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additive_bias
Additive bias is the systematic tendency to solve problems by adding new elements, features, or rules rather than removing existing ones. When seeking to improve a situation, people default to adding components even when subtraction would be more effective, simpler, and less costly. This bias leads to accumulating complexity over time.
When asked to improve a top-heavy Lego structure, participants overwhelmingly add support bricks rather than removing bricks from the top, even when removal is the simpler and more elegant solution.
A product team trying to simplify an app that users find confusing responds by adding a tutorial, a tooltip system, a help chatbot, and a guided onboarding flow — never considering that removing half the existing features would have solved the confusion more effectively.
A city council trying to ease downtown traffic congestion approves new turning lanes, additional traffic signals, and a park-and-ride scheme, while consistently voting down proposals to simply remove the one-way street system that traffic engineers identified as the root cause.
Binary (yes/no) questions an LLM must answer to identify this aspect:
Does the proposed solution add complexity rather than simplify?
Type: binaryWas removing elements considered as a potential solution?
Type: binaryWould the problem be better solved by taking something away rather than adding something?
Type: binaryAdditive bias is the systematic tendency to solve problems by adding new elements, features, or rules rather than removing existing ones. When seeking to improve a situation, people default to adding components even when subtraction would be more effective, simpler, and less costly. This bias leads to accumulating complexity over time.
Additive solutions are more cognitively accessible because they are easier to imagine and describe. Subtractive solutions require considering what is already present and evaluating what to remove, which demands more cognitive effort. Adding also feels productive while removing feels like losing.
Explicitly consider subtractive solutions for every problem: ask 'What can I remove, simplify, or eliminate?' before asking 'What can I add?' Make subtraction a required step in your problem-solving process.
Additive bias contributes to bureaucratic bloat (adding new regulations without removing old ones), feature creep in software products, and the accumulation of unnecessary meetings and processes in organizations.
Use these tools to detect, analyze, or train this aspect.