cas/taxonomy/feature-default.php (governing feature)


Complex Adaptive Systems display emergent global features: ones transcending that of the system's individual elements.

Emergence refers to the unexpected manifestion of unique phenomena appearing in a complex system in the absence of top-down control. Emergent, integrated wholes are able to manifest through self-organizing, bottom-up processes, with these wholes exhibiting clear, functional, structures. These phenomena are intriguing in part due to their unexpectedness. Coordinated behaviors yield an emergent pattern or synchronized outcome that holds properties distinct from that of the individual agents in the system. Emergence can refer both to these novel global phenomena themselves (such as ant trails, Benard rolls or traffic jams) or to the mathematical regularities - such as power-laws -  associated with them.

Starling Murmuration - an emergent phenomena

When we see flocks of birds or schools of fish, they appear to operate as integrated wholes, yet the whole is somehow produced without any specific bird or fish being 'in-charge'. The processes leading to such phenomena are driven by networks of interactions that, because of feedback mechanisms,  gradually impose constraints or limits upon the members of the system (see Degrees of Freedom).  Recursive feedback between these members (or 'agents') take what was initially 'free' behavior, and gradually constrain or enslaves the behavior into coordinated regimes.

These coordinated, emergent regimes generally feature new behavioral or operational capacities that are not available to the individual element of the system. In addition, emergent systems often exhibit mathematical pattern regularities (in the form of Power Laws ) pertaining to the intensity of the emergent phenomena. These intensities tend to be observed in aspects such as spatial, topological or temporal distributions of the emergent features. For example, there are pattern regularities associated with earthquake magnitudes (across time) city sizes (across space), and website popularity (across links (or 'topologically')).

Quite a lot of research in complexity is interested in the emergence of these mathematical regularities, and sometimes it is difficult to decipher which feature of complexity is more important - what the emergent phenomena do (in and of themselves), versus the structural patterns or regularities that these emergent phenomena manifest.

Relation to Self-Organization:

Closely linked to the idea of emergence is that of self-organization, although there are some instances where emergence and self-organization occur in isolation from one another.


One interesting case of emergence without self-organization is associated with the so-called 'wisdom of crowds'. A classic example of the phenomena, (described in the book of the same name), involves estimating the weight of a cow at a county fair. Simultaneously, experts as well as non-experts are asked to estimate a cow's weight. Fair attendees are given the chance to guess a weight and put their guess into a box.  None of the attendees are aware of the estimates being made by others. Nonetheless, when all the guesses from the attendees are tallied (and divided by the number of guesses), the weight of the cow that the 'crowd' collectively determined is closer than the weight of the cow estimated by experts. The correct weight of the cow 'emerges' from the collective, but no self-organizing processes are involved - simply independent guesses.

Despite there being examples of emergence without self-organization (as well as self organization without emergence), in the case of Complex Adaptive Systems these two concepts are highly linked, making it is difficult to speak about one without the other. If there is a meaningful distinction, it is that Self-Organization focuses on the character of interactions occurring amongst the Bottom-up Agents of a complex system, whereas Emergence highlights the global phenomena that appear in light of theses interactions.


At the same time, the concepts are interwoven, since emergent properties of a system tend to constrain the behaviors of the agents forming that system. Hermann Haken frames this through the idea of an Enslaved State, where agents in a system come to be constrained as a result of phenomena they themselves created.


An interesting illustration of the phenomena of 'enslavement' can be found in ant-trail formation. Ants, that initially explore food sources at random, gradually have their random explorations constrained due to the signals provided by pheromones (which are deployed as ants that randomly discover food). The ants, responding in a bottom-up manner to these signals, gradually self-organize their search and generate a trail. The trail is the emergent phenomena, and self-organization - as a collective dynamic that is distributed across the colony - 'works' to steer individual ant behavior. That said, once a trail emerges, it acts as a kind of 'top-down' device that constrains subsequent ant trajectories.

Emergence poses ontological questions concerning where agency is located - that is, what is acting upon what. The source of agency becomes muddy as phenomena arising from agent behaviors (the local level) gives rise to emergent manifestations (the global level) which subsequently constrains further agent behaviors (and so forth). This is of interest to those interested in the philosophical implications of complexity.

