By the end of this unit, participants will be able to:

• Recognize systems and their component parts in social innovation.
• Differentiate between simple, complicated and complex problems.
• Apply the lens of complexity theory in real life using systems thinking and systems mapping.

Background

Unit 2 takes students through some fundamental elements of systems theory and systems thinking.

The concepts covered in Unit 2 are interwoven and interdependent and in a sense, also form a system. These concepts are articulated around 2 complementary themes: a) understanding systems theory and b) applying systems theory. These two parts deal with a number of context broken down into:

Part 1: Understanding Systems Theory

• Systems Thinking.
• Complexity Science.
• Systems.
• Exogenous.
• Tighter Coupling/Lower Redundancy.
• Simple, Complicated and Complex Problems.
• Stacey Matrix.
• Nested Systems.

Part 2: Applying Systems Theory

• Understanding Systems using the Iceberg Model.
• Dancing with Systems.
• Systems Mapping.

The optional readings and case studies are resources that can be used to illustrate the fact that there are different types of systems, that systems are ever-changing and that we are almost always part of the systems that we’re trying to change. Those working transforming systems need to define the boundaries and remain aware that there are elements, both inside and outside these boundaries, they cannot control. This can also be an interesting topic of discussion.

Part 1: Understanding Systems

Core Concepts

Systems theory emerges from the interdisciplinary and transdisciplinary study of systems and encompasses a cluster of research programs. It relies on approaches such as systems dynamics and thinking and complexity science, among others. Let’s start by explaining each approach and then how they differ.

Systems

For our purpose, we define a system as any collection of interacting, interrelated or interdependent parts that form a complex and unified whole and that can be described has having a specific purpose. A system is always more than the sum of its parts. There is a difference between a collection of things and a system: a system has a structure and that structure is defined by the relations between its parts which can be of very many different types.

This definition has some interesting implications:

• What it means for a system to perform is defined by its purpose or function.
• Whether a system performs depends on the capacity of its parts to perform: all the necessary parts ought to be there and they themselves ought to perform their function.
• The structure, order or arrangement of a system’s parts impacts the system’s performance.

Systems Thinking

We live in and create systems whose complexity we often ignore in our daily activities. Think about the last time you went to the bank to get some cash, that simple action involved various processes to make that a seemingness process. When we do pay attention to systems, however, we immediately notice that it can be difficult to see through the complexity and even to separate the component parts from the whole. The notion of systems thinking is used in social innovation contexts to pinpoint the target: the systems in which social issues and wicked problems arise. Systems thinking involves breaking down complex systems into smaller parts to better understand how they work together.

Systems thinking is a tool that enables us to sense the systems around us in more nuanced ways, helping us see the world:

in its complexity

at multiple scales

from multiple vantage points

Systems thinking puts us in a position to appreciate the many connections and interdependencies at play.

The boundaries of systems are not fixed and how we see and understand a system can directly affect where we carve its joints.

For example, the city of Hamilton in Ontario is a system. What you understand the parts of that system to be and how they are connected depends on what you take the boundaries of Hamilton as a system to be. From a geographic standpoint, the system in which the city of Hamilton belongs is located in southern Ontario and stretches along the edge of Lake Ontario, both above and below the Niagara escarpment – it includes both urban industrial neighbourhoods and rich agricultural land, as well as beautiful waterfalls and so on. However, from an ecological standpoint, it is equally justified to think of the system in which the City of Hamilton belongs as part of the expansive network of watersheds that accrue to the region of the Great Lakes, which spans an international border and whose waters run out to the Atlantic Ocean slowly but surely.

The City of Hamilton’s connection to broader systems is not merely ecological. Hamilton is connected to other cities, regions and countries and these connections have implications across other kinds of human, political and commercial systems. For the purpose of a land acknowledgement, Hamilton’s geopolitical boundaries become vastly different, extending to the traditional territories of the Haudenosaunee and Anishinaabe peoples.

The wider the boundaries, the more connections and interactions there are. Taking into account the broader systemic considerations may affect decisions that are made by the City, for instance, around sustainability management, politics, agricultural trade and more.

Complexity science is the study of complex adaptive systems. These include, for instance, human bodies, forest ecosystems, hospitals, as well as societies of various scales. Specifically, complexity science explores the patterns of relationships within these systems, how they are sustained, how they self-organize and how they change. Because its objects are varied, complexity science needs to rely on various theoretical frameworks. Complexity science is highly interdisciplinary. In a quest to answer some of the most complex and fundamental questions about living, adaptable, changeable systems – say how life happens and how it evolves – we might require input from biologists, anthropologists, economists, sociologists, management theorists and many other experts.

