Emergence

Complex systems theory: This field explores how systems with multiple interacting components can exhibit emergent properties that cannot be predicted from the individual parts.

Chaos theory: Chaos theory studies the behavior of non-linear systems that are highly sensitive to initial conditions. It often involves chaotic behavior that spontaneously self-organizes.

Network theory: This field focuses on the structure and behavior of networks, in which nodes interact with each other according to certain rules, and can exhibit emergent properties such as robustness, adaptability, and connectivity.

Artificial intelligence: Self-organizing systems are often used to model and improve machine learning algorithms, which, in turn, can lead to machine learning systems that improve of their own accord.

Evolutionary biology: Emergence is often seen at different levels of biological organization, from individual organisms, to populations, to ecosystems.

Social complexity: Studies of self-organization often focus on social systems, like markets, online communities, or insurgent movements, and how these systems can exhibit complex patterns of behavior.

Swarm intelligence: This field studies collective behavior of groups of agents, like swarms of birds or insects, or human crowds, that can work together to solve complex problems or achieve coordinated goals.

Game theory: Game theory studies how agents interact with one another when given conflicting goals, and how these interactions lead to the emergence of new strategies and behaviors.

Information theory: Information theory studies how the structure of information can give rise to emergent phenomena, and how these phenomena can be harnessed to create self-organizing systems.

Psychology: Self-organizing systems can be seen at different levels of human behavior, from individual decision-making to group dynamics, and understanding these systems can teach us more about how humans act and interact with one another.

Strong emergence: The whole system is more than just the sum of its parts, and the properties of the system cannot be predicted based solely on the properties of its individual components.

Weak emergence: The properties of the system arise from interactions among its individual components, but can be predicted based on those interactions.

Collective emergence: Complex patterns or behaviors arise from the collective actions of many individuals. Example: flocking of birds or schooling of fish.

Context-dependent emergence: The emergent properties of a system depend on the environment in which it is situated.

Hierarchical emergence: Emergent properties arise at different levels of organization within a system, with each level building on the emergent properties of the level below it.

Spatial emergence: Physical space plays a role in shaping the emergent properties of a system.

Temporal emergence: The emergent properties of a system change over time, as patterns of interaction among its components evolve.

Evolutionary emergence: Emergent properties arise as a result of evolutionary processes, such as natural selection or genetic mutation.

Adaptive emergence: Emergent properties arise as a result of the system adapting to its environment, and changing its behavior or structure accordingly.

Behavioral emergence: Emergent properties arise as a result of the behaviors of individual agents within a system, which may be simple or complex.