There is a very tight coupling in these dynamics between a system's components and the environment that the components act within. Thus, a specific characteristic of the environment is that it also consists of system elements. Consequently, as elements shift in response to their environmental context, they are, in turn helping produce a new environmental context for themselves. This results in the systems components and the system environment forming a kind of closed loop of interactions. These kinds of loops of behaviors, that lead to forms of self-regulation, were the object of study for early Cybernetics thinkers.

Urban Interpretations:

The concept of Emergence has become increasingly popular in urban discourses. While some urban features come about through top-planning (like, for example, the decision to build a park), other kinds of urban phenomena seem to arise through bottom-up emergent processes (for example a particular park becoming the site of drug deals). It should be noted that not all emergent phenomena are positive! In some cases, we may wish to help steward along emergent characteristics that we deem to be positive for urban health, while in other cases we may wish to try to dismantle the kinds of feedback mechanisms that create spirals of decay or crime.

The concept of emergence can be approached very differently depending on the aims of a particular discourse. For example, Urban Modeling often highlights the emergence of Power Laws in the ratio of different kinds of urban phenomena. A classic example is the presence of power law distributions in city sizes, which looks at how the populations of cities in a country follows a power-law distribution,  but one can also examine power law distributions within rather than between cities, examining such characteristics such as road systems, restaurants, or other civic amenities.

Others, such as those engaged in the field of Evolutionary Economic Geography. (EEG) are intrigued by different kinds of physical patterns of organization.  EEG attunes to how 'clusters' of firms or 'agglomerations' appear in various settings, in the absence of top-down coordination.  They try to unpack the mechanisms whereby firms are able able to self-organize to create these clusters, rather then looking at any particular mathematical regularities or power-law attributes associated with such clusters.

Still other urban discourses, including Relational Geography and Assemblage Geography, are focused on how agents come together to create new structures or agents entities: which might  be buildings, institutions, building plans, etc. These discourses tend to focus on coordination mechanisms and flows that steer how such entities come to emerge.

Accordingly, different discourses attune to very different aspects fo complexity.


While this entry provides a general introduction to emergence (and self-organization), there are other interpretations of these phenomena that disambiguate these concepts with reference to Information theory. These interpretations focus upon the amount of information (in a Shannononian sense) required to describe self-organizing versus emergent dynamics.

While these definitions can be instructive, they remain somewhat controversial. There is no absolute consensus about how complexity can be defined using mathematical measures (for an excellent review on various measures, check the FEED for Ladyman, Lambert and Weisner, 2012). Often, an appeal is made to the idea of 'somewhere between order and randomness'. But this only tells us what complexity is not, rather than what it is. The explanation provided here is intended to outline the terminology in a more intuitive way, that, while not mathematically precise, makes the concepts workable.



Photo Credit and Caption: Image Credit: daniel-hjalmarsson-41Wuv1xsmGM-unsplash

Cite this page:

Wohl, S. (2022, 10 June). Emergence. Retrieved from

Emergence was updated June 10th, 2022.

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In Depth: Emergence

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James Ladyman, James Lambert & Karoline Wiesner, What is a complex system? - PhilPapers

Authors Abstract Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system, different examples of which are studied across many disciplines. However, there is no concise definition of a complex system, let alone a definition on which all scientists agree.

John Bonner's slime mold movies

Biology Professor Emeritus John Bonner's microscope films show the curiously collective nature of slime molds. Read more:

Why language might be the optimal self-regulating system - Lane Greene | Aeon Essays

Decades before the rise of social media, polarisation plagued discussions about language. By and large, it still does. Everyone who cares about the topic is officially required to take one of two stances.

The complexity of the ant world

This amazing video shows how the ants are able to build a complex structure in the absence of top-down control. The manner in which this can occur is part of what is studied in Complex Adaptive Systems Theory

This is a list of People that Emergence is related to.

Segregation model

Economist who developed one of the first cellular automata demonstrations: showing how segregation of agents will emerge as a phenomena due to simple rules that, in and of themselves, do not appear to be strongly linked to segregation outcomes.

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Santa Fe Institute; Fitness Landscape

Major complexity theorist associated with the Sante Fe institute, developed idea of a Fitness Landscape

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Fitness Landscapes

Fitness Landscapes / Path Analysis Learn more →

Synergetics | Enslaved States

Haken popularized the concepts of Enslaved States and 'synergetics'. The notion of 'enslavement' is similar to the idea of Attractor States, wherein a system will tend to gravitate towards a particular regime and then remain in that state unless there is a system Perturbation.

