To investigate, analyse and understand the nature of change it is useful to begin from a conceptual position. I understand change from two positions which I think are mutually supportive. The first is complexity theory, the second process philosophy. Together they provide an ‘ontological’ foundation of ‘process complexity’ as a basis for interrogating, understanding and designing for change. Here, I outline some of the basic features of these two perspectives, before going on to suggest how they can operate together as an overarching approach to understanding change.
Complexity Theory
Over the past 30 to 40 years complexity science has developed rapidly from its origins in physics, chemistry, biology and cybernetics to offer new insights within the social sciences. The use of a complexity lens within social contexts has started to create a different way of seeing the social world as well as suggesting new approaches to researching it. Defining complexity itself is difficult as there is no single, unified theory. As Cilliers (2010: vii) argues,
‘….there is a growing realisation that there is no single coherent ‘complexity theory’ which will unlock the secrets of the world in any clear and final way. Instead, we are beginning to understand more about exactly why complex things are so difficult to understand. We really have no choice but to acknowledge that we have to take complexity seriously, even if it does not guarantee perfect solutions.’
To demonstrate some of the different perspectives which have grown from a consideration of complexity, some of the contrasting approaches and typologies are given here. Morin (2008) distinguishes between:
Cilliers and Richardson (2001) alternatively classify complexity through three contrasting approaches to study:
Whilst Cilliers (2010) rightly points to the breadth of approaches to complexity, calling into question the idea of a single ‘theory’, Byrne and Callaghan (2014: 8) argue that we should see complexity less as a traditional theory and more as a ‘framework for understanding’, based on the notion that ‘much of the world and most of the social world consists of complex systems…if we want to understand it we have to understand it in those terms.’. They quote Castellani and Hafferty (2009:34) to differentiate a traditional theory from a frame of understanding:
‘social complexity theory is more a conceptual framework than a traditional theory. Traditional theories, particularly scientific ones, try to explain things. They provide concepts and causal connections (particularly when mathematicised) that offer insight into some social phenomenon….. Scientific frameworks, in contrast, are less interested in explanation. They provide researchers effective ways to organise the world; logical structures to arrange their topics of study; scaffolds to assemble the models they construct. When using a scientific framework ‘theoretical explanation’ is something the researcher creates, not the other way round.’
Complexity is explained on an ontological basis starting from a foundation of seeing much of social reality as complex (as opposed to either simple/linear or random).
Taken ontologically, complexity rests on a number of basic concepts.
Non-linearity: non-linear systems are those in which cause and effect relationships are disproportionate, i.e. small causes may have very large impacts and vice versa. There are different forms of non-linearity, including ‘threshold effects’, where a system might act in a linear and predictable way until it hits a certain threshold beyond which it acts in the non-linear manner, commonly referred to as a ‘bifurcation point’, and ‘general deterministic chaos’ where very small variations in initial conditions give very different outputs (the so-called ‘Butterfly effect’). What non-linearity stresses is an inability to accurately model or predict a complex system. Where mathematical modelling is used to explain and characterise such systems, it makes use of ‘qualitative methods’ which generate approximate descriptions as it is not possible to create exact and accurate simulations of complex, non-linear systems. This is not to suggest that quantitative approaches to complexity have no utility, they do. However, they are best used in mixed methods approaches with qualitative approaches.
Emergence: non-linear systems have the potential to create new properties from the interactions of the multitude of elements within them, properties which are not predictable given the known starting-points within the system. Deacon (2007) argues that three ‘levels’ of emergence exist.
Therefore, the basis for understanding emergence is the interplay of many properties over time and across scales which can lead to new, unpredictable states and properties.
Far from equilibrium systems: Systems can exist in a number of states. Those in equilibrium tend to be isolated with no exchange of energy with the environment beyond. In many cases the lack of interaction with the wider environment leads to decay and death. Much more common are systems which are close to equilibrium, called ‘closed’ systems where there is limited interaction with the environment beyond. Here, any move away from equilibrium tends to lead to damping effects to bring it back towards the near equilibrium state. In other words closed systems tend to be driven by negative feedback loops. Finally, there are ‘open’ systems of which most human systems are examples. These can be impacted by negative feedback but may also be impacted by positive feedback which can move the system further away from equilibrium. It is the introduction of elements from beyond the system which keep these systems in a state of flux and disequilibrium. One specific type of open system is the ‘adaptive’ system, commonly referred to in complexity theory as a Complex Adaptive System. Here, change is in part the result of experience with constant exchange of information between the system and the wider environment. Such systems have a number of features, the most important of which are discussed by Cilliers (1998) who identifies them as characterised by:
‘In building representations of open systems, we are forced to leave things out, and since the effects of these omissions are nonlinear, we cannot predict their magnitude.’
