What is Emergence?

Thinking in terms of bottom-up systems
Print Friendly, PDF & Email

Presented at The Developing Group, 16 Feb 2002

A little while ago someone gave us feedback that they felt our book, Metaphors in Mind, was a doorway into another way of thinking, and that while they hadn’t gone through the door, they appreciated knowing another world existed.  

At each Developing Group day we aim to create opportunities for you to enhance your ability to move freely into and out of that world.

We believe one of the keys is being able to think in terms of ’emergence’, a fundamental feature of the dynamics of self-organising systems:

“They are bottom-up systems, not top-down.  They get their smarts from below.  In a more technical language, they are complex adaptive systems that display emergent behavior.  In these systems agents residing on one scale start producing behavior that lies one scale above them: ants create colonies; urbanites create neighbourhoods; simple pattern-recognition software learns how to recommend new books.  The movement from low-level rules to higher-level sophistication is what we call emergence.” (Johnson, p. 18)

For what purpose?

The whole point of The Developing Group is not for us to have another piece of knowledge called ’emergence’; rather it is to learn to think and operate in this new way. A way that is congruent with the subject matter: bottom-up, circular feedback pathways, indirect control. While we will often talk about emergence, that is only a means to create the conditions for you to ‘think emergently’.

Of course, thinking top-down, linear cause-effect and centralised control is valuable — which is just as well because it is practised by the great majority of our clients (and us!). In fact,

“Some researchers argue that the centralised mind-set is hard wired into our brain; in other words, we default to top-down explanations and only reconcile ourself to bottom-up explanations after extensive training.”  (Johnson, p. 243)

When we see repeated structure emerging out of apparent chaos, our first impulse is to build a centralized model to explain that behavior. It’s as if we can’t help looking for nonexistent ‘decison-makers’, ‘controllers’ or ‘pacemakers’ [cells that order the behavior of other cells].

To not make sense of the world this way requires us to consciously attend to a different set of patterns.

Naturally we believe thinking emergently has some great advantages:

First, according to a growing number of scientists, all living systems are self-organising, complex-adapative and display emergent behaviour. In fact, as Humberto Maturana and Francisco Varela pointed out: these are the defining characteristics of life. Given our bodies operate this way, it would be very strange if ‘cognition’, and more generally ‘mind’, did not also function in a similar fashion.

Second,  we’ve concluded that bottom-up, inductive thinking is a prerequisite for modelling (which maybe is why modelling is still such a rare skill). This creates an apparent paradox. Modelling involves creating a representation of the way a system works that is congruent/isomorphic with that system. If the client/exemplar thinks top-down, then our model of their thinking will also need to be top-down — but our tools for creating our model need not be. We can learn to distinguish the product from the production processes that create it.

Third, thinking emergently changes what we pay attention to and therefore influences the general drift of our questions. It allows us to fully acknowledge our clients’ maps of the world while not being seduced by their top-down inherent logic.

Fourth, it’s a stretch. It gets us to break our habitual thinking and, a la Carlos Castaneda, we believe this is a useful exercise in its own right.

We will approach the topic of emergence in three phases:

 – What is it?
 – How do I recognise it?
 – How do I make use of that knowledge?

And rather than give you a top-down explanation, following are a number of quotations from which we hope a “pattern which connects” emerges. To help in this process we recommend you:

(a) Read the following a couple of times and, whether the author is referring to ants, brains, cities or software, wonder how it applies to metaphor landscapes.  On Feb 16 we’ll provide more interactive opportunities to experience and link your learning to working with clients.

(b) Review pages 29-48 of Chapter 2 of Metaphors in Mind.

(c) Bring along your own favourite example of emergence.

Unless otherwise stated, all quotes are from the highly recommended and easy to read book, EMERGENCE: The Connected Lives of Ants, Brains, Cities and Software by Steven Johnson (2001).

What is emergence?

