Problems generally fall into two broad categories: complicated and complex. While they may sound similar, their nature, dimensions, and approaches to resolution are vastly different.

Let’s dive into these two types of problems and explore the key dimensions that define them.

1. Complicated Problems

Complicated problems are often technical in nature and can be resolved using established procedures and expertise. These problems are characterized by:

  • Predictability of Outcome: The result of a complicated problem is predictable. For instance, if you follow the correct steps to repair a car engine, it will work as expected.
  • Cause-and-Effect Relationship: There is a clear, linear relationship between causes and their effects. Each step builds logically upon the previous one.
  • Chance of Finding an Expert: You can almost always find an expert for a complicated problem, whether it’s a mechanic, engineer, or coder.
  • Can Be Solved with Expert(s): A knowledgeable individual or a small team with the right tools can typically resolve a complicated issue efficiently.
  • Level of Uncertainty: These problems involve minimal uncertainty because the solution path is well-documented.

Examples:

  • Fixing a broken appliance.
  • Creating a blueprint for a building.
  • Writing software code for a defined task.

2. Complex Problems

In contrast, complex problems are dynamic and messy, involving multiple interconnected factors. They require a different approach because:

  • Predictability of Outcome: The outcome of a complex problem is unpredictable. For example, launching a new product in an evolving market involves unknown variables.
  • Cause-and-Effect Relationship: The relationship between causes and effects is non-linear and often difficult to discern. Small changes can lead to significant, unintended consequences.
  • Chance of Finding an Expert: No single expert exists for a complex problem. Solving it requires diverse perspectives and collaboration.
  • Can Be Solved with Expert(s): No individual or small group of experts can fully solve a complex problem. It demands input from multiple stakeholders and often unfolds iteratively.
  • Level of Uncertainty: Uncertainty is inherent and cannot be eliminated entirely. Solutions often need adaptation over time.

Examples:

  • Leading organizational transformation.
  • Addressing climate change.
  • Navigating geopolitical tensions.

Complicated problems require expertise and linear thinking, while complex problems demand systems thinking and collaborative approaches.

When tackling your next task, take a moment to assess: What type of problem are you dealing with?