Consequently, Richardson et al (2007) refer to CASs as ‘incompressible’, and argue that to understand them, at last in part, we need to use different perspectives to build ever richer, if incomplete, models of the system we are interested in. To some, this might be an excuse not to bother; why research something we cannot understand in its entirety? For others, there is the temptation to use experimental approaches which isolate single variables and assume they operate in the same way in a complex context. But in both cases, the complexity of the system is lost and in many experimental approaches interactive processes are assumed to have little, or no, impact.
Over the past 30 to 40 years complexity science has developed rapidly from its origins in physics, chemistry, biology and cybernetics to offer new insights within the social sciences. The use of a complexity lens within social contexts has started to create a different way of seeing the social world as well as suggesting new approaches to researching it. Defining complexity itself is difficult as there is no single, unified theory. As Cilliers (2010: vii) argues,
‘….there is a growing realisation that there is no single coherent ‘complexity theory’ which will unlock the secrets of the world in any clear and final way. Instead, we are beginning to understand more about exactly why complex things are so difficult to understand. We really have no choice but to acknowledge that we have to take complexity seriously, even if it does not guarantee perfect solutions.’
To demonstrate some of the different perspectives which have grown from a consideration of complexity, some of the contrasting approaches and typologies are given here. Morin (2008) distinguishes between:
- restricted complexity – complexity as an emergent product between simple agents. Such a view allows for rules-based, agentic modelling (i.e. modelling of the parts to give a sense of the whole), which is ultimately reductionist in character, i.e. leads to the idea that by modelling the parts, even though difficult, we can gain an understanding of the whole system;
- general complexity – within this definition the system of interest is not merely the sum of the parts/agents as when they interact they give rise to new properties which are themselves emergent and which resist modelling. Morin suggests that this shows that we are limited in our understanding of complex systems regardless of the approach we use, that we must accept this, and search for a new language in describing and explaining such systems.
Cilliers and Richardson (2001) alternatively classify complexity through three contrasting approaches to study:
- hard complexity – computational modelling and quantitative approaches, generally used within the sciences;
- soft complexity – often employed in the social sciences and often seen as offering a ‘metaphor’;
- complexity thinking – an approach which focuses on the lack of full understanding of systems. This inherently partial perspective is due to multiple factors operating across a number of scales. However, by focusing on emergence (the way systems develop through numerous interactions) as a central process, coherent change can be discerned and studied.
Whilst Cilliers (2010) rightly points to the breadth of approaches to complexity, calling into question the idea of a single ‘theory’, Byrne and Callaghan (2014: 8) argue that we should see complexity less as a traditional theory and more as a ‘framework for understanding’, based on the notion that ‘much of the world and most of the social world consists of complex systems…if we want to understand it we have to understand it in those terms.’. They quote Castellani and Hafferty (2009:34) to differentiate a traditional theory from a frame of understanding:
‘social complexity theory is more a conceptual framework than a traditional theory. Traditional theories, particularly scientific ones, try to explain things. They provide concepts and causal connections (particularly when mathematicised) that offer insight into some social phenomenon….. Scientific frameworks, in contrast, are less interested in explanation. They provide researchers effective ways to organise the world; logical structures to arrange their topics of study; scaffolds to assemble the models they construct. When using a scientific framework ‘theoretical explanation’ is something the researcher creates, not the other way round.’
Complexity is explained on an ontological basis starting from a foundation of seeing much of social reality as complex (as opposed to either simple/linear or random).
Taken ontologically, complexity rests on a number of basic concepts.
Non-linearity: non-linear systems are those in which cause and effect relationships are disproportionate, i.e. small causes may have very large impacts and vice versa. There are different forms of non-linearity, including ‘threshold effects’, where a system might act in a linear and predictable way until it hits a certain threshold beyond which it acts in the non-linear manner, commonly referred to as a ‘bifurcation point’, and ‘general deterministic chaos’ where very small variations in initial conditions give very different outputs (the so-called ‘Butterfly effect’). What non-linearity stresses is an inability to accurately model or predict a complex system. Where mathematical modelling is used to explain and characterise such systems, it makes use of ‘qualitative methods’ which generate approximate descriptions as it is not possible to create exact and accurate simulations of complex, non-linear systems. This is not to suggest that quantitative approaches to complexity have no utility, they do. However, they are best used in mixed methods approaches with qualitative approaches.
Emergence: non-linear systems have the potential to create new properties from the interactions of the multitude of elements within them, properties which are not predictable given the known starting-points within the system. Deacon (2007) argues that three ‘levels’ of emergence exist.
