Why Complicated Solutions Fail in a Complex World

Written by
Miles Rote

Why Complicated Solutions Fail in a Complex World

Written by
Miles Rote

Why Complicated Solutions Fail in a Complex World

Written by
Miles Rote

As we become more and more technological as a society, there is a perpetuating belief that systemic problems have algorithmic solutions.

This perspective stems from the impressive technical innovations that have characterized the digital age. With the right algorithms, the correct data, and the right intellectual capital, we feel as though we have the power to tackle any problem.

This approach can provide solutions for some problems. However, it isn't universally suitable. Specifically, it works for complicated problems, but stumbles when confronted with complex ones.

Understanding this key distinction is crucial in navigating today's increasingly interconnected and intricate world.

Defining the Terms: Complicated vs. Complex Systems

Let's start by clearly defining the difference between complicated and complex systems. A complicated system, such as a car engine, a meticulously assembled Swiss watch, or a computer, is characterized by numerous moving parts that interconnect in predictable ways. You can theoretically gain complete understanding of such a system through learning rules, applying decision procedures, and designing algorithms. This deep understanding is possible because complicated systems are fundamentally predictable. They do not change or adapt on their own, which often makes them brittle but manageable.

On the other hand, complex systems, like ecosystems, psychological development and learning, or human-computer interactions, are continuously evolving. Any interaction or change in the system leads to cascading effects that amplify possible outcomes, making many forms of prediction impossible. Unlike their complicated counterparts, complex systems are not fully knowable. Their behavior can't be entirely predicted, and they evolve and adapt independently. Studying them changes them, illustrating the principle that observation affects the observed in complex systems.

So why is this distinction important in problem-solving? The answer lies in the different approaches required for navigating complicated and complex systems. When we face a problem with a complicated system, we can observe it, analyze its full state of behaviors, and respond accordingly. This is possible because we know there is a well-defined answer in advance. If we understand the system’s governing rules well enough, we can find clear pathways to a solution.

However, complex systems require a different, more nuanced approach. You can’t know all of the system’s variables and dynamics, or even define success in a fixed manner. As sensemaking theorist Dave Snowden points out, complex systems require us to “probe, sense, and respond” rather than to “sense, analyze, and respond,” which is the method we apply to complicated systems.

In other words, to navigate complex systems, we must experiment within the system based on the best hypotheses available at the time, observe how it changes, and then adapt. Adaptation here doesn't merely mean becoming more efficient at “solving the problem.” It also involves evolving our understanding of what the problem is.

The Inner Workings of Complicated Systems: Examples and Insights

One example of a complicated system is climate. Which is why climate change is a complex problem of global scale.

Attempting to solve it with a complicated approach, like solely reducing carbon emissions, overlooks the system's complexity. This approach fails to account for the intricate network of ecological, economic, and social factors that contribute to and are affected by climate change.

Instead, it requires us to probe with various strategies, like promoting renewable energy, implementing carbon pricing, and encouraging sustainable agriculture, sense the impact of these strategies, and respond accordingly.

How to best approach modern technology is another complex problem. There is no one 'right' way to solve problems around artificial intelligence, misinformation, or gene editing.

The modern economy is also a complex problem. Far too complex for anyone to understand, complicated solutions won't account for solving our complex economic problems if we aren't consistently evolving what we're doing.

Many of today's crises stem from attempts to control complex systems with complicated solutions. Whether it's climate change, modern technology, our economy, social inequality, or public health, we often approach these complex problems with linear, reductionist strategies.

But these problems are not merely puzzles to be solved by finding the right pieces and fitting them together. They're evolving organisms that require continuous interaction, learning, and adaptation.

Embracing Unpredictability: Adapting to the Complex World

Instead of seeking to control and predict, we should aim to engage, learn, and adapt.

We should strive to understand the inherent complexity of the problems we face and appreciate the fact that our actions will influence these systems, just as they will influence us. This appreciation calls for humility, a recognition of our limitations, and a willingness to adapt our strategies as we learn more about these systems.

To do so, we must be open to iterative problem-solving processes. We can’t expect immediate, clear-cut solutions when dealing with complexity. Rather, we should anticipate a journey of discovery, one that necessitates continuous experimentation, evaluation, and adjustment of our strategies.

Moreover, collaboration and diversity of thought are essential. Complex systems are multi-dimensional, and our problem-solving approaches need to reflect that. We need to incorporate diverse perspectives and knowledge areas to develop a more holistic understanding of the systems we're engaging with. This could mean interdisciplinary teams working together to address public health issues or cross-sector collaborations to tackle environmental challenges.

Lastly, we must acknowledge that our goal in engaging with complex systems isn't to attain a state of stability or finality. The very nature of complex systems means they're continually evolving. Our goal, therefore, is not to arrive at a 'solution' in the traditional sense, but rather to foster resilience, facilitate positive adaptation, and continually navigate the dynamic landscape these systems present.

Understanding the difference between complicated and complex systems is more than an academic exercise. It's a fundamental shift in how we approach problem-solving in a world of increasing interconnectedness and complexity. As we grapple with the significant challenges of our time, we must move beyond seeking complicated solutions for complex problems. Instead, we need to embrace the complexity, engage with it adaptively, and collaborate across disciplines to foster resilience and positive change in the systems that shape our world.