“In the early 1920’s, the philosopher C. D. Braod coined the term ’emergent properties’ for those properties that emerge at a certain level of complexity but do not exist at lower levels.” (Fritjof Capra, The Web of Life, p. 28)

“The reason why higher-level [phenomena] can be studied at all is that under special circumstances the stupendously complex behaviour of vast numbers of [components] resolves itself into a measure of simplicity and comprehensibility. This is called emergence: high-level simplicity ’emerges’ from low-level complexity. High-level phenomena about which there are comprehensible facts that are not simply deducible from lower-level theories are called emergent phenomena.” (David Deutsch, The Fabric of Reality, p. 20)

“[Cities] are patterns of human movement and decision-making that have been etched into the texture of city blocks, patterns that are then fed back to the residents themselves, altering their subsequent decisions. …

A city is a kind of pattern-amplifying machine: its neighbourhoods are a way of measuring and expressing the repeated behavior of larger collectives — capturing information about group behavior, and sharing that information with the group.  Because those patterns are fed back to the community, small shifts in behavior can quickly escalate into larger movements: upscale shops dominate the main boulevards, while the working class remains clustered invisibly in the alleys and side streets; the artists live on the Left Bank, the investment bankers in the Eighth Arrondissement.

You don’t need regulations and city planners deliberately creating these structures. All you need are thousands of individuals and a few simple rules of interaction.”  (Johnson, pp. 40-41)

Schematically the process of emergence can be represented:

Using Ken wilber’s terms we can say: At the lower level the components demonstrate ‘agency’ through their autonomous behaviour and ‘communion’ through their interactions with other components. At the higher level the self-organised pattern is the “agency” while the “communion” is the interaction with other systems at this level.

Note also that this feedback loop is ‘vertical’ (across levels) whereas we are more familar with ‘horizontal’ (within level) feedback loops.

“Emergent complexity without adaption [to a larger system] is like the intricate crystals formed by a snow flake: it’s a beautiful pattern, but it had no function.  The forms of emergent behavior that we’ll examine in this book show the distinctive quality of growing smarter over time, and of responding to the specific and changing needs of their environment.  In that sense, most of the system’s we’ll look at are more dynamic …: they rarely settle on a single, frozen shape; they form patterns in time as well as space.”  (Johnson, p. 20)

“What unites these different phenomena [ant colonies, brains, cities, new types of software] is a recurring pattern and shape: a network of self-organization, of disparate agents that unwittingly create a higher-level order.  …  At each scale, the laws of emergence hold true.” (Johnson, p. 21)

“As noted by Farmer and Packard (1986), the study of self-organizing systems is, in some ways, the ‘related opposite’ of the study of chaos: in self-organizing systems, orderly patterns emerge out of lower-level randomness; in chaotic systems, unpredictable behavior emerges out of lower-level deterministic rules.”  (Mitchell Resnick quoted by Johnson, p. 237)

‘Success’ [from the viewpoint of the self-organising system] is the ability to survive repeated selective pressure. The new world-view implicit in a theory that solves a problem [is an] emergent property of the problem. In other words, obtaining solutions is inherently complex. Evolution — especially the focused, purposeful form of trial and error — is the only [?] way.” (David Deutsch, The Fabric of Reality, p. 68 Note: deleted words are not marked)

“Emergent simplicities are the peaks in the landscape of the possible. The bigger the peak, the more important that simplicity tends to be.” (Jack Cohen & Ian Stewart, The Collapse of Chaos, p. 395)

“Can we write down the equations for emergence? The short answer is no. … ‘Equation’ is in any case the wrong image; the formulation of detailed laws is a reductionist concept, and the whole point about emergence is that it is not reductionist.” (Jack Cohen & Ian Stewart, The Collapse of Chaos, p. 436)

Examples of Emergence:

  • Termites ‘constructing’ a nest
  • Birds flying in a ‘V’
  • Amazon.com’s book recommending software
  • Fish swimming in a shoal
  • Neighbourhoods that survive for centuries (e.g. Por Santa Maria in Florence has had silk traders since 1100 AD.)
  • Development of an embryo
  • Consciousness and Language
  • Habits and addictive behaviour

How to think emergence

“To see it [emergence] as a pattern you needed to encounter it in several contexts.”  (Johnson, p. 18)

“The fundamental law of emergence: the behavior of individual agents is less important than the individual system.” (Johnson, p. 145)

“I think the most important habits of thought are these:

    1. To think about processes;
    2. To work inductively, reasoning from particulars to the general rather than the reverse;
    3. To seek for ‘unaverage’ clues involving very small quantities, which reveal the way larger and more ‘average’ quantities are operating.” (Jane Jacobs quoted by Johnson, p. 237)

“My concerns in Artificial Intelligence were not so much the actual processing as they were in how systems change, how they evolve — in a word, how they learn.” (Oliver Selfridge, quoted by Johnson, p. 53)

“The key here is that life does not simply reduce down to transcribing static passages from our genetic scripture.  Cells figure out which passages to pay attention to by observing signals from the cells around them: only with that local interaction can ‘complex’ neighbourhoods of cell types come into being.  The Nobel laureate Gerald Edleman calls this process topobiology, from the Greek word for ‘place,’ topos.  