- First order emergence is characterised by an aggregate of elements to give a ‘simple’ higher-order property which occurs through statistically or stochastically determined behaviours. It is the relationships between the lower-order elements/properties which give rise to the higher-order properties, a relationship referred to as ‘supervenience’. This form of emergence is perhaps most closely aligned to Morin’s (2008) definition of restricted complexity.
- Second order emergence introduces the impact of time on the processes involved. First-order emergence may retain self-similarity in the relationships over time. Second-order emergence shows change and development of both ‘micro and macro-properties over time.’ (Deacon, 2007:99). This means that prior states in the system become irreversibly replaced and superseded by new states/system characteristics.
- Third order emergence has an evolutionary character. Here, there can be amplification of global influences on parts which can lead to recursive amplification (positive feedback) or degradation (negative feedback) across all scales of the system. Hence, initial complex states can become amplifiable initial conditions for later states.
Therefore, the basis for understanding emergence is the interplay of many properties over time and across scales which can lead to new, unpredictable states and properties.
Far from equilibrium systems: Systems can exist in a number of states. Those in equilibrium tend to be isolated with no exchange of energy with the environment beyond. In many cases the lack of interaction with the wider environment leads to decay and death. Much more common are systems which are close to equilibrium, called ‘closed’ systems where there is limited interaction with the environment beyond. Here, any move away from equilibrium tends to lead to damping effects to bring it back towards the near equilibrium state. In other words closed systems tend to be driven by negative feedback loops. Finally, there are ‘open’ systems of which most human systems are examples. These can be impacted by negative feedback but may also be impacted by positive feedback which can move the system further away from equilibrium. It is the introduction of elements from beyond the system which keep these systems in a state of flux and disequilibrium. One specific type of open system is the ‘adaptive’ system, commonly referred to in complexity theory as a Complex Adaptive System. Here, change is in part the result of experience with constant exchange of information between the system and the wider environment. Such systems have a number of features, the most important of which are discussed by Cilliers (1998) who identifies them as characterised by:
- a large number of elements with many interactions;
- interactions which are non-linear, i.e. large-scale causes can have small-scale impacts and vice versa;
- interactions which lead to feedback loops, both negative and positive;
- an ‘open’ system, having interactions with elements in external environments beyond the immediate system;
- elements which interact with their environment making the identification of boundaries difficult;
- a system which is far from equilibrium and therefore needs a constant energy flow for it to operate;
- the importance of history, past processes playing a role in forming the present, often unpredictably;
- each element only acting on local information rather than information from the whole system.
‘In building representations of open systems, we are forced to leave things out, and since the effects of these omissions are nonlinear, we cannot predict their magnitude.’
Consequently, Richardson et al (2007) refer to CASs as ‘incompressible’, and argue that to understand them, at last in part, we need to use different perspectives to build ever richer, if incomplete, models of the system we are interested in. To some, this might be an excuse not to bother; why research something we cannot understand in its entirety? For others, there is the temptation to use experimental approaches which isolate single variables and assume they operate in the same way in a complex context. But in both cases, the complexity of the system is lost and in many experimental approaches interactive processes are assumed to have little, or no, impact.
Process Philosophy
Much of the western philosophical tradition takes its starting point form an essentially Aristotelian notion of the fundamental nature of things being that of substance; the material is seen as primary. But a different, if more minor tradition, stems from the work of Heraclitus. His observation that ‘Ever-newer waters flow on those who step into the same rivers.’ Sums up the idea of seeing reality as a series of impermanent fluxes and flows, a series of processes. This leads to the idea that rather than substance being the fundamental nature of things, it is instead processes. This does not discount the importance of substance, but argues that it is processes which bring those substances into being, and hence, processes should be seen as at least as important as substances if not more so. Rescher (2000: 5-6) extends and develops some of these ideas and suggests some basic principles for understanding process.
Time and change are principle elements in metaphysical understanding. Alteration, change and emergence are fundamental aspects of reality. As time unfolds, change occurs and any attempt to construct knowledge and understanding has to take this into account.
Process is a principle category of ontological description. If change occurs through time, then this must be the case due to particular processes acting to give the change. Therefore, any understanding of reality must involve consideration of processes.
Processes are as/more fundamental than things. Rescher (2000: 7) explains the primacy of processes over substance thus,
As process philosophers see it, processes are basic and things derivative, because it takes a mental process (of separation) to extract ‘things’ from the blooming buzzing confusion of the world’s physical processes. Traditional metaphysics sees processes (such as the rod’s snapping under the strain when bent sufficiently) as the manifestation of dispositions (fragility), which must themselves be rooted in the stable properties of things. Process metaphysics involves an inversion of this perspective. It takes the line that the categorical properties of things are simply stable clusters of process-engendered dispositions.’
