Month: December 2017

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.

Resources

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.

 

Directed and emergent order

A system can be defined as a set of interconnected elements that form a coherent whole with a distinct pattern of behaviour. These elements or agents can be as diverse as animals, cells, humans, organisations or businesses. In contrast to an aggregate, in a system the properties of the elements depend on the systemic context within which they are located. In other words, the system consists of the elements and, in turn, the elements are influenced by the systemic whole (Juarrero, 1999). For example, as part of a community people shape the way things work in the community but their individual behaviours are in turn shaped by the rules and norms of the community they create. This phenomenon is called emergence.

Kurtz and Snowden (2003) describe in their paper two different types of order in natural systems: ‘directed order’ and ‘emergent order’.

A machine: an example of a complicated system with directed order.

Directed order describes a system where “the relationship between an action and its consequences is knowable by bringing in relevant expertise” (Hummelbrunner & Jones, 2013:2). In this space, solutions can be designed as it is clear what the problem is and an agreement can be found on how it can be fixed. These systems can be highly intricate and analysis difficult, which is when they are called complicated. In complicated contexts, the system can be taken apart, defective individual elements can be fixed or optimised and then the system can be put back together. This can be seen for example when a car engine is fixed or when parts of a solar power generation plant are optimised. This works because the functionality of the system is given by the sum of the functionality of the parts. Taking the system apart and fixing or optimising parts individually leads to improved performance of the overall system. If one part fails, these systems often malfunction completely.

A market: an example of a complex system with emergent order.

Emergent order is different. In these systems “there is a fascinating kind of order in which no director or designer is in control but which emerges through the interaction of many entities” (Kurtz & Snowden, 2003:464). Emergent order gives the system abilities that individual components do not have. Most abilities that we attribute to complex systems are emergent properties, such as consciousness emerging from a system of individually unconscious neurons; intricate patterns in the murmuring of hundreds or thousands of starlings emerging from individuals that follow simple rules and only receive signals from their immediate neighbours; a set of rules and norms emerging from a community of individuals living in close proximity; and so on.

Emergence is a process of the elements self-organising into a qualitatively novel state of interrelation, and hence a higher-level order. Emergence occurs when previously uncorrelated elements or processes in the system suddenly become coordinated and interconnected (Juarrero, 1999). An example of this process is the emergence of impersonal exchange in economies. Interrelations between individual market actors over time lead to the establishment of institutions that allow for impersonal exchange. Yet societies have not simply decided to design these institutions and put them in place from one day to the next – rather, they have evolved over time.

Under emergent order, causality is not predictable because the structure of these systems is not fixed but continuously created by the interactions of the actors. The structure changes with the behaviour of the actors in the system. The behavioural choices in turn depend on the structure. This feedback loop creates continuous, dynamic adaptation. Interventions change the system in a way so a repeated intervention will lead to a different result. Hence an understanding of the causal relations for each change can only be gained in hindsight and not through foresight. Snowden (2011) therefore describes emergent order as being only retrospectively coherent. In other words, the causality between an intervention and its effect can only be assessed once it has been implemented. In such systems, analysis and intervention have to merge into a process of continuous trial, learning and adaptation.

Typically in these situations, “there is not only considerable disagreement about the nature of the situation and what needs to be done, but also about what is happening and why. The relationship between an action and its consequences is unknowable beforehand, depending considerably on context” (Hummelbrunner & Jones, 2013:2). These systems are called complex systems or complex adaptive systems.

The current overall functionality of the system has emerged because of the way the components currently function or behave, whether they are perceived as working correctly or being broken. Complex systems often continue to work when one component fails as each part continuously adapts to the functioning of the other parts to preserve the overall functionality of the system. Optimising individual parts will have unintended and unpredictable effects on the functioning of the overall system.

The description of complexity and complex systems builds the basis of the understanding of the economy as presented in complexity and evolutionary economics. Social technologies and effective institutions emerge without a central director or designer and provide an emergent order for human interaction. Effective institutions are the reason humans can achieve capabilities that are not accessible to the individual. For example, institutions are needed to coordinate specialised knowledge in an industry. The institutional landscape co-evolves together and the institutions are consequently strongly interrelated. Optimising them in isolation will have unintended and unpredictable effects on the overall system.

References

HUMMELBRUNNER, R. & JONES, H. 2013. A Guide to Managing in the Face of Complexity. ODI Working Paper. London: Overseas Development Institute.

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

KURTZ, C.F. & SNOWDEN, D.J. 2003. The new dynamics of strategy: Sense-making in a complex and complicated world. IBM Systems Journal, 423 462-483.

SNOWDEN, D.J. 2011. Good fences make good neighbors. Information Knowledge Systems Management, 101-4 135-150.

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