Complexity science can help reframe the understanding of systems that are only partially understood by traditional, mechanistic scientific insights. Complexity science assumes that some of the conclusions we draw about some systems may help understand others. For instance, systems as diverse as stock markets, human bodies, forest, manufacturing businesses, the microbiome in the human gut, termite colonies and hospitals may share patterns of behaviour. Recognising these similarities may in turn provide insights into sustainability, viability, health and innovation. Leaders and managers in organizations of all types use complexity science to discover new ways of working. —  A Complexity Science Primer: What is Complexity Science and Why Should I Learn About It?

Feedback Loops

One other important principle of complexity theory is that systems maintain stability “through feedback loops”. Feedback and feedback loops are inherent to systems: systems are not linear and the processes involved are not unidirectional. A system’s parts can be understood as subsystems in which a process output may be used again as input, creating a loop that feeds back into the whole system. This means that the impact of a process in a subsystem can influence the next iteration of the process at the level of the system in a continuous manner.

Because processes in a system are subject to feedback loops, in order to change the system intentionally – i.e. in order to innovate socially – one must first understand how the consequences of an action or event may impact the system. Only then is one genuinely in a position to determine how one may adjust to create the intended change.

This brings about the question of whether humans can ‘control’ systems. Rather than aiming for control, it might be more judicious to approach systems work with the idea that there are ways to “work with” systems, or “nudge” them in the places that are the most crucial to make positive change.

Illustrations

Exogenous

The word “exogenous” is used in systems theory to refer to something that has an external cause or origin, something that pertains to external factors. For instance, when the unbearable heat of the desert is an exogenous cause of dehydration of the human body.

Tighter Coupling/Lower Redundancy

According to complexity expert Thomas Homer Dixon, our economies and societies are becoming more complex because performance consistently improves across the micro, meso and macro levels. People, organizations and new information technologies continually create new routines, knowledge flows, beliefs and expectations. According to Dixon, this means that our social, ecological and economic networks have more nodes, denser networks of links and that material, energy and information move faster through these links.

Dixon uses the term ‘Tighter coupling’ to describe this phenomenon. According to him, our societies increasingly rely on these types of connections and interdependencies. However, tighter coupling can also make a system more brittle because it creates conditions in which the following are more likely to happen:

1. Increased risk of cascading failures
2. Lower redundancy of critical system components
3. Difficulty supplying the energy needed to maintain the stability of the social, economic and technological systems experiencing steadily rising stress

At the time, Dixon himself was mostly worried about climate change, but the COVID 19 pandemic has also contributed to highlighting the risks around system brittleness. During the pandemic, we saw lower redundancy all over the supply chain. For instance, because few companies manufactured microchips, production of all kinds of technology-enabled products decreased or ceased temporarily.

When only a few companies produce certain items and the systems break down, there is no immediate solution. What happens as you wait has varying degrees of negative impacts.

For more of Dixon addressing these ideas, see this video.

 

Simple, Complicated and Complex Problems

What kinds of problems require a social innovation approach?

The answer to this question requires a distinction between 3 types of problems: simple, complicated and complex. The difference helps itself suggest there are different approaches to problem-solving that require different types of techniques and skills.

Compare the 3 following tasks:

• Baking a cake
• Send a rocket to the moon
• Raising a child

Baking a cake is a simple problem. No particular expertise is needed, so long as you can follow instructions and the latter are widely available and generally reliable.

Sending a rocket to the moon is a complicated problem. To produce successful results, research, scientific expertise and evidence are needed. Prototyping and testing can extend over years and usually relies on the vast combined experience of many different teams. However, with the right skills and resources it is possible to predict with great accuracy which plan has the most chance of success and proceed with assurance.

Raising a child is complex problem. Not only are there no real instructions, even with all the knowledge and expertise we have of child development today and all the shared wisdom of generations past, there is no way to predict with accuracy how to proceed to produce the best result. One can read up on the best ways to raise children, think back to being a child oneself, or call on people for advice, but ultimately there are few guarantees that everything will go smoothly. In fact, things are very likely to go wrong at times. And even when you have raised one child successfully, there is no guaranty of success the next time around, even if you apply the very same principles. The process is subject to numerous factors outside of your or anyone’s control. The best approach is to do your best, stay adaptive and find a way forward.

