Evolution of Freedom
Part II: TEAM SPORT - Chapter 12
SYSTEMS THINKING
We started out with a simple premise. We wanted to know what the highest quality of life is we can guarantee everybody within the planetary boundaries and how it can be achieved. Since we wanted to keep this a strictly scientific inquiry, we specified that this premise follows the maximin decision rule found in game theory. Inherent in this challenge is our need to solve the systemic problems that now threaten our existence, including war, crime, famine, climate change etc.
To understand the challenge, we turned to evolutionary science, the unifying framework of all science. We learned that despite the clear benefits that selfish behavior confers, forming larger and more complex cooperative entities, sports teams as we call them, can offer a competitive advantage in the struggle for existence. John Maynard Smith called these new cooperative layers major transitions in evolution and they form the foundation of all complex life.
With digitalization, we are currently in the middle of just such a transition, but we don’t yet know what form and scale our cooperation will take. According to David Sloan Wilson, the Omega Point is the logical endpoint to evolution, where humanity forms a new cooperative superorganism that spans the whole globe. This new form of cooperation should solve most of our systemic problems and enable us to maximize human well-being within the planetary boundaries.
Our task in this book is to design the cooperative logic that can accomplish all this. As we are determined to steer away from political ideologies and keep the design process scientific, we continue to follow both evolutionary science and game theory as our main frames of reference. To help us further, in the next chapters we introduce three scientific design methods that we hope will help us meet our challenge: systems thinking, mechanism design and biomimicry. We will use the insights of all of these disciplines in our design process and also invite their practitioners to study, critique and offer further improvements on the proposition we arrive at.
When we set out to design a whole new system of governance, we don’t have to fumble our way in the dark: instead we can turn to an established scientific discipline to illuminate our way–systems thinking. By understanding the fundamental building blocks found in all systems and what makes them tick, our ability to design a well-functioning system is greatly improved. To do this, we turn to Donella H. Meadows and her book Thinking in Systems, which is a required primer for anybody wanting to understand the behavior of systems. This chapter is so deeply indebted to her book that it can be considered a highly condensed version of it.
Systems thinking aims to understand all systems using the exact same set of tools. Systems theory became its own distinct discipline after World War II and developed alongside other related disciplines such as cybernetics and artificial intelligence (AI).
So what exactly is a system?
We are surrounded by systems. They surround us in nature, in society and in our everyday lives. In fact, each and every one of us is a system unto ourselves. Yet, we rarely stop to think about the underlying principles shared by all systems. What, if anything, could possibly be the shared qualities of, say, a rabbit and a toothbrush?
A tree is a system in and of itself but also a subsystem of a larger system called the forest, which in turn is a subsystem of our whole ecosystem. Likewise, an engine is a system, but also a subsystem of a car, which in turn is a subsystem of our transportation infrastructure. A family is a system with a subsystem called a person with a subsystem called the heart, and so on.
According to its own definition, a system consists of:
To understand the challenge, we turned to evolutionary science, the unifying framework of all science. We learned that despite the clear benefits that selfish behavior confers, forming larger and more complex cooperative entities, sports teams as we call them, can offer a competitive advantage in the struggle for existence. John Maynard Smith called these new cooperative layers major transitions in evolution and they form the foundation of all complex life.
With digitalization, we are currently in the middle of just such a transition, but we don’t yet know what form and scale our cooperation will take. According to David Sloan Wilson, the Omega Point is the logical endpoint to evolution, where humanity forms a new cooperative superorganism that spans the whole globe. This new form of cooperation should solve most of our systemic problems and enable us to maximize human well-being within the planetary boundaries.
Our task in this book is to design the cooperative logic that can accomplish all this. As we are determined to steer away from political ideologies and keep the design process scientific, we continue to follow both evolutionary science and game theory as our main frames of reference. To help us further, in the next chapters we introduce three scientific design methods that we hope will help us meet our challenge: systems thinking, mechanism design and biomimicry. We will use the insights of all of these disciplines in our design process and also invite their practitioners to study, critique and offer further improvements on the proposition we arrive at.
