A Concise Framework For Thinking in Systems
Understanding the world through emergence
“The… goal of all theory is to make the… basic elements as simple and as few as possible without having to surrender the adequate representation of… experience” – Albert Einstein
Wherever we go, we are surrounded by systems. Systems Theory aims to describe how structures emerge, in which many people are at play to form structures larger than the individual parts. In this, interdisciplinary concepts are analyzed to understand synergy and emergent behavior.
Here’s where this article will lead you:
We are limitless. When we perceive, we do so based on the techniques we have learned as humans: word, thought, perception, and social agreement. At least in these systems, these boundaries are artificial.
Pushing the boundaries. The reason we talk about “pushing the boundaries,” so much is that because the greatest complexities arise at boundaries. It is at boundaries where things can mix up, enabling more self-organization through an augmentation of knowledge, capabilities, and disorder; an augmentation of creativity and transdisciplinarity.
The world is a continuum. Everything causes something else, which eventually comes back.
Throughout the winter I got lost in many books and articles around systems thinking. Systems Theory describes how processes in the world happen - from culture to technology to your personal life. This is a culmination of what I have learned in my research, and some intiital thoughts around the topic of Systems Theory. There are only a few examples explaining the generalities, and to keep things short, most things are represented in graphs. If you want to learn more about physics, systems, culture, and technology, follow me on my substack! :)
Structure of Systems
A Singular System
Generally, a system has three components: elements that make up the system, the purpose or function of the system, and the interconnections that build a bridge between the two. A system could be educational institutions, interpersonal relationships, your favorite soccer team, or just the soup you eat when you are sick.
Despite dividing a system into parts and components, knowing that a system is more than the sum of its parts is essential. Its existence creates dynamic, self-persevering, and emergent behavior that sometimes pre-dominantly shapes the overall structure. The system can sometimes cause its behaviors and loops through these emergent behaviors.
Therefore, we should not consider systems as rigid structures but instead as massive networks that exist because information flows – physical and conceptual information. A system then creates and is created by the information flow.
Example Soccer Team [from Thinking in Systems]
Elements: Players, coach, field, ball.
Interconnections: Rules of the game, the coach’s strategy, the players’ communications, and the laws of physics.
Purpose: To win games, have fun, exercise, make millions of dollars, or all of these.
Examples Interpersonal Relationships
Elements: At least two people, interaction, emotional bonds, (shared) experiences, expectations, and boundaries.
Interconnections: Communication, Learning, Conflict & Resolution, Support Systems,
Purpose: Social Integration, Security and Stability, Emotional Fulfillment, Personal Growth, Synergy & Collaboration
Example Educational Institutions (simplified & ideal)
Elements: Students, Teachers, Administrators, Infrastructure (physical or remote),
Interconnections: Rules in the System, Grades/Evaluations, community, etc.
Purpose: Teaching knowledge and skills, job offerings, etc.
Note that we are touching upon these systems very briefly here. andin a very simplified mannner. Systems are usually more complex than just dividing them into separate components. Usually, systems have subsystems, which have subsystems.
The Complexity of Systems
“The reality we can put into words is never reality itself.” ― Werner Heisenberg
Systems are like the powers of ten, going into macroscopic and microscopic dimensions above or below what we want to analyze. Stopping at identifying a system’s elements, interconnections, and purposes, we cannot reach the more useful aspect of approaching the world and problems through this framework through intuitive, novel, over-arching models.
Every system can usually be described as a set of sub-systems – whether these are seen through the elements, interconnections, or purposes of the system we want to analyze. Unfortunately, it doesn’t stop there because the same applies to the sub-systems.
Imagine you are trying to model the interaction between the human brain and the body. It makes sense to start with the neural pathways and activated muscles, right? But would it make sense to start at the very fundamentals: the quantum mechanics, the organization of atoms in molecules, and the motion. of molecules in your brain cells, and so on.
Depth and Breadth are great, but there is a time when you must stop to fulfill the purpose of your analysis and your own sanity. It is okay to stop at a particular abstraction level. You can always go deeper later.
Systems are complex. Similar to what Heisenberg said, if we claim we can describe a system, it might actually not accurately represent it.
System Dynamics
Now, the exciting part in most of this is how we model the system dynamics – how do systems evolve, how do they change, how do they influence other systems?
Flows
Inflows: information flowing into the system, usually from some origin. This might be food entering your stomach or a new class of students.
Outflows: information flowing out of the system, usually having some sort of destination. This might be the digested food leaving your body or students graduating.
The System Itself
A dynamic system is often called a stock, specifying that we are looking at a system with its history at a specific point in time with all of its elements, dynamics, and such. Information flows into and out of the system, similarly to feedback loops.
