Systems Thinking: Embracing the Whole

Introduction to Systems Thinking

There are two ways of looking at the world: in parts or as a whole. Most of us have been trained to think in parts. This approach involves taking something complex, breaking it down into individual components, and studying each one to understand the more complex world. Our educational systems are structured this way, with subjects like English, math, biology, chemistry, and physics examined separately to comprehend the world around us.

"Our schools are set up this way."

However, as Aristotle famously said, "the whole is more than the sum of its parts." It's usually in the connections between different subjects and their interrelationships that we truly understand how something works.

"The whole is more than the sum of its parts."

This is where systems thinking comes into play. By using six mental models, we can better grasp how these connections and relationships form a complete picture.

Linear vs Nonlinear Organization

In our journey to explore systems thinking, we start with understanding the concept of linear versus nonlinear organization. As Vicky introduces, linear thinking is something most of us are familiar with. It's the approach where we break things down into a sequence of events: "if a happens, then b happens, and then c happens." This is akin to how Excel spreadsheets function, where everything proceeds in a step-by-step manner.

"In linear thinking, it's reducing things down to if then."

However, life is far from linear. The world is dynamic and interconnected, where events influence each other in complex ways. This leads us to nonlinear thinking. Unlike the straightforward sequence of linear models, nonlinear thinking recognizes that "a" might influence "b," which in turn affects "c," and then loops back to impact "a." This cyclical pattern reflects the complexity and interdependence of real-world systems.

"The world is very dynamic." "A nonlinear way of thinking could be that a feeds into b, which feeds into c, which then feeds into a."

Understanding these differences enhances our ability to see problems holistically and identify root causes rather than merely addressing symptoms.

The Biggest Little Farm: A Case Study

"The Biggest Little Farm" is a captivating documentary available on Netflix that beautifully illustrates the concept of nonlinear systems. The film follows Molly and John, a couple who, along with their dog, transition from city life in Los Angeles to a farm that had once been dedicated to exclusive berry farming.

Initially, the farm had suffered from nutrient depletion due to the monoculture of berries. Molly and John embarked on a mission to revive the land by introducing biodiversity, aiming for sustainable and organic farming practices that would support the local community.

To rejuvenate the soil, they planted cover crops like citrus trees. However, this effort attracted snails that infested their garden. Additionally, they raised farm animals such as cows and sheep to enhance soil fertility with manure. Yet, this led to an unexpected fly population boom.

Their pond, home to ducks and fish, thrived until a drought caused duck droppings to create toxic algae that killed the fish.

Through these challenges, Molly and John discovered that each element of their farm wasn't an isolated problem but part of an interconnected system. Recognizing these interconnections allowed them to find solutions more efficiently.

For example, chickens intended for egg production also consumed maggots in manure, reducing the fly population. Ducks that caused algae issues in the pond were found to consume snails in the cover crops—turning a pest problem into a nutrient solution for plants.

This documentary illustrates how recognizing interconnections within systems can transform seemingly insurmountable challenges into manageable solutions. It emphasizes an understanding of how "all these parts weren't working separately." Instead of isolated problems, they were part of an interdependent system working together.

Stock and Flow: Simplifying Systems

Understanding and managing complex systems can often seem daunting, but breaking them down into simpler components can make this task more approachable. The stock and flow model is a powerful mental tool for achieving this simplification, focusing on just two parts: stock and flow.

Introduction to Stock and Flow Model

The stock and flow model helps us comprehend systems by visualizing how different elements interact dynamically. Stock refers to the elements within the system that can be added or subtracted. These are the tangible or intangible components that make up the system's structure. For instance, in a farming system, the stock includes animals, humans, plants, soil, water, and even financial resources like money. All these components are essential parts of the farm's ecosystem.

Flow represents the actions or processes that alter the quantity of stock. It is essentially about how these changes occur over time within the system. For example, selling farm produce like lemons is a flow action. When lemons are sold, their quantity decreases while the monetary resources increase. This dynamic illustrates how actions within systems can lead to changes in stocks.

Explanation of Stock and Flow in the Farm Example

Let's delve deeper into this concept using a farm as our example. The farm's stocks are its resources—everything from animals to water—each capable of being increased or decreased based on various actions. Flows are those actions such as selling produce or purchasing supplies that directly impact these stocks.

By recognizing these flows, we gain insight into how different parts of a system interact and influence each other over time. This perspective helps us identify not just what changes occur but also how they happen.

Benefits of Using Stock and Flow to Understand Systems

Employing the stock and flow model allows us to map out complex processes more intuitively by visualizing them as interconnected elements rather than linear steps. This approach aids in uncovering hidden connections between different components of a system.

"This is understanding systems in just two parts, stock and flow.",[object Object], ,"Selling here is the flow." "The stock are the things and the flow connects the things together."

By simplifying our understanding with this model, we can better identify opportunities for optimizing processes within systems—whether they be farms or other complex networks—ultimately enhancing their organic resilience.

The Iceberg Model: Uncovering Hidden Realities

In the world of systems thinking, the iceberg model emerges as a pivotal tool, helping us uncover layers of reality that are often hidden beneath the surface. The model identifies four levels of realities that every systems thinker should be aware of: events, patterns of behavior, systems, and mental models.

"The iceberg model identifies the four levels of realities."