When we set out to design a whole new system of governance, we don’t have to fumble our way in the dark: instead we can turn to an established scientific discipline to illuminate our way–systems thinking. By understanding the fundamental building blocks found in all systems and what makes them tick, our ability to design a well-functioning system is greatly improved. To do this, we turn to Donella H. Meadows and her book Thinking in Systems, which is a required primer for anybody wanting to understand the behavior of systems. This chapter is so deeply indebted to her book that it can be considered a highly condensed version of it.
Systems thinking aims to understand all systems using the exact same set of tools. Systems theory became its own distinct discipline after World War II and developed alongside other related disciplines such as cybernetics and artificial intelligence (AI).
So what exactly is a system?
We are surrounded by systems. They surround us in nature, in society and in our everyday lives. In fact, each and every one of us is a system unto ourselves. Yet, we rarely stop to think about the underlying principles shared by all systems. What, if anything, could possibly be the shared qualities of, say, a rabbit and a toothbrush?
A tree is a system in and of itself but also a subsystem of a larger system called the forest, which in turn is a subsystem of our whole ecosystem. Likewise, an engine is a system, but also a subsystem of a car, which in turn is a subsystem of our transportation infrastructure. A family is a system with a subsystem called a person with a subsystem called the heart, and so on.
According to its own definition, a system consists of:
1) parts,
2) connections between those parts, and
3) a shared purpose or function.
2) connections between those parts, and
3) a shared purpose or function.
If we examine our society, which is the system we hope to transform, we see that the parts consist of all the physical elements, the people, the buildings, the infrastructure etc., but also many of the non-physical elements, such as the organizations and institutions that shape our society.
The connections between these parts are the human and professional relationships, laws and customs, trade and financial relationships, communication and transportation infrastructure, and culture and heritage, to name some of the most essential connections.
The shared purpose or function of a system can sometimes be difficult to spot, but in the case of our society it is presumably something like self-preservation and the attempt to grow and multiply. The actual outcome of a system can be seen as its purpose even when it differs from the stated purpose. From the perspective of systems thinking, an undesirable outcome such as unemployment is therefore not a bug but a feature of our economic system. In our case, the stated purpose of the system we are designing is the maximization of human well-being within planetary boundaries.
Systems thinking observes a system’s behavior over a longer period of time and not as single events. This way we can observe if a system manages to fulfill its purpose or if it encounters some other boundary. This also helps us to understand the structure of the system. As we’ve observed the outcomes of our political and economic systems over centuries, we have found them lacking.
For us to be able to study systems in a meaningful way, we have to draw boundaries. For the sake of simplicity, we can, for example, study only the heart even when the person carrying the heart will have a huge impact on how that heart functions. The maps drawn by systems thinking are always simplifications as are the boundaries, which should always be temporary. Since system maps are always simplifications, we shouldn’t mistake them for reality, which can be much more complex than our diagram.
Systems are generally made out of different stocks or resources that can either be renewable or non-renewable. Any part of a system can be viewed as a stock. Inflows increase the reserves whereas outflows deplete them. If we view a bathtub as a system and the water in it as the stock we want to study, we can conclude that the stock is renewable and that inflows are created by running the tap and outflows by opening the drain. Humans are a renewable stock in our society, with births creating inflows and deaths outflows. In our case, well-being is the stock we want to maximize, but it is dependent on many other stocks, such as money and energy/matter.
If a system keeps working the same way over a longer period, it contains functioning feedback loops, which keep it stable. A thermostat is an example of a feedback loop. It turns the heating on when the temperature drops below a certain point and off again when a desired temperature has been reached. The thermostat controls the heater and ensures that the temperature in the house remains stable.
If there’s one idea everybody should take from this chapter it is the idea of the feedback loop. As we design a system, it is the design of these feedback loops that will require most of our attention. In a car we know what causes the inflows and outflows of the gas tank. The fuel gauge is a feedback loop that tells us when the gas tank needs to be filled. Flows and feedback loops are connections between the various parts of the system. Systems thinkers see the world as a complex web of feedback loops. When we have had enough to eat, our stomach sends a signal that it is full so we know to stop eating. Hunger, on the other hand, is the opposite signal, telling us that we need to eat. Such feedback loops are called balancing feedback loops.