Feedback Loops
Usually, systems have feedback loops, constructs that either correct and balance a behavior or reinforce it. Interestingly, systems with similar feedback structures cause similar system dynamics.
Balancing feedback loops: It is based on the current state of the system and its discrepancy to the desired, balanced state. The feedback loop is a mechanism that approaches the desired state.
Reinforcing feedback loops: These are also based on the current state of the system, but instead of balancing towards a desired state, a reinforcing loop is self-enhancing and leads to exponential growth or collapse of a system over time if there is no balance. You can think about this in terms of an ecosystem: if there is only prey but no predators – and no other things that could harm the prey, enough resources, etc. – they would keep growing forever.
Delays: Feedback can often be influenced by delays – delays in information transfer. This often causes complex dynamic behavior because the system can only adjust as quickly as the last piece of information reaches its flow. Feedback only forwards the last bit of information and is, therefore, always one step in the past.
Designing Systems for Us
Apart from the general structure of the system, we also have to optimize for specific sets of characteristics in our systems. Sometimes, we might only be able to understand the characteristics as they emerge from the system. The point of these characteristics is mostly to change and aim the system's interconnections through an adjustment and placing of the feedback loops.
4 Systems Characteristics
The main characteristics described are resilience, self-organization, and hierarchy, but I believe that we should also try to design for anti-fragility.
Resilience also shows in many forms – it might be hidden from all of us and not very intuitive. Resilience is one of the reasons we are alive, e.g., genetic variability lets our populations grow and evolve through diversity and, given enough time, form new, better systems.
Self-organization is the expression of creativity. It is inherent in all of us, and in some form, it can also be described as intelligence – but not the intelligence that we usually describe through IQ tests, school grades, and more but rather as a holistic measurement of a set of things.
Anti-fragility in engineering is the property of systems that increase their capabilities to thrive due to stressors, shocks, volatility, noise, mistakes, faults, attacks, or failures, as Nassim Nicholas Taleb defines. In more philosophical and psychology-based approaches, antifragility describes a system that gets better through suffering. This means that over time, the system itself is self-evolving instead of solely returning to its balanced, desired state. Designing feedback loops that let antifragility thrive is challenging because there might be quite a fine line between resilience and antifragility.
Examples of these characteristics in existing systems
Life is based on the basic self-organizing rules of the chemistry of DNA, RNA, and protein molecules.
The human body can fight off diseases/invasive substances, tolerating various temperatures and food supplies and healing its defective parts. It is resilient.
Universities showcase hierarchies quite well: different departments have mostly closed-off system structures within themselves but contribute to the larger, whole university system & purpose.
Traps we often fall into for these four characteristics
Self-organization is often sacrificed for short-term productivity & stability.
No human body plus intelligence has been resilient enough to keep itself or any other body from eventually dying, so beware of (current-day) limits to resilience.
Hierarchies usually split along their subsystem boundaries. Malfunctioning hierarchies are often not meeting the overall goals. For example, If a team member is more interested in personal glory than in the team winning, he or she can cause the team to lose.
When a subsystem’s goals dominate at the expense of the total system’s goals, the resulting behavior is called suboptimization.
The Archetypes
Throughout our study of systems, we have encountered many different types. Remember that a similar feedback structure within systems causes similar behaviors.
There are more archetypes; find them here. These archetypes include Success to the Successful, Drift to low Performance, Escalation, Shifting the burden to the intervenor (Addiction), Rule Beating, Seeking the Wrong Goal.
Things to be aware of
Everything we think we know about the world is a model.
Usually, they are built on facts and knowledge we have about the world, which is why they have great congruence, but they also often fall short of representing it fully. Our ignorance and arrogance rather than humble observations and understanding can infiltrate them. As much as we would like to, as humans, it is currently impossible to model the world perfectly because our knowledge is limited. We know so little and yet astonishingly much to be able to understand certain complex systems already.
Short-term vs. long-term behavior
We live in an interconnected world full of feedback loops and purposes/functions of systems that we don’t always see – the only way to move towards a more accurate understanding seems to be to take our eyes off short-term behavior and search for long-term behavior and structure.
Events
Events can be spectacular: crashes, assassinations, great victories, terrible tragedies. But they are snapshots and don’t show the dynamic patterns of behavior. The behavior of a system is its performance over time – growth, stagnation, decline, oscillation, randomness, randomness, or evolution. That’s why we should put things into a historical context rather than looking at a snapshot of time – they are the key “to understanding not just what is happening but why.”
It is the structure that determines the latent behaviors of a system, revealing this as a series of events over time. “The devil that I know is better than the devil that I don’t” – a system in which you know the behavior is usually smarter than re-creating an entire system from scratch unless you cannot fix the dysfunctional behavior.
