Tag: metaphors

Attractors and attractor landscapes

When systems that govern behaviour emerge through human interaction (see ‘constraints and emergence‘), this novel structure becomes self-referential and self-preserving; the second-order contextual constraints maintain, streamline, and renew their systems-level organisation (Juarrero, 1999). In other words, the dynamics become embodied in an attractor that defines and enforces behavioural patterns in the system going forward. Attractors are the reason why systems generally resist change – the ones in power want to stay in power, people want to do things in a way they have always done them, new year resolutions get abandoned after a couple of days, etc.

Attractors define specific behavioural patterns that actors in a system adopt. By structuring the system, attractors give the system an element of order. They alter the probability of the behaviour of the actors in the system. Social norms are examples of  attractors; they specify how we are supposed to (and usually do) behave in social situations but also what behaviour we can expect from others, thus reducing uncertainty.

“Attractors thus promote stability in thought and behaviour despite changing conditions and contradictory information.” (Coleman et al., 2011:42)

The behaviour of actors in an economy are defined by a multitude of attractors, building a dynamic landscape of evolving structures. Attractor landscapes constrain a system’s possible future behaviour; they define behaviours with higher and lower probability. This is often called path dependence – what is possible in the future depends on how we got to the present. To be effective in changing a system, we need to understand a system’s disposition (it’s current attractor landscape) and its propensity for change (what change is more likely and what change is less likely to happen) (Quinlan, 2017).

In practice, the concept of attractors can be used in a metaphorical way to describe dynamics in social systems. Attractors describe coherent sets of values and beliefs that encode specific behavioural norms and lead to behavioural patterns. They are formed through common use of stories, metaphors and practice. The participation in a social group that shares a set of common metaphors and practices makes people more likely to adopt certain behaviours and over time it will be difficult for individuals to change the disposition that an attractor creates.

Different types of attractors have different characteristics. So-called single-point attractors are relatively low in complexity and are relatively stable. They are built around one strong, dominant narrative that allows little ambiguity – they can be illustrated as a deep, narrow well in the attractor landscape. An example of a strong, single point attractor is US President George W. Bush’s statement after the 9/11 attacks: “Every nation, in every region, now has a decision to make. Either you are with us, or you are with the terrorists.” (Vonanews, 2009) Single-point attractors are usually easy to recognise but difficult to overcome. Because of their unambiguous nature, change can often only occur radically by completely switching to a competing narrative. More common in human systems are so-called strange attractors that are often formed by the common use of metaphors or myths in a community with a common culture. They give a sense of overall direction and pattern with enough ambiguity to allow diversity and contextualised adaptation – they can be illustrated as relatively wide valley in an attractor landscape, constraining the behaviour by its flanks, but allowing for some diversity on the wide valley floor. These attractors are often difficult to detect but are understood by the people in the system as ‘the way things are done around here’ (Juarrero, 1999).

New attractors emerge when various enabling factors interlock to allow system actors to self-organise into a new set of interrelations and to adopt a new set of behavioural norms. This new behaviour generally entails new capabilities not accessible to the people before. Attractors cannot be purposefully engineered. To enable their emergence, change agents need to stimulate ‘enablers’ to catalyse new attractors. This can be done in the form of a portfolio of safe-to-fail experiments.

The use of attractors in social change has been explored in conflict resolution and peace-building work as described by Coleman and colleagues. For them, “[a]n attractor represents a narrow range of mental states and actions that are experienced by a person or group. These psychological states are mutually congruent in their subjective meaning and thus provide a coherent frame of reference in processing information and deciding how to act towards others” (Coleman et al., 2011:42).

attractor dynamics
Figure 1: Example of attractor dynamics

A simplified example of how changing attractor dynamics can be understood is shown in Figure 1.