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This is a list of Terms that Emergence is related to.

A concept from Deleuzian philosophy - when distinct entities settle into synergies and act as a unit.

Relates to {{Assemblage-Geography}}

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Claude Bernard

Negative Feedback | stability Learn more →

This is a collection of books, websites, and videos related to Emergence

This is a list of Urban Fields that Emergence is related to.

Cellular Automata & Agent-Based Models offer city simulations whose behaviors we learn from. What are the strengths & weaknesses of this mode of engaging urban complexity?

There is a large body of research that employs computational techniques - in particular agent based modeling (ABM) and cellular automata (CA) to understand complex urban dynamics. This strategy looks at how rule based systems yield emergent structures.

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Many cities around the world self-build without top-down control. What do these processes have in common with complexity?

Cities around the world are growing without the capacity for top-down control. Informal urbanism is an example of bottom-up processes that shape the city. Can these processes be harnessed in ways that make them more effective and productive?

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Increasingly, data is guiding how cities are built and managed. 'Datascapes' are both derived from our actions but then can also steer them. How do humans and data interact in complex ways?

More and more, the proliferation of data is leading to new opportunities in how we inhabit space. How might a data-steered environment operate as a complex system?

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Cities traditionally evolved over time,  shifting to meet user needs. How might complexity theory help us  emulate such processes to generate 'fit' cities?

This branch of Urban Thinking consider how the nature of the morphologic characteristics of the built environment factors into its ability to evolve over time. Here, we study the ways in which the built fabric can be designed to support incremental evolution

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Across the globe we find spatial clusters of similar economic activity. How does complexity help us understand the path-dependent emergence of these economic clusters?

Evolutionary Economic Geography (EEG) tries to understand how economic agglomerations or clusters emerge from the bottom-up. This branch of economics draws significantly from principles of complexity and emergence, seeing the rise of particular regions as path-dependent, and looking to understand the forces that drive change for firms - seen as the agents evolving within an economic environment.

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Communicative planning  broadens the scope of voices engaged in planning processes. How does complexity help  us understand the productive capacity of these diverse agents?

A growing number of spatial planners are realizing that they need to harness many voices in order to navigate the complexities of the planning process. Communicative strategies aim to move from a top-down approach of planning, to one that engages many voices from the bottom up.

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Might the world we live in be made up of contingent, emergent 'assemblages'? If so, how might complexity theory help us understand such assemblages?

Assemblage geographers consider space in ways similar to relational geographers. However, they focus more on the temporary and contingent ways in which forces and flows come together to form stable entities. Thus, they are less attuned to the mechanics of how specific relations coalesce, and more to the contingent and agentic aspects of the assemblages that manifest.

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This is a list of Key Concepts that Emergence is related to.

Self-organization refers to processes whereby coordinated patterns or behaviors manifest in a system without the need for top-down control.

A system is considered to be self-organizing when the behavior of elements in the system can, together, arrive at a globally more optimal functional regimes compared to if each system element behaved independently. This occurs without the benefit of any controller or director of action. Instead, the system contains elements acting in parallel that will gradually manifest organized, correlated behaviors: Emergence.

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Complex Adaptive Systems become more 'fit' over time. Depending on the system, Fitness can take many forms,  but all involve states that achieve more while expending less energy.

What do we mean when we speak of Fitness? For ants, fitness might be discovering a source of food that is abundant and easy to reach. For a city, fitness might be moving the maximum number of people in the minimum amount of time. But fitness criteria can also vary - what might be fit for one agent isn't necessarily fit for all.

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Complex Systems can unfold in multiple trajectories. However, there may be trajectories that are more stable or 'fit'. Such states are considered 'attractor states'.

Complex Adaptive Systems do not obey predictable, linear trajectories. They are "Sensitive to Initial Conditions", such that small changes in these conditions can lead the system to unfold in unexpected ways. That said, in some systems, particular 'potential unfoldings' are more likely to occur than others. We can think of these as 'attractor states' to which a system will tend to gravitate.

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There would be some thought experiments here.

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