Contingency, emergence, novelty and creativity are fundamental to metaphysical understanding. Whereas the belief in the primacy of substance can lead to an understanding of reality as being stable and predictable, a focus on process, with its temporal fluxes and flows is suggestive of change and hence contingency. This means that processes can lead to the emergence of new patterns and new configurations of substances leading to novelty and creativity. Therefore, a turn to process is a turn to impermanence and change.
The above does not mean that we should discard interest in substances, as they are obviously of great importance, but we should consider them from the perspective of the processes which are responsible for their creation, relationships with other substances, and to their eventual decay. An example of the difference in emphasis this might bring at a practical level is that from educational research focusing on curriculum materials. If we are interested primarily in the ‘substance’ of this focus, we might want to analyse and understand a resource in its own right, the language used, the format, etc. A process focus, however, will focus on the activities and thinking leading to the creation of the resource, and once created, how it is then used as part of a wider network of processes within the classroom.
Much of the western philosophical tradition takes its starting point form an essentially Aristotelian notion of the fundamental nature of things being that of substance; the material is seen as primary. But a different, if more minor tradition, stems from the work of Heraclitus. His observation that ‘Ever-newer waters flow on those who step into the same rivers.’ Sums up the idea of seeing reality as a series of impermanent fluxes and flows, a series of processes. This leads to the idea that rather than substance being the fundamental nature of things, it is instead processes. This does not discount the importance of substance, but argues that it is processes which bring those substances into being, and hence, processes should be seen as at least as important as substances if not more so. Rescher (2000: 5-6) extends and develops some of these ideas and suggests some basic principles for understanding process.
Time and change are principle elements in metaphysical understanding. Alteration, change and emergence are fundamental aspects of reality. As time unfolds, change occurs and any attempt to construct knowledge and understanding has to take this into account.
Process is a principle category of ontological description. If change occurs through time, then this must be the case due to particular processes acting to give the change. Therefore, any understanding of reality must involve consideration of processes.
Processes are as/more fundamental than things. Rescher (2000: 7) explains the primacy of processes over substance thus,
As process philosophers see it, processes are basic and things derivative, because it takes a mental process (of separation) to extract ‘things’ from the blooming buzzing confusion of the world’s physical processes. Traditional metaphysics sees processes (such as the rod’s snapping under the strain when bent sufficiently) as the manifestation of dispositions (fragility), which must themselves be rooted in the stable properties of things. Process metaphysics involves an inversion of this perspective. It takes the line that the categorical properties of things are simply stable clusters of process-engendered dispositions.’
Contingency, emergence, novelty and creativity are fundamental to metaphysical understanding. Whereas the belief in the primacy of substance can lead to an understanding of reality as being stable and predictable, a focus on process, with its temporal fluxes and flows is suggestive of change and hence contingency. This means that processes can lead to the emergence of new patterns and new configurations of substances leading to novelty and creativity. Therefore, a turn to process is a turn to impermanence and change.
The above does not mean that we should discard interest in substances, as they are obviously of great importance, but we should consider them from the perspective of the processes which are responsible for their creation, relationships with other substances, and to their eventual decay. An example of the difference in emphasis this might bring at a practical level is that from educational research focusing on curriculum materials. If we are interested primarily in the ‘substance’ of this focus, we might want to analyse and understand a resource in its own right, the language used, the format, etc. A process focus, however, will focus on the activities and thinking leading to the creation of the resource, and once created, how it is then used as part of a wider network of processes within the classroom.
A Process Complexity Approach to Change
The very brief outlines of the two perspectives of complexity and process philosophy show a number of overlaps. Both are fundamentally interested in temporal aspects of systems, with change, emergence, and by implication, processes being central to understanding reality. By bringing them together, the focus on processes and the stable, but often transient confluences which lead to nodes of stability and materiality, can be further described and explained through an understanding of the often non-linearity and large number of processes responsible for the observed systems. Bringing these two perspectives together stresses the importance of research focusing on process as well as substance, whilst also offering a level of humility that we can never capture the entirety of the systems we are interested in – our understanding will always offer only a partial view.
The very brief outlines of the two perspectives of complexity and process philosophy show a number of overlaps. Both are fundamentally interested in temporal aspects of systems, with change, emergence, and by implication, processes being central to understanding reality. By bringing them together, the focus on processes and the stable, but often transient confluences which lead to nodes of stability and materiality, can be further described and explained through an understanding of the often non-linearity and large number of processes responsible for the observed systems. Bringing these two perspectives together stresses the importance of research focusing on process as well as substance, whilst also offering a level of humility that we can never capture the entirety of the systems we are interested in – our understanding will always offer only a partial view.