Stage 1 in Figure 1 shows an attractor landscape with two attractors. A dominant attractor (with the yellow ball) that shapes the behaviour of most people, and a latent attractor (with the green ball). Taking an example from Local Economic Development, the dominant attractor shapes the view entrepreneurs from a nearby city have about a rural area. They think it is remote and difficult to access and not viable for business. Their opinion is formed based on their current business model, their logistics arrangements, infrastructure, by observing other businesses, etc. All of these elements build the disposition of the current situation, embodied in the attractor. The latent attractor could be formed by a business who bucks the trend. It has designed its business model and arranged its operations in a way that make the rural area a viable place for business.

In stage 2 of Figure 1, there are three dynamics that change the attractor landscape. Firstly, the latent attractor gets stronger, i.e. receives more energy. This could be because the outlier company is successful in their business in the rural area. Secondly, the dominant attractor gets weaker. This could be due to a very competitive situation in the city where businesses that focus there start loosing business. Thirdly, the ridge between the attractors becomes smaller, i.e. the energy that is used to cross it is lower. This could be for example because new infrastructure is built in the rural areas or because the outlier company (which is a first mover) has developed business models others can easily copy.

In stage 3, the yellow ball has vanished and the latent attractor has now become dominant. Views about business in the rural areas are more positive, but the wider valley of the attractor shows that there is also more ambiguity in terms of the behaviour as a response to that view.

The evolution of physical technology is an other example where the metaphor of attractors is useful. The evolution is shaped by successive technological paradigms. These paradigms are embodied in dominant attractors that structure thought. Dosi and Nelson (2010:67) describe technological paradigms as “cognitive frames shared by technological professionals in a field that orient what they think they can do to advance a technology.” Ideas on how to solve technological problems are shaped by the attractor; they are more likely to follow the logic of the current technological paradigm (e.g. using the printing press) than to break with it (e.g. use digital content distribution). The attractor influences both what perspectives are considered (who is asked for ideas) as well as the search heuristics applied. People will, however, inevitably tinker with innovations belonging to new technological paradigms, which creates latent attractors – attractors that are not yet dominant but can be clearly discerned. If in the selection criteria in the evolutionary process shift, a small innovation based on the thinking of a latent attractor can be selected and amplified throughout the system, this can lead to a tipping point and regime shift through which the latent attractor becomes the new dominant attractor and the technological paradigm shifts. This dynamic is often illustrated in subsequent technological S-curves (Foster, 1986).

From an institutional perspective two distinct institutional arrangements can be characterised as examples of distinct system dispositions. On the one hand, there is an institutional regime that features policies that are designed to generate rents and protections that keep the dominant ruling coalition stable. On the other hand, there are institutional regimes that promote open access to political, economic, social and intellectual infrastructure (Shirley, 2008). Development generally seeks to achieve a regime shift from the former to the latter.


COLEMAN, P.T., VALLACHER, R., BARTOLI, A., NOWAK, A. & BUI-WRZOSINSKA, L. 2011. Navigating the landscape of conflict: Applications of dynamical systems theory to addressing protracted conflict. In The Non-Linearity of Peace Processes Theory and Practice of Systemic Conflict Transformation. Körppen, D., Ropers, N. & Giessmann, H.J. (Eds.), Leverkusen, Germany: Barbara Budrich Publishers.

DOSI, G. & NELSON, R.R. 2010. Technical Change and Industrial Dynamics as Evolutionary Processes. In Handbook of the Economics of Innovation. Bronwyn, H.H. & Nathan, R. (Eds.), Amsterdam: North-Holland, pp. 51-127.

FOSTER, R. 1986. Innovation: the Attackers Advantage. New York: Summit Books.

JUARRERO, A. 1999. Dynamics in Action: Intentional Behavior as a Complex System. Cambridge, Massachusetts; London, England: MIT Press.

QUINLAN, T. 2017. SenseMaker contours of narrative – How a culture might evolve, where a culture won’t shift. Narrate Blog. Published 12 October 2017.

SHIRLEY, M.M. 2008. Institutions and Development: Advances in New Institutional Analysis. Cheltenham, UK: Edward Elgar.

VONANEWS. 2009. Bush: ‘You Are Either With Us, Or With the Terrorists’ – 2001-09-21. Published 27 October 2009.


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