What are the underpinning necessities or conditions—the essential ingredients—that lead to and engender the qualities or characteristics of the complex world, especially its processual and emergent nature?
Three conditions for complexity: the essential ingredients
A watch or intricate machine is not complex. Nor is a saucer of water. So, when do we regard something as complex? What are the necessary conditions for complexity fully to be realised?
What are the key ideas that define the science of complexity? How do they help us better understand our world so that we can engage more effectively?
The science of complexity conveys a view of the world as dynamic, richly interdependent and full of variety.
“A world – organic and emergent, shaped by history and context – naturally patterned, yet always in process” (Boulton 2024: 39).
Ilya Prigogine asked why classical physics and evolutionary biology seem to contradict each other. The word that brought these two sciences together and shaped the development of complexity theory, was ‘open’ (Prigogine 1977).
Situations that are open to their environments display emerging order in the form of patterns of relationships.
What are systems archetypes and how can they be used to bring a deeper understanding of causal drivers, potential dynamic behaviour in the future, and policy resistance when tackling complex problems, including those in sustainable development?
Systems archetypes are recurring generic systems structures found in many kinds of organisations, under many circumstances, and at many levels and scales. They are distinctive combinations of reinforcing and balancing processes theoretically rooted in systems thinking and modelling.
There are eight common archetypes, each with specific underlying causal drivers (eg., feedback loops, delay), expected dynamic behaviour (eg., acceleration, disruption, tipping point), and policy implications (eg., how to respond, where to intervene). Archetypes can help shift an analytical focus from simple behavioural correlations or a limited understanding of interactions between certain goals to a generalised knowledge of recurring patterns, causes, and consequences.
How can pattern language be used to share tips for knowledge co-creation in transdisciplinary research? What is pattern language?
Pattern language
Pattern language is an idea that originated in the field of architecture and city planning in the 1970s. The American architect Christopher Alexander and his colleagues created a common language, referred to as pattern language, that can be used by non-experts to participate in the process of city planning and building design.
In this pattern language, the rules of thumb for solving common and timeless problems in design are summarised in units called ‘patterns.’ Each pattern describes a specific problem, the situation or context in which it likely occurs, and the core of the solution to that problem.
The solutions are not written as specific procedures or manuals, but rather as ‘hints’ for solving the problem. Therefore, the solution can be used in many ways based on one’s own needs and situation.
Scatterplots are used in many disciplines, which makes them useful for communicating across disciplines. They are also common in newspapers, online media and elsewhere as a tool to communicate research results to stakeholders, ranging from policy makers to the general public. What makes a good scatterplot? Why do scatterplots work? What do you need to watch out for in using scatterplots to communicate across disciplines and to stakeholders?
In part 1 of our blog posts on why use patterns, we argued for making unstated, tacit knowledge about integrated modelling practices explicit by identifying patterns, which link solutions to specific problems and their context. We emphasised the importance of differentiating the underlying concept of a pattern and a pattern artefact – the specific form in which the pattern is explicitly described.
In order to actually use patterns to communicate about practices, the artefact takes on greater importance: what form could artefacts describing the patterns take, and what mechanisms and platforms are needed to first create, and then share, maintain, and update these artefacts?
While the concepts of ‘problem, solution and context’ should be discussed in some way, there is no single best way of representing patterns as artefacts. The form of artefacts will differ depending on many factors, including how the users perceive the ease of:
How can modellers share the tacit knowledge that accumulates over years of practice?
In this blog post we introduce the concept of patterns and make the case for why patterns are a good candidate for transmitting the ‘know-how’ knowledge about modelling practices. We address the question of how to use patterns in a second blog post.
In broad terms, a pattern links a solution to a problem and its context. As a means of externalizing understanding of practices, the concept has been used productively in various fields, including architecture, computer science, and design science. For a more general introduction to patterns, see Scott Peckham’s blog post. While a “pattern” is ultimately a simple idea, there tends to be disagreement about a precise definition. This poses a problem for this blog post.
What does the word ‘pattern’ mean to you? And how do you use patterns in addressing complex problems?
Patterns are repetitions. These can be in space, such as patterns in textiles and wallpaper, which include houndstooth, herringbone, paisley, plaid, argyle, checkered, striped and polka-dotted.
The pattern concept can also be applied to repetitions in time, as occur in music. Those who know the temporal patterns can classify a piece of music as a blues, waltz or salsa. For each of these types of music, there are also classic dance steps, that usually go by the same name; these are patterns of movement in space and time.
These examples get to the idea that patterns can be viewed more generally as any type of repetitive structure or recurring theme that we can look for and potentially recognize or discover and then assign a memorable name to, such as “houndstooth” or “waltz”.