The mere existence of feedback loops doesn’t necessarily guarantee that a desired state is reached. The heating system can be underpowered, meaning the house will be cold during parts of the year. Even if we listen to the signals our stomach sends us, we can eat too much and over time gain weight. In such cases the feedback loop might be insufficient or there is a failure to act according to the signal.
In addition to balancing feedback loops there are also reinforcing feedback loops. These cause either vicious or virtuous cycles. Inflation is a good example of a reinforcing feedback loop. The more prices rise, the more wages need to rise. The more wages rise, the more prices need to rise. The value of money decreases and a vicious cycle is born.
Money deposited in a bank, on the other hand, is an example of a positive reinforcing feedback loop. When interest is paid on top of interest, the deposit doesn’t grow linearly any more, it grows exponentially. Reinforcing feedback loops are behind population growths.
Systems often behave counterintuitively. Their web of feedback loops can be so complex that an attempt to fix a problem can often lead to even worse results. This is what characterizes humans’ struggle with nature, the most complex system of them all. Misunderstanding how nature works and what inputs lead to which outputs have caused havoc since the dawn of time.
Complex systems such as our society have numerous reinforcing and balancing feedback loops, which compete with each other. Many of the feedback loops also contain delays. A delay can occur in making the relevant finding, in the reaction to the finding or in the delivery itself. A classic example of such a delay is the hot water you have to wait for in the shower. When this happens, we usually turn the faucet all the way up, and when the hot water finally arrives, it is scalding. We then react by turning it all the way down and the water becomes freezing again. This is called oscillation within the system, which is caused by a delay in the opposite feedback loops. Our reactions can amplify the oscillations even if our aim is to balance the system.
A shower is a very simple system compared to, say, a nation’s economy, and its dizzying array of feedback loops and their various delays. Needless to say, when we build our system, delays should be eliminated as a matter of principle.
When a well-functioning system, such as an animal or an ecosystem, confronts a problem, it has the capacity to heal and return to its original state but also to adapt to changing circumstances. This means they have a capacity for self-organization, which means an ability to change one’s behavior and structure.
Successful systems have an ability to create hierarchies, subsystems and metasystems, which are assigned with specific tasks. A city’s organization is usually divided into specific departments, so that the whole organization isn’t held responsible if the streets aren’t cleaned on time. The human body assigns the task of circulation to a specific organ called the heart.
Systems have a tendency to repeat certain problem behaviors when their goals aren’t met or when their communication systems, reward systems or feedback loops fail. Meadows identifies numerous system traps, which all have their own prescribed cures. Policy resistance, tragedy of the commons, drift to low performance, escalation, competitive exclusion, addiction, rule beating and seeking the wrong goal are all names for problem behaviors regularly identified in various human systems.
When we examine systems carefully, we learn how the systems themselves create their own behavior. All of a sudden, a bad outcome doesn’t have an easily defined cause or a scapegoat any more. The blame lies “in the system.” The fact that systems behave in surprising ways is partly due to our tendency to think linearly, through stories. Linear thinking is unhelpful in trying to understand systems, which are non-linear in nature. If you try to accurately describe what happens in a car’s engine when you step on the gas, you’ll notice that you are forced to put events that happen simultaneously into a sequence of events. This is why systems thinking prefers to use maps and charts instead of words to express the underlying structure of the systems it studies.
In her book, Meadows goes into great detail in describing how systems can be changed and which changes yield the biggest results. In changing a system changing its parts doesn’t usually have a big effect. Even if you swapped every football player on the field, the game would still be football. This is also obvious in politics. We change our politicians regularly, but the game stays the same.
By changing the connections or feedback loops between the parts, we can change the behavior of the parts quite significantly. Changing the rules of football to those of basketball would yield a dramatic change.
