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the claim
Humans are exceptionally good at solving certain NP-hard or NP-complete problems
the verdict
CONTESTED
the evidence cuts both ways
confidence 40/100

While humans can use heuristics and collective problem-solving to tackle certain complex combinatorial tasks efficiently, experimental studies show that their performance drops significantly as problem difficulty increases, indicating they do not inherently solve NP-hard problems exceptionally well compared to optimized algorithms.

The evidence we hold leans evenly split

How this was weighed

official record 3x · fact-check 2x · hedged 1x · crowd & reference 1x

  • A glass-box interactive machine learning approach for solvin · peer-reviewed · supports · weight 1.6 · 2017
  • Playing the System: Can Puzzle Players Teach us How to Solve · peer-reviewed · supports · weight 1.05 · 2023
  • Is Hardness Inherent in Computational Problems? Performance · peer-reviewed · refutes · weight 1.05 · 2020
  • Human Navigation in a Multilevel Travelling Salesperson Prob · peer-reviewed · refutes · weight 1.05 · 2022
Evidence for · 2
A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop
2017 · cited by 98
Human intuition and heuristic selection can successfully reduce the search space and complexity of NP-hard problems when integrated into human-in-the-loop machine learning.
Evidence against · 2
Is Hardness Inherent in Computational Problems? Performance of Human and Electronic Computers on Random Instances of the 0-1 Knapsack Problem
2020 · cited by 5
While humans recognize instances of NP-complete problems that are difficult for computers, increased cognitive effort does not allow them to overcome computational hardness.
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More for · 1
Playing the System: Can Puzzle Players Teach us How to Solve Hard Problems?
2023 · cited by 5
Collective problem-solving and puzzle-playing by millions of humans can generate solutions to complex NP-hard biological sequence alignment tasks that rival standard algorithmic approaches.
More against · 1
Human Navigation in a Multilevel Travelling Salesperson Problem
2022 · cited by 4
Human performance in spatial optimization tasks like the traveling salesperson problem falls short of optimal combinatorial algorithms, especially as complexity and dimensionality increase.
The paper trail · every fact has a biography
first checked01 Aug 2026
judged → CONTESTED · 4001 Aug 2026
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