Most people only notice the first level—events. In a farm scenario, for instance, the event might be as apparent as noticing snails on a citrus tree and deciding to remove them. This approach merely addresses symptoms rather than underlying causes, and such problems tend to recur.

To delve deeper, one must consider patterns of behavior—the second level in the iceberg model—which introduces the element of time. By observing over extended periods, we can identify recurring patterns. On the farm, Molly and John consistently faced new problems every time they introduced a solution. This indicates a deeper structural issue.

The third level focuses on systems—the structures that create and connect these patterns. In Molly and John's case, biodiversity was at play. Their aim to integrate biodiversity into sustainable organic farming led to new challenges as new species introduced new dynamics within the ecosystem.

Finally, we reach mental models—the fourth level—which are often deeply ingrained beliefs or assumptions that dictate how we perceive structures and events around us.

By understanding these layers, we can develop a more comprehensive view of complex systems and apply more effective solutions rather than simply treating symptoms.

Finding the Bottleneck: Strengthening Systems

Identifying bottlenecks in any system is crucial for strengthening and optimizing its effectiveness. As the saying goes, "The system is only as strong as its weakest part." In the context of farming, this principle becomes even more critical. Farmers must pinpoint where delays and inefficiencies occur to enhance their operations.

"You want to find the bottleneck."

A practical example can be seen in sustainable farming practices where mental models are applied to identify and resolve bottlenecks. The focus is on aligning with nature rather than trying to control it. On a farm, biodiversity becomes a tool for creating sustainable organic farming practices that promote harmony with nature.

For instance, instead of manually controlling snail populations, farmers can use ducks that naturally feed on snails. This approach not only manages pests but also fertilizes the soil, showcasing a synergy with natural processes.

In finding bottlenecks on a farm, several challenges may arise:

• Pest Infestations: A significant bottleneck where 70% of crops are affected by pests.

• Resource Limitations: Insufficient chickens to meet egg demand or lack of fertile soil for more crops.

• Knowledge Gaps: A shortage of expertise in biodiversity and farming techniques.

Among these issues, the most critical bottleneck is pest control. This problem requires prioritization using mental models such as the 80/20 rule to focus on areas that yield the highest impact when resolved.

Second Order Thinking: Beyond the Obvious

In our journey through systems thinking, we find ourselves at the doorstep of a crucial concept: second order thinking. This mental model pushes us beyond the immediate and obvious, encouraging us to explore the ripple effects of our actions. Most of us are accustomed to first order thinking, where we see a direct cause and effect. For example, "If I do this, then this happens." This is linear thinking, which is familiar but often insufficient for complex systems.

Second order thinking delves deeper. It's about understanding and predicting the implications of our first order actions. To illustrate this, let's revisit the farm example. In first order thinking, if the soil is fallow with no nutrients, planting cover crops seems a straightforward solution to rejuvenate it with nutrients and water.

However, second order thinking asks us to consider what happens next. By planting cover crops to enrich soil nutrients and water retention, we change the environment. This change might attract bugs and critters to a now healthier soil ecosystem. Additionally, it might lead to issues such as snails affecting citrus trees if they are nearby.

"Second order thinking is about finding the implications to the first order actions."

This advanced layer of thought doesn't come automatically or intuitively for most people. It requires knowledge about biodiversity and ecosystems that many might not possess initially. Without it, one might stop at first order conclusions and face unforeseen consequences later.

By engaging in second order thinking, you can identify what additional information you need—like what pests citrus trees attract—and where your understanding falls short. It guides your research efforts effectively.

The questions you should ask yourself include:

1. What are the likely outcomes?

2. Which outcome do I believe will occur?

3. What knowledge do I have that supports this belief?

4. If unsure of these outcomes or lacking information, whom can I consult for advice?

These questions help ensure your decisions are based on deeper insight rather than superficial knowledge or assumptions.

Building Feedback Loops: Continuous Improvement

Building effective feedback loops is crucial for continuous improvement and informed decision-making. This concept revolves around designing a system that provides you with information and data to see if you are moving closer to your goals.

"This is about designing a system so that you have information and data."

Importance of Feedback Loops in Systems

Feedback loops are essential because they allow you to test your confidence in your decisions. Understanding how confident you are not only aids in decision-making but also provides insight when reflecting on past decisions. For instance, if you were confident about a decision that turned out to be incorrect, examining the missing pieces can be insightful.

Steps to Set Up a Feedback Loop

1. Define Your Goal: Clearly knowing what you want to achieve sets the stage for the entire feedback process.

2. Articulate Assumptions: Identify assumptions about how you plan to reach your goal, as these will guide your measurements.

3. Choose Measurements: Select metrics that align with your assumptions and goals.

4. Track and Assess: Use tools like decision journals to track decisions and assess them over time, typically after 3-6 months.

5. Reflect and Learn: Reflect on what worked or didn’t work, and modify your approach for future decisions.

"The feedback loop can look something like this..."

Application in Personal and Professional Contexts

In personal contexts, setting up feedback loops might involve tracking personal habits or learning methods, such as using mental models or frameworks to improve decision-making. In professional settings, it might involve regular reviews of business strategies or project outcomes.

A practical example involves tracking how many mental models you've learned and applied over time. This helps assess which models are effective in various scenarios, enhancing both personal growth and professional effectiveness.