One of the biggest changes we can make to a system is to change its purpose. Knowing the system’s purpose will guide us in how the connections should be drawn. This is why having a clear purpose is so important to any functioning system.
In our case, the purpose of the system we intend to build is to maximize human well-being for everybody in perpetuity within the carrying capacity of Earth. Since we have articulated a new purpose, but most of the parts that make up the system remain exactly the same, the main thing we can change to achieve our desired outcome is the connections between the pre-existing parts.
This can be done by changing our laws and financial incentives and the way we communicate and cooperate. But we also need to build whole new institutions, new parts that replace the institutions that have become obsolete. In the next chapter we’ll immerse ourselves into a discipline called mechanism design, which is solely dedicated to solving three-sided equations. By knowing the starting point (parts) and the end point (purpose), mechanism design strives to create such a mechanism (connections) that produces the predetermined outcome and solves the three-sided equation.
The connections between these parts are the human and professional relationships, laws and customs, trade and financial relationships, communication and transportation infrastructure, and culture and heritage, to name some of the most essential connections.
The shared purpose or function of a system can sometimes be difficult to spot, but in the case of our society it is presumably something like self-preservation and the attempt to grow and multiply. The actual outcome of a system can be seen as its purpose even when it differs from the stated purpose. From the perspective of systems thinking, an undesirable outcome such as unemployment is therefore not a bug but a feature of our economic system. In our case, the stated purpose of the system we are designing is the maximization of human well-being within planetary boundaries.
Systems thinking observes a system’s behavior over a longer period of time and not as single events. This way we can observe if a system manages to fulfill its purpose or if it encounters some other boundary. This also helps us to understand the structure of the system. As we’ve observed the outcomes of our political and economic systems over centuries, we have found them lacking.
For us to be able to study systems in a meaningful way, we have to draw boundaries. For the sake of simplicity, we can, for example, study only the heart even when the person carrying the heart will have a huge impact on how that heart functions. The maps drawn by systems thinking are always simplifications as are the boundaries, which should always be temporary. Since system maps are always simplifications, we shouldn’t mistake them for reality, which can be much more complex than our diagram.
Systems are generally made out of different stocks or resources that can either be renewable or non-renewable. Any part of a system can be viewed as a stock. Inflows increase the reserves whereas outflows deplete them. If we view a bathtub as a system and the water in it as the stock we want to study, we can conclude that the stock is renewable and that inflows are created by running the tap and outflows by opening the drain. Humans are a renewable stock in our society, with births creating inflows and deaths outflows. In our case, well-being is the stock we want to maximize, but it is dependent on many other stocks, such as money and energy/matter.
If a system keeps working the same way over a longer period, it contains functioning feedback loops, which keep it stable. A thermostat is an example of a feedback loop. It turns the heating on when the temperature drops below a certain point and off again when a desired temperature has been reached. The thermostat controls the heater and ensures that the temperature in the house remains stable.
If there’s one idea everybody should take from this chapter it is the idea of the feedback loop. As we design a system, it is the design of these feedback loops that will require most of our attention. In a car we know what causes the inflows and outflows of the gas tank. The fuel gauge is a feedback loop that tells us when the gas tank needs to be filled. Flows and feedback loops are connections between the various parts of the system. Systems thinkers see the world as a complex web of feedback loops. When we have had enough to eat, our stomach sends a signal that it is full so we know to stop eating. Hunger, on the other hand, is the opposite signal, telling us that we need to eat. Such feedback loops are called balancing feedback loops.
The mere existence of feedback loops doesn’t necessarily guarantee that a desired state is reached. The heating system can be underpowered, meaning the house will be cold during parts of the year. Even if we listen to the signals our stomach sends us, we can eat too much and over time gain weight. In such cases the feedback loop might be insufficient or there is a failure to act according to the signal.
In addition to balancing feedback loops there are also reinforcing feedback loops. These cause either vicious or virtuous cycles. Inflation is a good example of a reinforcing feedback loop. The more prices rise, the more wages need to rise. The more wages rise, the more prices need to rise. The value of money decreases and a vicious cycle is born.