Cells rely heavily on the code of DNA for development, but they also need a sense of place to do their work.  Indeed, the code is utterly worthless without the cell’s ability to determine its place in the overall organism, a feat that is accomplished by the elegant strategy of paying attention to one’s neighbours.  As [Matt] Ridley writes, ‘the great beauty of embryo development, the bit that human beings find so hard to grasp, is that it is a totally decentralized process’.” (Johnson, p. 86)

“You can’t think of a system … as a purely representational entity, the way you think about a book or a movie.  It is partly representational, of course:  … The medium is still the message… — it’s just that there is another level to the experience, a level that our critical vocabularies are only now finding words for.  … There is a medium, a message, and an audience.  … The difference is that those elements exist alongside a set of rules that govern the way the messages flow through the system. … To understand how these [systems] work, you have to analyze the message, the medium, and the rules … It’s an algorithmic problem, then, and not a representational one.  It’s the difference between playing a game of Monopoly and hanging a Monopoly board on your wall.” (Johnson, pp. 157-159)

Our restatement of Steven Johnson’s “five fundamental principles” (pp 77-79) as tips for modelling self-organising systems are:

  • More examples are better: Studying a few ants will never lead to an understanding of the global behaviour of the colony.
  • Low-level ignorance is useful: Lose a few ants and it doesn’t make much difference.
  • Notice how the system responds to random encounters: Individual ants will stumble across a new resource which increases the adaptiveness of the whole (and reduces the possibility of getting stuck on a ‘false peak’).
  • Notice the patterns in the signs: Ants respond the the frequency of ant encounters and the gradient of pheromone trails, not to messages from individual ants.
  • Components pay most attention to their neighbours: In this way swarm logic leads to global wisdom.

Johnson’s other “four core principles” are “neighbour interaction, pattern recognition, feedback, and indirect control.”  (p. 22)

Neighbour Interaction

“Local turns out to be the key term in understanding the power of swarm logic.  We see emergent behavior in systems like ant colonies when the individual agents in the system pay attention to their immediate neighbours rather than wait for orders from above.  They think locally and act locally, but their collective action produces global behavior … the perceptual world of an ant, in other words, is limited to the street level.  There are no bird’s eye views of the colony, no ways to perceive the overall system.  …  ‘Seeing the whole’ is both a perceptual and conceptual impossibility for any member of the ant species.” (Johnson, pp. 74-75)

“Ants are not born to do a certain task; an ant’s function changes along with the conditions it encounters, including the activities of other ants.”  (Deborah Gordon, quoted in Johnson, p. 261)

Pattern recognition

“The specialization of the city makes it smarter, and more useful for its inhabitants.  And the extraordinary thing again is that this learning emerges without anyone even being aware of it.  Information management — subduing the complexity of a large-scale human settlement — is the latent purpose of a city, because when cities come into being, their inhabitants are driven by other motives, such as safety or trade.” (Johnson, p. 109)

Feedback [pathways]

“All decentralized systems rely extensively on feedback, for both growth and self-regulation.”  (Johnson, p. 133)

“Negative feedback, then, is a way of reaching an equilibrium point despite unpredictable — and changing — external conditions.  The ‘negativity’ keeps the system in check, just as ‘positive feedback’ propels [the] system onward.” (Johnson, p. 138)

Our technical definitions of the two kinds of feedback pathways are taken from Systems Theory, and are not to be confused with the more popular meaning:

  • Negative feedback is any self-perpetuating process where an action produces a reaction which in turn reduces the condition responsible for the first action.  This reduces the need for repeating the first action which in turn reduces the reaction, and so on, leading to a fixed point.
  • Positive feedback is any self-amplifying process where an action produces a reaction which in turn intensifies the condition responsible for the first action.  This intensifies the need for repeating the first action which in turn intensifies the reaction, and so on, leading to an escalation.