Nonlinear World
I read James Gleick in high school and was surprised to find his quotes fitting in this context
“Nonlinearity means that the act of playing the game has a way of changing the rules… That twisted changeability makes nonlinearity hard to calculate, but it also creates rich kinds of behavior that never occur in linear systems” – James Gleick
“The spot is a self-organizing system, created and regulated by the same nonlinear twists that create the unpredictable turmoil around it. It is stable chaos.” – James Gleick
In comparison to this, linear systems are relationships that are intuitive to us: If I do more, then I get more. They are solvable, they are understandable – they are easy. But they are actually quite hard to find in reality.
“We are not very skilled in understanding the nature of relationships.” [p.91, thinking in systems] That is the hard part in all of this. A non-linear system often leaves you confused – you do something that you think will ease the system, but it actually does the opposite or makes everything worse because nothing we ever do is easy.
Examples of non-linearities in life
Economic systems exhibit non-linear behavior. Small changes in policy, market confidence, or technological innovation can lead to disproportionately large impacts on economic growth, employment rates, and other economic indicators.
Rarely talking about your thoughts may cause people to listen with more excitement when they listen to you, but if you talk frequently, you may attract disgust for yourself. (the more, the less)
The spread of information, trends, or behaviors through social networks often follows a non-linear pattern.
Nonexistent Boundaries
The information inflows and outflows of a system create the boundaries of the system. A system can only go as far as information takes it; the key unit of a system is information. Rarely, the boundaries are completely accurate or real, they are solely drawn based on our limited perception abilities.
When we perceive, we do so based on the techniques we have learned as humans: word, thought, perception, and social agreement. At least in these systems, these boundaries are artificial — we are limitless.
The reason we talk about “pushing the boundaries,” so much is that because the greatest complexities arise at boundaries. It is at boundaries where things can mix up, enabling more self-organization through an augmentation of knowledge, capabilities, and disorder; an augmentation of creativity and transdisciplinarity.
The interesting thing is that almost everything comes from somewhere and goes somewhere – everything keeps moving until it might include the whole planet (not everything does this). However, we have to draw our own boundaries for clarity and sanity, but before analyzing a system, we have to draw the boundaries based on the purpose of our discussion.
Think about it: The way you interact with the environment, leads to manifestation and change in the environment, which leads to an influence on other system, etc. etc. The world is a continuum.
Have mental flexibility when you encounter a new problem – draw the boundary appropriately for your purposes, so you don’t end up too confused within the complexity of your mind.
Limiting Factor
Khalil Gibran writes in The Prophet:
“You have been told that, even like a chain, you are as weak as your weakest link.
This is but half the truth.
You are also as strong as your strongest link.”
Systems have limiting factors – with them they can be strong but without them, they cannot. This is the same for systems: At any given time, the input that is most important to a system is the one that is most limiting.
Ubiquitous Delays
We must learn to wait as we learn to create and as we learn to fix. We are surprised over and over again at how much time things take. Delays are ubiquitous in every system, e.g. the delay in changing the social norms for desirable family size – at least one generation (p.g. 104, thinking in systemes). The longer a delay, the longer it takes for the system to react – a perfect example of this is the current point in politics for tech policy. Technology is moving quickly, but policy is taking longer to follow.
Bounded Rationality
Again, here, you make decisions based on the information that you have – your rationality is bounded. That’s why it is a little bit stupid to talk about I’m more rational then you because everyone’s rationality is bounded at the end of the day. We often misperceive, risk, and assume things when reality looks different. We focus on our current situation more than on the long term.
This leads us to an amazing understanding: within the limits of our information, we can understand the “rational” decisions of others.
“Change comes first from stepping outside the limited information that can be seen from any single place in the system and getting an overview.” – Thinking in Systems
Someone proclaiming that they are open and can see the world while judging others for being unable to have not fully reached an understanding of bounded rationality.
Redesign
When we redesign a system, we should look out for:
Improving information flow
Incentives and disincentives
Goals & stresses
constraints
that affect a specific actor within the system.
Acknowledgments
This Framework is a culmination of and inspired by the resources Thinking in Systems by Donna H. Meadows, The Art of Strategy: A Game Theorist's Guide to Success in Business and Life by Avinash K. Dixit & Barry J. Nalebuff, 21 Lessons for the 21st Century by Noah Yuval Harrari, Physics and Philosophy by Werner Heisenberg, Idea for a Universal History from a Cosmopolitan Point of View by Immanuel Kant, Chaos: Making a New Science by James Gleick, The Systems View of Life: A Unifying Vision
Book by Fritjof Capra and Pier Luigi Luisi, Information Theory, Game Theory, Spiral Dynamics, Social Choice Theory, Bayesian Games, Contract Theory, Maslow's Hierarchy of Needs, personal experiences & thoughts, and more.