Money deposited in a bank, on the other hand, is an example of a positive reinforcing feedback loop. When interest is paid on top of interest, the deposit doesn’t grow linearly any more, it grows exponentially. Reinforcing feedback loops are behind population growths.
Systems often behave counterintuitively. Their web of feedback loops can be so complex that an attempt to fix a problem can often lead to even worse results. This is what characterizes humans’ struggle with nature, the most complex system of them all. Misunderstanding how nature works and what inputs lead to which outputs have caused havoc since the dawn of time.
Complex systems such as our society have numerous reinforcing and balancing feedback loops, which compete with each other. Many of the feedback loops also contain delays. A delay can occur in making the relevant finding, in the reaction to the finding or in the delivery itself. A classic example of such a delay is the hot water you have to wait for in the shower. When this happens, we usually turn the faucet all the way up, and when the hot water finally arrives, it is scalding. We then react by turning it all the way down and the water becomes freezing again. This is called oscillation within the system, which is caused by a delay in the opposite feedback loops. Our reactions can amplify the oscillations even if our aim is to balance the system.
A shower is a very simple system compared to, say, a nation’s economy, and its dizzying array of feedback loops and their various delays. Needless to say, when we build our system, delays should be eliminated as a matter of principle.
When a well-functioning system, such as an animal or an ecosystem, confronts a problem, it has the capacity to heal and return to its original state but also to adapt to changing circumstances. This means they have a capacity for self-organization, which means an ability to change one’s behavior and structure.
Successful systems have an ability to create hierarchies, subsystems and metasystems, which are assigned with specific tasks. A city’s organization is usually divided into specific departments, so that the whole organization isn’t held responsible if the streets aren’t cleaned on time. The human body assigns the task of circulation to a specific organ called the heart.
Systems have a tendency to repeat certain problem behaviors when their goals aren’t met or when their communication systems, reward systems or feedback loops fail. Meadows identifies numerous system traps, which all have their own prescribed cures. Policy resistance, tragedy of the commons, drift to low performance, escalation, competitive exclusion, addiction, rule beating and seeking the wrong goal are all names for problem behaviors regularly identified in various human systems.
When we examine systems carefully, we learn how the systems themselves create their own behavior. All of a sudden, a bad outcome doesn’t have an easily defined cause or a scapegoat any more. The blame lies “in the system.” The fact that systems behave in surprising ways is partly due to our tendency to think linearly, through stories. Linear thinking is unhelpful in trying to understand systems, which are non-linear in nature. If you try to accurately describe what happens in a car’s engine when you step on the gas, you’ll notice that you are forced to put events that happen simultaneously into a sequence of events. This is why systems thinking prefers to use maps and charts instead of words to express the underlying structure of the systems it studies.
In her book, Meadows goes into great detail in describing how systems can be changed and which changes yield the biggest results. In changing a system changing its parts doesn’t usually have a big effect. Even if you swapped every football player on the field, the game would still be football. This is also obvious in politics. We change our politicians regularly, but the game stays the same.
By changing the connections or feedback loops between the parts, we can change the behavior of the parts quite significantly. Changing the rules of football to those of basketball would yield a dramatic change.
One of the biggest changes we can make to a system is to change its purpose. Knowing the system’s purpose will guide us in how the connections should be drawn. This is why having a clear purpose is so important to any functioning system.
In our case, the purpose of the system we intend to build is to maximize human well-being for everybody in perpetuity within the carrying capacity of Earth. Since we have articulated a new purpose, but most of the parts that make up the system remain exactly the same, the main thing we can change to achieve our desired outcome is the connections between the pre-existing parts.
This can be done by changing our laws and financial incentives and the way we communicate and cooperate. But we also need to build whole new institutions, new parts that replace the institutions that have become obsolete. In the next chapter we’ll immerse ourselves into a discipline called mechanism design, which is solely dedicated to solving three-sided equations. By knowing the starting point (parts) and the end point (purpose), mechanism design strives to create such a mechanism (connections) that produces the predetermined outcome and solves the three-sided equation.