From top-down viewpoint, negative feedback ‘dampens down’ while positive-feedback ‘ramps up’. Both self-perpetuating and self-amplifying feedback is required for system survival.  Norbert Wiener called the knack for self-regulation: homeostasis.

Indirect Control

In a self-organising system there is no centralised control. The system governs and learns from itself – from its ‘footprints’. Emergence of new forms and new behaviours happen naturally when the necessary conditions exist.

“The designer [of computer games based on emergence] controls the micromotives of the player’s actions. But the way those micromotives are exploited – and the macrobehavior they generate – are out of the designer’s control. They have a life of their own” (Johnson, p. 178)

Making use of emergence

All quotes from Steven Johnson:

“Most of the time, making an emergent system more adaptive entails tinkering with different kinds of feedback.”  (p. 137)

“The likelihood of a feedback loop correlates directly to the general interconnectedness of the system.”  (p. 134)

“In a real sense, our personalities are partially the sum of all these invisible feedback mechanisms; but to begin to understand those mechanisms, you need additional levels of feedback.”  (p. 142)

“You couldn’t tell any of that [the emergent behaviour] just by looking at the original instruction set.  You have to make it live before you can understand how it works.” (p. 165)

“I have carefully examined their instruction sequences [of the programmes birthed by Danny Hillis’ bottom-up programme generation software], but I do not understand them: I have no simple explanation of how the programmes work than the instruction sequences themselves. It may be that the programmes are not understandable.” (p. 173)

“I think they [today’s computer game-playing kids] have developed another skill, one that almost looks like patience: they are more tolerant of being out of control, more tolerant of that exploratory phase where the rules don’t all make sense, and where few goals have been clearly defined.”  (p. 176-177)  [NB. All skills a modeller needs!]

“You can conquer gridlock by making the grid itself smart.”  (p. 231)

“Many organisations nowadays are consciously trying to figure out how they can use self-organising principles without becoming either disintegrated or inert — in short, as avatars of fruitful complexity.  Ecotrust lists these three requirements: (a) autonomous agents able to make independent decisions within a framework of relatively simple rules; (b) moderately dense network and web connections among the agents — that is, the organisation’s part; and (c) vigorous experimentation by agents, disciplined by responding to feedback on results.”   (Jane Jacobs quoted in Johnson, p. 264)

“We contribute to emergent intelligence, but it is almost impossible for us to perceive that contribution, because our lives unfold on the wrong scale.” (p. 100)


Fritjof Capra, The Web of Life, HarperCollins, 1996.

Jack Cohen & Ian Stewart, The Collapse of Chaos, Penguin Books, 1995,

David Deutsch, The Fabric of Reality, Penguin Books, 1998.

Steven Johnson, Emergence: The Connected Lives of Ants, Brains, Cities and Software, Allen Lane The Penguin Press, London, 2001.

Humberto Maturana and Francisco Varela, The Tree of Knowledge, (1992)


A couple of years after this workshop. David Grove became very interested in, what he called ‘Emergent Knowledge” For once we were ahead of David. We had latched on to the importance of ‘emergence’ several years prior to David’s interest. See:

Symbolic Modelling and the Emergence of Background Knowledge’, Rapport 39, Spring 1998.

And Metaphors in Mind (2000):

The Five-Stage Therapeutic Process is a framework for facilitating clients to self-model the way their metaphor landscape is organised and evolves. Although the five stages are presented sequentially, the process is not a linear procedure; rather it is an emergent, systemic and iterative way of conducting psychotherapy. (p. 39)

Symbolic Modelling involves working with emergent properties, fuzzy categories, apparently illogical causal relations, multiple levels of simultaneous and systemic processes, iterative cycles and unexpected twists and turns. (p. 46)

When clients model the relationships between and across their symbolic perceptions, patterns emerge. These patterns are made manifest as repeating relationships of space, time, form, perceiver and through the emergence of inherent logic. (p. 150)

One of the hallmarks of transformation is the emergence of novel forms. (p. 223)

But naturally, David has his own unique take on emergence. See these articles

Print Friendly, PDF & Email
body * { color: inherit !important; }