Computational Complexity and Human Decision-Making.

Area of Science:

• Cognitive Science
• Decision Theory
• Computational Neuroscience

Background:

• The rationality principle assumes optimal choices but overlooks decision-making difficulty.
• Existing decision models lack a framework for quantifying the complexity of finding the best option.

Purpose of the Study:

• To propose computational complexity theory (CCT) as a framework for defining and quantifying decision difficulty.
• To review evidence on how computational complexity affects human decision-making.
• To argue for the intractability of current decision-making and metacognition models.

Main Methods:

• Review of existing evidence on human decision-making and computational complexity.
• Theoretical proposal of CCT as a framework for decision difficulty.

Main Results:

• Human decision-making is demonstrably affected by computational complexity.
• Current models of decision-making and metacognition are computationally intractable.
• Biological resource constraints must be considered in future decision models.

Conclusions:

• Computational complexity theory provides a valuable lens for understanding decision-making limitations.
• Future theories must integrate computational resource requirements and biological constraints for plausibility.
• Rethinking decision-making models is necessary to account for inherent computational challenges.