(or the Attraction for Complexity) There is a very common tendency in computer science and it is to complicate solutions. This complication is often referred as incidental/accidental complexity i.e. anything we coders/designers do to make more complex a simple matter. Some times this is called over engineering and stems from the best intentions :

1. Attraction to Complexity: there’s often a misconception that more complex solutions are inherently better or more sophisticated. This can lead to choosing complicated approaches over simpler, more effective ones.
2. Technological Enthusiasm: developers might be eager to try out new technologies, patterns, or architectures. While innovation is important, using new tech for its own sake can lead to unnecessary complexity.
3. Anticipating Future Needs: developers may try to build solutions that are overly flexible to accommodate potential future requirements. This often leads to complex designs that are not needed for the current scope of the project.
4. Lack of Experience or Misjudgment: less experienced developers might not yet have the insight to choose the simplest effective solution, while even seasoned developers can sometimes overestimate what’s necessary for a project.
5. Avoiding Refactoring: In an attempt to avoid refactoring in the future, developers might add layers of abstraction or additional features they think might be needed later, resulting in over-engineered solutions.
6. Miscommunication or Lack of Clear Requirements: without clear requirements or effective communication within a team, developers might make assumptions about what’s needed, leading to solutions that are more complex than necessary.
7. Premature Optimization: trying to optimize every aspect of a solution from the beginning can lead to complexity. The adage “premature optimization is the root of all evil” highlights the pitfalls of optimizing before it’s clear that performance is an issue.
8. Unclear Problem Definition: not fully understanding the problem that needs to be solved can result in solutions that are more complicated than needed. A clear problem definition is essential for a simple and effective solution.
9. Personal Preference or Style: sometimes, the preference for certain coding styles, architectures, or patterns can lead to more complex solutions, even if simpler alternatives would suffice.
10. Fear of Under-Engineering: there can be a fear of delivering a solution that appears under-engineered or too simplistic, leading to adding unnecessary features or layers of abstraction.