A few hundred years ago, a churning polychromatic chaos in Jupiter’s atmosphere gave birth to the huge vortex that we call the Great Red Spot. The frenetic firing of billions of neurons in your brain gives rise to your subjective and unitary experience of reading these words. As pedestrians each seek to weave a path on a crowded sidewalk, they begin to follow one another, falling into streams that no one ordained or consciously chose.
These are examples of emergent phenomena—large-scale patterns and organizations arising from numerous interactions between parts. Despite their ubiquity, there is no universally accepted scientific theory to explain emergence. Broadly, the behavior of a complex system might be considered emergent if it can’t be predicted from the properties of the parts alone. But when do such big structures and patterns come into existence, and what is the threshold as to when something is emergent, and when something isn’t? Confusion has reigned.
The Quest for a Theory of Emergence
As physicist Jim Crutchfield of the University of California, Davis, puts it, “It’s just a muddle.” According to Anil Seth, a neuroscientist at the University of Sussex in England, the problem is a lack of the right tools—”not only the tools for analysis but the tools for thinking.” Measuring and theorizing emergence could help us think about these systems more richly.
Over the past few years, a community of physicists, computer scientists, and neuroscientists has been working toward a better understanding of emergence. They have developed theoretical tools for identifying when emergence occurs. In February, Fernando Rosas, a complex-systems scientist at Sussex, along with Seth and five coauthors, introduced a framework for understanding how emergence arises.
A New Framework for Understanding Emergence
Rosas uses the term “software in the natural world” to refer to emergent phenomena. The software on your laptop, for example, runs without needing to keep track of all the microscale information about electrons in the computer circuitry; similarly, emergent phenomena are governed by macroscale rules that seem self-contained.
The researchers were able to identify criteria for which systems have this hierarchical structure using a mathematical formalism called computational mechanics, and tested the criteria on several model systems known to exhibit emergent-type phenomena, including neural networks and Game of Lifestyle cellular automata. They showed how these systems displayed the predicted relationship between microscopic and macroscopic scales.
The Complexity of Closure
Rosas approached the question of emergence from many different angles, drawing on music, philosophy, mathematics, and electrical engineering. He discussed whether the brain is like a computer. The software determines identical outputs for a given set of inputs, but electrons do not travel the same trajectories every time. In the same way, the behavior of the brain is regular, even though neural activity is never identical.
Rosas and his collaborators distinguished three varieties of closure in emergent systems:
- Informational Closure: The macro level is not helped in prediction by the details below.
- Causal Closure: The macro level’s future can be fully determined by the macro level alone, never helped by lower-level details.
- Computational Closure: Through computational mechanics, involves how the interactions of parts are represented by creating epsilon, ε, machines that predict future states.
Practical Applications and Implications
The researchers tried out their notions on model systems, including random walks through networks and artificial neural networks. In such systems, self-organization into states that reliably recognize macroscopic patterns despite differences on the microscopic level had already taken place.
The framework of Rosas helps to see when researchers can develop predictive coarse-grained models. It has repercussions for understanding cause and effect in complex systems. Traditionally, causation has been viewed as bottom-up, but in emergent systems, causation can act at a higher level, independently of the lower-level details.
The Future of Emergence Research
Even as the arguments continue, the framework Rosas and his colleagues have provided offers a clear route to understanding emergence. Optimistically, researchers like Crutchfield believe we will know exactly how these phenomena work within a decade. This research deepens our understanding of complex systems but touches on profound questions about the structure of the universe and the nature of causation.
The study of emergent phenomena is an area in development that is interdisciplinary in scope. It is only with the elaboration and improvement of theoretical tools that scientists are beginning to realize how higher-scale patterns and behaviors actually emerge from interactions of smaller elements, like atoms or animals, into a new understanding of how our world works at the most basic level.