In our highly industrialized societies – complicated problems get a lot of attention – expertise is valued, machines do things we can’t, smart people work on producing smart machines and technologies and there is great satisfaction in knowing that we can leverage resources to tackle complicated issues.

However, complex problems cannot be addressed in the same way: while enabling technologies can be helpful along the way, they are insufficient. To address complex problems through social innovation (see Unit 1), we need systems thinking.

The COVID 19 pandemic forced us to address a complex problem and the issues humans faced during the crisis were systemic. Take the disruption in the supply-chain that caused the infamous global shortage of new computers parts back in the early 2020s. It wasn’t just the logistics that broke. People’s decision to buy products or secure a service for themselves or their family had a huge impact on what caused the rupture of stocks. People ordered more product of all kinds online; medical and other equipment was repurposed and directed towards pandemic emergency response; employers/employees applied public health rules with varying degrees of adequacy; etc. Each of these states of affairs impacted the human systems in ways that were unforeseen.

In many ways human behaviours are hard to predict – and the system during the COVID-19 crisis was not ready to accommodate the types of human behaviours we experienced. This unpredictability which is inherent to human and ecological systems in general, means that the work in social innovation is beyond complicated: it is complex. Expertise alone, even when exhaustive, won’t help address complex problems we face. What is needed is the ability to adapt to changes as they happen.

Complex problems can feel overwhelming. But complexity is not always daunting and it certainly hasn’t stopped us: the fact that humans continue to survive is a case in point.

Stacey Matrix

The Stacey Matrix was developed by Ralph Stacey to help articulate the factors that contribute to complexity. Stacey originally designed his matrix to inform governance and project management in organisational settings, especially to mitigate the consequences of disagreement within a team.

The Stacey Matrix also provides some insights into what it means for problems to be simple, complicated, or complex in terms of attitudes of innovation actors toward their interventions.

According to Stacey, the level of complexity of an issue can be understood as a function of the relation between the degree of agreement and the degree of certainty of those involved toward the solution. Hence the matrix is two dimensional.

 

Figure 2.1. The Stacey Matrix.

An approach to addressing a problem is close to certainty when the cause and effect relationships are well known and similar approaches have been used in the past with success. When problems are simple, it is usually possible to apply past solutions and learnings and to predict the outcome of a new solution. Certainty is driven by evidence and, in that sense at least, it can be said to be “objective”. Agreement, by contrast, is a purely social matter: members of a group, team or organization may have differing views on the solutions and how to achieve it.

According to the Stacey Matrix, problems are simple when people are both in agreement and close to certainty. And vice versa: if people are both certain and in agreement, it’s generally because the problem is simple.

Problems are complicated when one of the two dimension is relatively weaker: the solution is relatively certain, but there is less agreement; or people agree on the solution although they have less evidence for it.

Problems are complex when their solutions need to be created in conditions where both agreement and certainly are low, although neither is at its lowest. When an intervention fails to generate both agreement and certainty, what we are dealing with is chaos.

The conditions in which complexity exists are especially hostile to good decision making. This is why complexity is also the space where the skills we associate with e.g., deliberation (critical thinking and analytical skills), problem solving and creativity are most important. These skills also play an important role in addressing complicated issues, but they play a crucial role when addressing complexity. When cause-and-effect relationships are unclear and received knowledge and past experience have less sway, there is increased incentive to rely on collaborative deliberation to reflect and try something new and creative.

Nested Systems

Systems rarely exist in a void. They overlap and can be understood as “nested” in various ways that are relevant when it comes to social innovation. The figure below takes the example of a child to illustrate how systems generally interrelate across four different levels of nesting:

Figure 2.2. Systems Surrounding a Child.

Individual level: physical, psychological, cognitive conditions

Micro level: close, local relationships with family, siblings, peers

Meso level: interactions with and effects of organizations, institutions, e.g.: media, work, school

Macro level: culture, history, worldviews, social and institutional norms and conventions

Systems at all levels of the nesting structure evolve as a result of many factors, including their reciprocal interaction. While there is no objective fact of the matter when it comes to determining the levels of a nesting structure, recognising boundaries between the individual, micro-, meso- and macro-levels is useful when it comes to identifying the junctures at which systems effectively interact or react against each other and thus the points in the system where we need to better understand a relationship or where we can intervene to effect change.