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How Humans Solve Complex Problems: The Case of the Knapsack Problem.

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Human decision-making struggles with complex problems like the knapsack problem. Performance decreases with complexity, yet effort often exceeds marginal gains, showing unique cognitive strategies.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Behavioral Economics

Background:

  • Human decision-making theories often overlook problem complexity.
  • The knapsack problem, a discrete optimization task, is relevant across cognitive levels.
  • Computer science quantifies knapsack problem complexity, but its impact on human cognition is less understood.

Purpose of the Study:

  • To experimentally investigate how problem complexity affects human performance in the knapsack problem.
  • To compare human problem-solving strategies with computer algorithms.
  • To explore the influence of biological constraints on cognitive decision-making.

Main Methods:

  • Experimental study using instances of the knapsack problem.
  • Analysis of human performance metrics (effort, economic outcome) in relation to instance complexity.
  • Observation of human problem-solving approaches and comparison with computational algorithms.

Main Results:

  • Human performance significantly decreased as knapsack problem complexity increased.
  • Participants exerted effort beyond the point of positive marginal gain, and economic performance improved with difficulty.
  • Human strategies showed similarities to computational algorithms, but were impacted by memory limitations.
  • A minority found solutions quickly, often without realizing it, highlighting significant heterogeneity.

Conclusions:

  • Problem complexity, as defined in computer science, is a critical factor in human decision-making.
  • Human economic behavior in complex tasks deviates from traditional economic principles.
  • Biological constraints like memory significantly shape cognitive strategies for optimization problems.
  • Heterogeneity in solutions suggests that market-based mechanisms may be more effective for incentivizing discovery than patents or prizes.