewed as a secular or long-term trend and it can maximize the rate of exploration of the adjacent possible of an existing organization. Stuart Kauffman advocates for the idea of the adjacent possible to advance the understanding of emergent phenomena, and he references it as a source of biological evolution. Steven Johnson interprets emergence more widely, extending it to a phenomenon occurring in a human community. Therefore, consecutive micro–macro recurrent self-organization is defined as an emergent phenomenon.įor example, David Christian states that emergence is the deepest and most beautiful concept in science. A group of elements creates a self-organizing structure, which governs the individual micro rules and creates a new macro structure. To this end, an emergent phenomenon is defined as the macroscopic layers of patterns and structures that appear as a result of cooperative phenomena between autonomously behaving elements. This paper discusses life as an emergent phenomenon. This article is part of the themed issue ‘Reconceptualizing the origins of life’. We argue that excessive information and material flow can become a source of innovation.

We contextualize these results in terms of adjacent possible theory and as a new way to understand collective intelligence. In this case, novelty is not considered to arise randomly rather, it is generated as a result of a large and structured network. We show that the generation rate of novel tags and their usage frequencies are high in the higher-order cliques. In the second analysis, large web data, consisting of shared photos with descriptive tags, tended to group together users with similar tendencies, allowing the network to develop a core–periphery structure.

In the boid swarm simulations, the nucleation, organization and collapse dynamics were found to be more diverse in larger flocks than in smaller flocks. We discuss this hypothesis in relation to large-scale boid simulations and web data. A large group with a special structure can become the mother of emergence.

Emergent phenomena