Have you ever wondered why we don’t naturally find the “best solution” immediately — even when the goal seems clear?
The way our minds approach problems often mirrors how search algorithms work. And it offers a fascinating lesson in why we tend to settle for a “good enough” path instead of the optimal one.
Blind Steps: How We Naturally Explore
In the early stages of problem-solving, our thinking often resembles uninformed search algorithms — simple strategies that act without insight into the bigger picture.
- Depth-First Search (DFS) Mirrors our instinct to pick a path and follow it all the way. It’s the “just go for it” approach: fast, but risky. We might hit dead ends or overlook better alternatives.
- Breadth-First Search (BFS) Reflects a more cautious approach — examining all nearby options before moving deeper. Reliable, but slow and resource-intensive.
Both DFS and BFS are blind; they focus on local progress without considering global optimality.
Our natural reasoning often works sequentially, step by step, rather than strategically toward the best solution.
Adding Cost and Foresight: The Cognitive Leap
The evolution of search algorithms mirrors a breakthrough in structured thinking.
- Uniform-Cost Search (UCS) Introduces cost-awareness. It prioritizes paths based on the effort already spent, always expanding the “cheapest” option first. While this guarantees the lowest-cost solution, UCS is still myopic — it only looks at the past, with no sense of the distance to the goal.
- A* (A-star) Combines experience (cost so far) and foresight (estimated cost to the goal). By weighing both, A* consistently chooses the most promising path first. This demonstrates heuristic thinking: using structured rules to guide exploration efficiently.
Why Humans Didn’t Discover A Immediately*
Finding the optimal path first isn’t intuitive. Our brains naturally tend to:
- Focus on local steps, not the entire problem space.
- Struggle with systematic estimation of future outcomes.
- Find it hard to balance immediate effort with long-term optimality.
Like early algorithms, we often settle for “good enough” paths. Identifying the truly best solution requires a structured approach — combining memory, prediction, and evaluation.
The Takeaway for Life and Work
This isn’t just about algorithms. It’s a lesson for decision-making in life, work, or any complex problem:
- We naturally explore sequentially and locally.
- To consistently make optimal decisions, we need to think both locally and globally.
- By formalizing what “best” means and combining past experience with future estimates, we can move beyond intuition toward truly informed choices.
The best path is rarely obvious — but structured thinking can guide us there.